Financial Regulation and the Current Account

Sergi Lanau, Tomasz Wieladek

WP/12/98

© 2012 International Monetary Fund WP/12/98

IMF Working Paper

Strategy, Policy, and Review Department

Financial Regulation and the Current Account

Prepared by Sergi Lanau, Tomasz Wieladek 1

Authorized for distribution by James Roaf

April 2012

Abstract

This paper examines the relationship between financial regulation and the current account in an intertemporal model of the current account where financial regulation affects the current account through liquidity constraints. Greater liquidity constraints decrease the size and persistence of the current account response to a net output shock. The theory is tested with an interacted panel VAR model where the coefficients are allowed to vary with the degree of financial regulation. The current account reaction to an output shock is 60% larger and substantially more persistent in a country with low financial regulation than in one with high financial regulation.

JEL Classification Numbers:F32, F41, G28

Keywords: Current account, financial regulation, panel VAR

Author’s E-Mail Address:slanau@imf.org and twieladek@london.edu

1 Strategy, Policy, and Review Department, International Monetary Fund, Washington D.C., and London Business School, London, U.K.

This Working Paper should not be reported as representing the views of the IMF. The views expressed in this Working Paper are those of the author(s) and do not necessarily represent those of the IMF or IMF policy. Working Papers describe research in progress by the author(s) and are published to elicit comments and to further debate.

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Contents Page

I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

II. Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7A. How does financial regulation affect the response of the current account to

output shocks? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

III. Empirical methodology and data . . . . . . . . . . . . . . . . . . . . . . . . . . . 15A. General methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15B. Identification of net output shocks . . . . . . . . . . . . . . . . . . . . . . . . 17C. Estimation and inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20D. Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

IV. Empirical results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22A. Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

V. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Appendix

A. Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32A.1. Deriving the linearized budget constraint . . . . . . . . . . . . . . . . . . . . 32A.2. Derivation of the current account reaction function with external habits and a

constant world real interest rate . . . . . . . . . . . . . . . . . . . . . . . . . 34A.3. Derivation of the current account reaction function with internal habits and a

constant world real interest rate . . . . . . . . . . . . . . . . . . . . . . . . . 38A.4. Derivation of the current account reaction function under a stochastic time-

varying world real interest rate and no habitual consumption . . . . . . . . . . 39

Tables

1. Sign restrictions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182. Country sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Figures

1. Size of current account imbalances . . . . . . . . . . . . . . . . . . . . . . . . . . 412. Financial deregulation index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 413. Current account persistence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 424. Impulse response functions to log level net output shock - financial regulation . . . 435. Impulse response functions to log difference net output shock - financial regulation 446. Impulse response functions to log level net output shock - capital account openness 45

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7. Impulse response functions to log difference net output shock - capital accountopenness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

8. Impulse response functions to log level output shock - financial regulation con-troling for fx regime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

9. Impulse response functions to log difference output shock - financial regulationcontroling for fx regime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

10. Impulse response functions to log level output shock - capital account opennesscontroling for fx regime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

11. Impulse response functions to log difference output shock - capital account open-ness controling for fx regime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

12. Histogram of the square root of λ . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

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I. INTRODUCTION

The run-up to the global financial crisis of 2008 was characterized by large and persistentcurrent account imbalances. One school of thought argues that ‘the origins of the crisis liein the imbalances in the world economy that built up over a decade or more’ (King, 2009).There is both empirical and theoretical evidence to support this proposition. Theoretically,Caballero and Krishnamurthy (2009) develop a model to show how capital flows to the UStriggered a sharp rise in asset prices and a decrease in risk premia and interest rates. Em-pirically, Warnock and Warnock (2009) estimate that, if there had been no foreign officialflows into US government bonds over the course of a year, long rates would be almost 100basis points higher. Similarly, Sa, Towbin, and Wieladek (2011) use a structural VAR modelto show that, in OECD countries, capital inflows shocks have a greater effect on real houseprices and residential investment than monetary policy shocks.

Others contend that current account imbalances just reflect domestic credit booms and assetprice bubbles. Laibson and Mollerstrom (2010) present a model in which a current accountdeficit is the result of a domestic asset price bubble and credit boom, features that the authorsargue are consistent with the data. Similarly, Gete (2010) shows that increases in housing de-mand can lead to a trade deficit via consumption smoothing across tradable and non-tradablegoods. Empirically, Fratzscher, Juvenal, and Sarno (2007) find that US asset price movementscan explain up to 32 percent of the US trade balance, providing support for this view.

A third, still nascent, school of thought points to financial deregulation/innovation as an ex-planation for global current account imbalances. In particular, Ferrero (2011) considers ashock to the collateral constraint, which he argues can be interpreted as the macroeconomiceffect of financial innovation, in a two-country model. The realization of this shock in a cali-brated version of his model can, to some extent, replicate the US current account deficit andhouse price boom observed in the run up to the crisis.1 In a similar vein, Borio and Disyatat(2011) argue that the problem is the excess ‘elasticity’ of the international monetary and fi-nancial system. They define this elasticity2 as ‘the degree to which extent monetary and finan-cial regimes constrain the credit creation process, and external funding more generally’. They

1Similarly, Favilukis and Nieuwerburgh (2009) consider a 2-sector general equilibrium model of housing andnon-housing, where households have limited opportunity to insure against shocks. They show that financialliberalization, together with a rise in foreign ownership of domestic debt, can replicate the house price dynamicsobserved in the US during the boom period. But in their model, foreign ownership of domestic debt is taken asexogenous, therefore explicitly ruling out the link between household behavior and the current account we wantto model.2The authors trace the term back to as early as Jevons (1875), who defined ’elasticity’ to be the degree with

which money grows to accommodate the demand in the economy.

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hypothesize that an increase in the elasticity of the financial system over time, as a result offinancial deregulation for instance, led to greater domestic and external imbalances.3 Indeed,the data suggests that the increase in the absolute size of current account imbalances (figure1) and their persistence (figure 2) emerged against the backdrop of financial deregulation inboth OECD and emerging market countries (figure 3)4, providing informal support for the‘excess elasticity’ hypothesis. But, to our knowledge, no previous work has rigorously testedif and to which extent, the reaction of the current account depends on the domestic financialregulatory regime. In this paper, we take a step towards filling this gap. In particular, we pro-vide the first empirical evidence that, for a given set of net output shocks, the current accountresponse is larger and more persistent in a country with a low, than in one with a high, degreeof financial regulation.

In the first part of the paper, we derive robust identification restrictions and theoretical predic-tions from the intertemporal model of the current account (Sachs, 1981), which we augmentwith financial regulation. Following previous theoretical and empirical work on long-run eco-nomic growth, savings, and consumption, we model financial regulation as a liquidity con-straint. The tighter financial regulation is, the fewer agents in the economy have access to thesaving and borrowing technology. Recent work argues that the standard model needs be ex-tended by either external habit formation (Bussiere, Fratzscher, and Müller, 2004), internalhabit formation (Gruber, 2004) or a stochastic world real interest rate (Bergin and Sheffrin,2000) to match the persistence of the current account typically observed in the data. We de-rive analytical solutions of our model under each of these assumptions (always under liquid-ity constraints) to show that our predictions are robust to these modeling choices. We thentrace the effect of a shock to the log level (difference) of net output on the current accountfor different values of the liquidity constraint. This permits us to analytically show that, re-gardless of modeling choices and parametrization, a given net output shock will have a largereffect on the current account balance if agents are less liquidity constrained. In the case ofexternal habit formation in consumption, the current account response also becomes morepersistent as the liquidity constraint eases. In other words, the lighter financial regulation, thegreater and more persistent the reaction of the current account to net output shocks. The intu-ition is simple: the current account reflects agents’ savings decisions in response to net outputshocks. In a repressed (liberalized) financial system, few (many) agents have access to bor-

3Merrouche and Nier (2010) analyze quantitatively the role of the three factors discussed so far (capital flows,loose monetary policy, and financial regulation), and conclude that the effect of capital inflows on the build-upof current account imbalances is amplified where financial regulation is weak.4We define persistence as the AR(1) coefficient from an autoregressive panel data model of the current account

to GDP ratio.

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rowing and saving and the current account will therefore show a smaller (greater) reaction todomestic shocks.

In the second part of the paper, we take our theory to the data. The theoretical model suggeststhat real per capita net output growth and the fiscal balance to net output ratio, together withtheir lags, determine the current account to net output ratio. As these variables are likely to behighly endogenous, the VAR methodology is the most suitable approach to verify the theo-retical predictions. The theory also predicts that the effect of the shock should depend on thedegree of liquidity constraints/financial regulation. We therefore allow the structural VARcoefficients to vary with the degree of financial regulation and capital account openness, anindependent determinant of liquidity constraints (Lewis, 1997). This interacted panel VARmodel is estimated with time and fixed effects on an unbalanced panel of annual data across84 countries over the period 1973-2005, using the de jure financial regulation indices pro-vided in Abiad, Detragiache, and Tressel (2010). Our theory also provides sign and shapeidentification restrictions that are robust to parametrization and specific modeling choices.In particular, a log level (difference) net output shock will lead to an increase in the level ofnet output and a current account surplus (deficit). Furthermore, a non-explosive net outputprocess in log levels (differences) has the property that the response to a one-time unexpectedshock does not increase (decrease) over time. Similarly, in absolute value terms, the currentaccount response to either net output shock should not increase over time. These properties ofthe model naturally translate into sign and shape restrictions of the type used in Canova andNicolo (2002), Faust and Rogers (2003), and Uhlig (2005). This allows us to identify both loglevel and log difference net output shocks under regimes of low and high financial regulation.We then compare the impulse responses under the two regimes to assess if the effect of theseshocks on the current account balance to net output ratio varies with financial regulation. Ourresults confirm that the impact of both shocks on the current account differs across low- andhigh-regulation regimes in a statistically-significant way. The response of the current accountto either type of net output shock is approximately 60 percent larger, as well as more persis-tent, in the average financially-liberalized country than in the average financially-repressedcountry. This implies that a future move toward tighter financial regulation around the globecould have a sizeable impact on current account imbalances. To our knowledge, this dimen-sion of the financial reform debate is not usually stressed but is potentially important for thefunctioning of the international monetary system.

This paper contributes to the existing literature in a number of ways. We provide the first em-pirical investigation of the interaction between financial regulation and the current account,expanding upon existing empirical work that examines the relationship between financial reg-

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ulation and savings decisions in the closed economy (Bandiera and others, 2000; Bayoumi,1993a,b; Bayoumi and Koujianou, 1989; Jappelli and Pagano, 1994). Second, we work with abroad sample of countries for the standards of previous papers studying the effect of financialregulation on savings decisions, being the first to apply the sign restriction methodology totest the predictions of this type of model. Finally, a secondary contribution is our detailed ex-ploration of the role of liquidity constraints in different standard versions of the intertemporalmodel of the current account.

The rest of this paper proceeds as follows. Section 2 lays out the theoretical model and de-rives theoretically robust identification restrictions and predictions. Section 3 discusses theempirical specification and the data used. Section 4 presents the empirical results and robust-ness checks. Section 5 concludes.

II. THEORY

Our aim is to embed financial regulation in a model of the current account that is simpleenough to take to the data in a neat way. We build on the Sachs (1981) intertemporal modelof the current account (ICA). Conceptually, financial regulation can affect macroeconomicvariables in at least two ways. First, regulation can affect the efficiency of transforming sav-ings into productive investment (Goldsmith, 1969) by promoting or hindering competitionin the banking system. Second, the volume of savings flowing into investment can either in-crease (McKinnon, 1973; Shaw, 1973) or decrease (Devereux and Smith, 1994; Jappelli andPagano, 1994) following deregulation. The former is likely to affect the price, while the lat-ter the quantity, of capital available for investment. Since current account balances reflectquantities rather than prices we focus on the latter channel. While theoretically ambiguous,empirical evidence supports the proposition that tighter financial regulation decreases savingsthrough the liquidity constraints channel. Diaz-Alejandro (1985) points out that in developingcountries, the volume of savings tends to fall following financial liberalization (deregulation).Bayoumi (1993a), Bayoumi (1993b), Sarno and Taylor (1998) find that financial deregula-tion in the UK in the 1980s decreased liquidity constraints, leading to a decline in aggregatesavings. Bayoumi and Koujianou (1989) and Jappelli and Pagano (1994) confirm this patternacross a range of industrial countries. Bandiera and others (2000) also find empirical supportfor the idea that financial regulation decreases savings via the liquidity constraints channelin eight emerging market economies. Finally, using data for 72 countries, Lewis (1997) findsthat consumers in countries with government restrictions on international transactions tend

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to act as if they are liquidity constrained. This breadth of evidence justifies our approach ofintroducing financial regulation via liquidity constraints in the ICA model.

Bergin and Sheffrin (2000) and Ghosh (1995) among others, do not find empirical supportfor the simple intertemporal current account model in most G7 countries. The literature hasproposed three different modifications of the basic model to improve its empirical perfor-mance. Gruber (2004) introduced internal habit formation (where utility is a function of pastindividual consumption), Bussiere, Fratzscher, and Müller (2004) used external habit forma-tion (where utility is a function of past average consumption), and Nason and Rogers (2006)argued that a time-varying stochastic world interest rate delivers a more realistic model. Re-cently, Kano (2009) has shown that the internal habits and time-varying world real interestrate approaches provide observationally equivalent predictions. Together with one of thesethree frictions, we add liquidity constraints to the standard ICA model in order to capture theeffects of financial regulation. This allows us to show that our identification restrictions arerobust across these modeling choices and reasonable parametrization.

We consider a small open, endowment economy populated by a large number of householdswho maximize utility subject to a budget constraint. Output, investment, government expendi-ture, and lump-sum taxes are exogenous. 5 There are two types of households in the economy.One type of household is liquidity constrained in the sense that they do not have access to anysavings technology. We will refer to them as non-Ricardian households. These householdsconsume their exogenous income each period:

CNRt = Yt− It−Tt

where Yt is output, It is investment and Tt are taxes. The second type of household has accessto incomplete international financial markets in which only non-contingent riskless bonds aretraded. These households are characterized by their optimal behavior with respect to the in-tertemporal allocation of consumption. We will refer to this group of households as Ricardianthroughout. We assume that non-Ricardian households make up a fraction of λ ∈ [0,1] of thepopulation with Ricardian households as the remainder. Hence, aggregate consumption is de-fined as Ct = λCNR

t +(1−λ )CRt .

5The exogeneity assumption is a convenient simplification that is unlikely to hold empirically. However, sinceour main theoretical predictions follow directly from the Euler equation, they would most likely also hold inmore complex DSGE models where all macroeconomic variables are endogenous. Unlike in those models how-ever, the fact that we can obtain an analytical solution and we do not need to resort to numerical methods, allowsus to easily and transparently demonstrate that our predictions are robust to reasonable parameter values.

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The representative Ricardian household solves the following maximization problem:

Ut = E0

∞

∑t=0

βtU(CR

t −hCt−1) (1)

s.t.BPt+1 = (1+ rt)BP

t +Yt− It−Tt−CRt (2)

Limi→∞

Rt,iEtBi+1 ≥ 0 (3)

Ri,t =

1/(t+i∏

j=t+1(1+ r j)) if i≥ 1

1 if i = 0(4)

where β ∈ {0,1} is the discount factor and CRt consumption at time t, and Ct−1 is past con-

sumption. BPt+1 is the net stock of international bonds held by the Ricardian household at the

end of time t, which are reimbursed at the world real interest rate rt . Households choose CRt

and BPt+1 to maximize discounted lifetime consumption subject to the budget constraint and

a no-ponzi condition. We assume 0 ≤ h < 1, which implies that Ricardian households maybe habit forming with respect to consumption. We explore both the possibility of externaland internal habit formation in consumption. As a result, utility is increasing in consumptionexpenditure that exceeds the depreciated value of last period’s average consumption in theeconomy (in the case of external habits) or the depreciated value of the household’s laggedconsumption (in the case of internal habits), with h being the rate of depreciation. The utilityfunction takes a log form throughout. The first-order necessary conditions of this optimizationproblem comprise the budget constraint (2), the transversality condition (3) and the followingEuler equation:

U ′(CRt −hCt−1) = βEt [(1+ rt+1)U ′(CR

t+1−hCt)] (5)

Iterating the budget constraint and imposing the no-ponzi condition yields the intertemporalbudget constraint of the Ricardian household:

Et

∞

∑i=0

Rt,iCRt+i = (1+ rt)BP

t +Et

∞

∑i=0

Rt,i(Yt+i− It+i−Tt+i) (6)

We also assume the presence of an exogenous government sector. The government budgetconstraint is:

BGt+1 = (1+ rt)BG

t +Tt−Gt

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where BGt+1 is the net stock of international bonds held by the government at the end of time t.

Gt is government spending. Iterating the government budget constraint and combining it with(6), one can show that

Et

∞

∑i=0

Rt,iCRt+i = (1+ rt)(BP

t +BGt )+Et

∞

∑i=0

Rt,iNOt+i (7)

where NOt =Yt− It−Gt is net output. Since only Ricardian households hold bonds, the stockof external assets, Bt , is

Bt = (1−λ )BPt +BG

t

In order to derive an analytical solution to the present value of the current account, we takelinear approximations of both the budget constraint (7) and the Euler equation (5). Followingthe approach in Kano (2008)), one can show after a fair amount of algebra (see appendix) that

CRt

NOt[1+

∞

∑i=1

Et exp

{t+i

∑j=t+1

(∆ lnCRj − ln(1+ r j))

}] (8)

= exp{ln(1+ rt)}BP

t +BGt

NOt+[1+

∞

∑i=1

Et exp

{t+i

∑j=t+1

(∆ lnNO j− ln(1+ r j))

}]

Let c,bG,bP,µ and γ denote the unconditional means of the consumption to net output ratio,the net foreign private asset to net output ratio, the net foreign public asset to net output ratio,the log of the gross world real interest rate and the growth rate of net output and consumption,respectively. Let us define Xt = Xt−X , where X is the steady state value of variable X . Takinga first-order Taylor expansion of (8) around the steady state yields:

CRt

NOt=

1−κ

e−µ(

BGt

NOt+

BPt

NOt)+

∞

∑i=1

κiEt

{∆ ˜lnNOt+i− ln ˜(1+ rt+i)

}(9)

−c∞

∑i=1

κiEt

{∆ ˜lnCRt+i− ln ˜(1+ rt+i)

}+

1−κ

e−µ(bG +bP) ln ˜(1+ rt)

where κ = exp(γ − µ). This is the linearized budget constraint. At this point further assump-tions have to be made to solve the model. The literature has proposed three different simpli-fications to solve the model. The first option is to assume that the world real interest rate isconstant and that there is external habit formation (i.e., the argument of the utility function is

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CRt − hCt−1 where C denotes average consumption in the economy). The second option is to

assume a constant world real interest rate and internal habit formation (i.e., the argument ofthe utility function is CR

t −hCRt−1). Finally, one can assume a stochastic world real interest rate

and no habit formation (h = 0 in our model). Kano (2009) shows that these last two frictionsimply observationally equivalent data generating processes. Since in practice it is difficult toknow which model is underlying the data generating process, we show that our identificationassumptions are robust to any of these specific modeling choices. The detailed derivation ofthe current account reaction functions is relegated to the appendix.

Under a constant world real interest rate and external habit formation the current account re-action function is

cat = (1−λ )hcat−1 +λ f bt− (1−hκ)(1−λ )∞

∑i=0

κiEt

{∆ ˜lnNOt+i

}(10)

+(1−λ )∆ ˜lnNOt−hκ(1−λ )2∞

∑i=0

κi(Et−Et−1)

{∆ ˜lnNOt+i

}+ ft

where ft is an expectational error, defined as ft = h(1−λ )2c∑∞i=1 κ i(Et−Et−1)

{∆ ˜lnCRt+i−1

},

cat =CAtNOt

and f bt = r BGt

NOt+ Tt

NOt− Gt

NOt. Let us develop some intuition underlying this equa-

tion. In the absence of liquidity constraints, the current account under external habit forma-tion is a function of the lagged current account and a weighted average of current and futurenet output changes.6 As habits become stronger, the importance of both the lagged current ac-count to net output ratio increases (the first term in (10)) and the weight of expected future netoutput growth (third term) increases. Liquidity constraints play three roles in equation (10).First, they create an interaction between the fiscal balance and the current account. This is be-cause the way government spending is financed directly affects the income and consumptionof non-Ricardian households. A fiscal deficit results in a current account deficit since it in-creases the income of these households.7 Second, as a larger fraction of households is liquid-ity constrained, the importance of net output shocks diminishes since fewer households areable to smooth such shocks by borrowing and lending internationally. Finally, the coefficienton the lagged current account term is a function of both habits and the fraction of liquidity-constrained households. This follows directly from the Euler equation, since households’ de-sired smoothing pattern depends on aggregate average consumption and hence implicitly on

6In this case the model becomes virtually identical to Gruber (2004).7This is consistent with empirical evidence that documents twin deficits in the data (Corsetti and Müller, 2008;

Kim and Roubini, 2008).

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the liquidity constraint. With external habits, therefore, the speed of current account adjust-ment also becomes a function of the liquidity constraint.

Under a constant world real interest rate and internal habitual consumption the current ac-count reaction function is

cat = hcat−1 +λ f bt−λh f bt−1 +(1−λ )∆ ˜lnNOt (11)

−(1−λ )(1−hκ)∞

∑i=0

κiEt

{∆ ˜lnNOt+i

}−(1−λ )(hκ)

∞

∑i=0

κi(Et−Et−1)

{∆ ˜lnNOt+i

}+ ft

where ft is an expectational error, defined as ft = (1−λ ) cκh1−κh(∆

˜lnCRt −Et−1∆ ˜lnCRt). Theintuition underlying (10) and (11) is very similar. Unlike in (10), the lagged current accountcoefficient is not affected by liquidity constraints in (11). This is because, in the case of inter-nal habitual consumption, only individual consumption is relevant and there is no scope forthe liquidity constraint to interact with the lagged current account term.

Without habit formation, but under the assumption of a stochastic time-varying world realinterest rate ( rt = ρr rt−1 +ηt), the current account reaction function is

cat = ρrcat +λ f bt−ρ

rλ f bt−1 +(1−λ )∆ ˜lnNOt (12)

−(1−λ )(1−ρrκ)

∞

∑i=0

κiEt

{∆ ˜lnNOt+i

}−(1−λ )(ρr

κ)∞

∑i=0

κi(Et−Et−1)

{∆ ˜lnNOt+i

}+ ft

where ft = [((1−λ )(eµ −1)bG− eµbG)+ (1−λ )ρrκ

1−ρrκ]ηt . Note that, as in Kano (2009), the cur-

rent account reaction function under internal habits (equation (11)) is observationally equiv-alent to the reaction function under a stochastic world real interest rate (equation (12) ). Forthe remainder of this paper, we will therefore only derive results for the internal habits model,keeping in mind that its prediction are observationally equivalent to the time-varying stochas-tic world real interest rate model.

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A. How does financial regulation affect theresponse of the current account to output shocks?

In this section we derive robust identification restrictions and examine how the current ac-count reacts to net output shocks at different levels of regulation (the liquidity constraint)in our theoretical model. In order to understand how the current account reacts to net out-put shocks at different levels of financial regulation, we need to make further assumptions onthe stochastic process driving ˜lnNOt . Net output can be subject to shocks in log differences,∆ lnNOt = ρ∆ lnNOt−1 + εt , or log levels, lnNOt = ρ lnNOt−1 + εt . A priori, it is not feasi-ble to know which process is driving the log of net output. We will therefore consider both ofthem and examine the effect of an unexpected shock to either net output process on the cur-rent account under the assumption of internal and external habits individually. At this point,most previous work would solve the model numerically and show that those theoretical im-pulse responses, from which the sign identification restrictions are derived, are robust to manydifferent possible parameter values.8 The advantage of our approach is that we can demon-strate the robustness of our identification restrictions and theoretical predictions analytically.

Let us consider the case of external habitual consumption first. Under the assumption that netoutput is subject to shocks in log differences, one can show that (see appendix) (10) becomes

cat = (1−λ )hcat−1 +λ f bt +(1−λ )κ(h−ρ)

1−ρκρ∆ ˜lnNOt−1 (13)

−κ(1−λ )(ρ(1−κ)+h(κ−λ )−ρκh(1−λ ))

(1−κ)(1−ρκ)εt + ft

It is then easy to see that:

∂ cat∂εt

=−(1−λ )κ ρ(1−κ)+h(κ−λ )−ρκh(1−λ )(1−κ)(1−ρκ) < 0 ∂ cat

∂εt∂λ= κ[ρ(1−κ)+h(κ−λ )+(h−2ρκh)(1−λ )]

(1−κ)(1−κρ) > 0

as long as λ ,ρ,h,κ ∈ [0,1) and κ ≥ λ , which will be satisfied under any plausible parametri-zation of this model.9 Given these parameter restrictions, one can clearly see that the impact

8See Enders, Müller, and Scholl (2011) for more details on this approach.9Since κ = exp(γ−µ) and γ−µ is unlikely to be large, Kano (2008) argues that κ should be fairly close to, but

smaller than, one. We therefore assume that κ ∈ [0.9,1) Since λ , the fraction of the population that is liquidity

14

response of a net output log difference shock upon impact is negative and that greater liquid-ity constraints make the impact response of this shock less negative, thus smaller. In otherwords, financial deregulation, i.e. the removal of liquidity constraints, makes the response ofthe current account to a net output log difference shock larger. Since the coefficient on cat−1

is a function of λ one can also clearly see that the effect of a past shock on the current ac-count today declines with a greater fraction of liquidity constrained households. This meansthat the persistence of the current account is also decreasing in the liquidity constraint.

Under the assumption of internal habits and considering a net output log difference shock, thecurrent account reaction function is

cat = hcat−1 +λ f bt−λh f bt−1 +(1−λ )κ(h−ρ)

1−ρκ∆ ˜lnNOt−1 (14)

−(1−λ )κρ(1−κ)+hκ(1−ρ)

(1−κ)(1−ρκ)εt + ft

With regard to the size prediction, it is then easy to see that the effects of financial regulationunder internal habits are qualitatively identical to those under external habits:

∂ cat∂εt

=−(1−λ )κ ρ(1−κ)+hκ(1−ρ)(1−κ)(1−ρκ) < 0 ∂ cat

∂εt∂λ= κ

ρ(1−κ)+hκ(1−ρ)(1−κ)(1−ρκ) > 0

But unlike in the case of external habits, liquidity constraints no longer affect current accountpersistence. Let us turn to the case of net output shocks in log levels. In this case, the modelwith external habits becomes

cat = (1−λ )hcat−1 +λ f bt−(1−λ )κ(h−ρ)

1−ρκ(1−ρ) ˜lnNOt−1 (15)

+(1−λ )((1−κ)+hκ(1−ρ)(1−λ ))

(1−κ)εt + ft

It is then easy to show that ∂ cat∂εt

> 0 and ∂ cat∂εt∂λ

< 0.

For the case of shocks in log-levels, the current account under internal habits is

constrained, is unlikely to exceed this lower bound in reality, the condition κ ≥ λ will always be satisfied for anyplausible parametrization of the model.

15

cat = hcat−1 +λ f bt−λh f bt−1− (1−ρ)(1−λ )κ(h−ρ)

1−ρκ

˜lnNOt−1 (16)

+(1−λ )(1−ρ)hκ +(1−κ)

(1−κ)εt + ft

and∂ cat∂εt

> 0 ∂ cat∂εt∂λ

< 0

This means that the current account goes into surplus following a log level net output shockand that the reaction of the current account is smaller as liquidity constraints increase. As inthe case of log difference shocks, with external habit formation, the persistence of the cur-rent account is declining in the liquidity constraints. In short, the model predicts that underlower liquidity constraints, the response of the current account becomes larger in any case,and more persistent in the presence of external habits, for a given log level/difference net out-put shock.

Finally note that our theory suggests that the data generating process for the current accountwill be a reduced form VAR model with cat , f bt , ˜∆ lnNOt as the regressors.

III. EMPIRICAL METHODOLOGY AND DATA

This section develops a methodology to test the main implications of our model, namely thatthe reaction of the current account to net output shocks is larger and more persistent underlighter financial regulation.

A. General methodology

In practice, all the variables required to test this model are likely to be endogenous with re-spect to each other. To address this concerns, we adopt a panel VAR approach where we ex-plicitly identify net output shocks while addressing endogeneity concerns.10 We use annualdata given that financial regulation indices are not available at quarterly frequency. The panel

10An alternative approach would of course be the instrumental variable approach, but for annual data Chinn andPrasad (2003) have shown that it is difficult to obtain reliable instruments for the variables driving the currentaccount.

16

VAR includes the three variables that drive the data-generating process for the current ac-count according to our model: the current account to net output ratio, the fiscal balance to netoutput ratio and the net output growth rate. We therefore estimate the following interactedpanel VAR model:

A0Yi,t = γt +L

∑k=1

AkYi,t−k +L

∑k=0

BkYi,t−kXi,t−k +CXi,t + ei,t ei,t ∼ N(0,Σ) (17)

where Yi,t is an n×1 vector consisting of the current account to net output ratio, the fiscal bal-ance to net output ratio and the net output growth rate in country i at time t. Since the theoryprovides predictions for variables expressed in deviations from their steady state value, weexpress these three variables in deviations from their country-specific means (which are ourproxies for their steady state values). This removes the country fixed effect for these vari-ables. γt is a time fixed effect.11 Ak is an autoregressive matrix up to lag L. ei,t is an n×1 vec-tor of residuals, assumed to be uncorrelated across countries and normally distributed with aconstant covariance matrix Σ. The model is estimated with 2 lags.12

Xi,t is a vector of the variables that we interact the panel VAR coefficients with. In other words,we allow the impact and lagged VAR coefficients to vary with the interaction term. Most ex-isting studies model time variation in the VAR coefficients as a random walk (Cogley andSargent, 2005). In contrast to these papers, we follow Towbin and Weber (2011) and Sa, Tow-bin, and Wieladek (2011) and allow the coefficients to vary with observable deterministicvariables.13 In our baseline specification, the q× 1 vector Xi,t contains Finregi,t , an index offinancial regulation, and KAOpeni,t , a measure of capital account openness. Previous work byLewis (1997) has documented that restrictions on international transactions may lead house-holds to act as if they are liquidity constrained. Similarly, Towbin (2008) has shown that thetrade balance, an important component of the current account, becomes more persistent withgreater capital account openness. To avoid the omitted variable bias, capital account opennessis therefore included as an independent determinant of the VAR coefficients. We use the de

jure versions of these indicators because they are unlikely to be affected by net output shocks

11Since we want to ensure that we identify net output shocks of domestic, rather than external, origin, we in-clude time fixed effects in the model to account for external shocks.12Ex-ante lag length selection criteria, such as the Akaike, Hanan-Quinn, and Schwartz-Bayesian criteria sug-gest a lag length of one. However, one the main assumptions of the VAR model is that residuals behave likewhite noise. Estimated with one lag, the residuals were autocorrelated of order 1, which is obviously inconsis-tent with white noise behavior and suggested that in at least one of the equations 2 lags would be necessary.Since the bias from omitting a lag is typically worse than that from including an extra lag, we estimate the modelwith 2 lags.13Our exposition of this model closely follows Sa, Towbin, and Wieladek (2011).

17

directly and can therefore be treated as exogenous with respect to the VAR system.14 In ad-dition, Xi,t enters separately to ensure that the change in the VAR coefficients does not occurdue to omission of the interaction variable in levels.

The matrix A0 is an n×n matrix with ones in the diagonal. Any element above the diagonal is0. As a result of the lower triangular form, the residuals, ei,t , will be orthogonal to each otherand the covariance matrix Σ will be diagonal. The lower triangular elements of this matrix areinteracted with Xi,t and take the following form: the coefficient in row j and column h is mod-eled as A0,i,t( j,h) = A0( j,h)Yi,t(h)+B1

0( j,h)Yi,t(h)Finregi,t +B11( j,h)Yi,t(h)KAOpeni,t , where

j < h . B10( j,h) and B1

1( j,h) are both scalar coefficients that denote the marginal change in fi-nancial regulation and capital account openness on A0( j,h), respectively. By setting Finregi,t

at a high (low) value, one can therefore obtain A0,i,t( j,h), which would correspond to the im-pact coefficient in a country with high (low) financial regulation. Estimating the VAR equa-tion by equation in this recursive form provides a simple way of allowing the reduced formcovariance matrix of the VAR, Σi,t = A−1

0,i,tΣA−1′0,i,t , to vary with financial regulation and cap-

ital account openness. All coefficients in the lagged dependent variable coefficient matrix,Ak,i,t( j,h), are also allowed to vary with the interaction terms, Xi,t , in an identical manner, butwithout any zero or one restrictions.

In summary, we are allowing the coefficients in both the impact and lagged dependent vari-able matrix to vary with observable deterministic variables. The advantage of this approach isthat we can assess to which extent the impulse responses to a given shock differ with the de-gree of financial regulation (the liquidity constraint). Since our theory predicts that the effectof a net output shock on the current account to net output ratio depends on the degree of theliquidity constraint, this method is better suited to test our theory than standard time-varyingcoefficient VARs, which typically do not provide information on the source of time variation.

B. Identification of net output shocks

Previous work by Kano (2008) used zero restrictions derived from a model similar to (11)to identify net output shocks in Structural VARs. It is however difficult to establish whetherthe zero restrictions proposed by either of the models presented here are valid in the data. Wetherefore adopt an identification procedure that does not rely on zero restrictions. As we haveshown in section II.A, a log level net output shock increases the log level of net output andresults in a current account surplus. Conversely, a log difference net output shock results in

14These variables are not demeaned as they are already standardized.

18

Table 1. Sign restrictions

Log Level Net Output shock Log Difference Net Output shockLN NOt ≥ 0 ≥ 0CAtNOt

≥ 0 ≤ 0FBtNOt

? ?

an increase in the log level of net output and a current account deficit. This provides mutu-ally exclusive sign restrictions, robust across all of the theoretical models considered in thispaper. We therefore use sign restrictions as pioneered by Canova and Nicolo (2002), Faustand Rogers (2003), and Uhlig (2005) to identify the shocks of interest. In particular, we usethe QR decomposition approach presented in Rubio-Ramirez, Waggoner, and Zha (2010) tosearch across the space of all possible structural VAR decompositions. Those that do not pro-duce impulse responses that satisfy the restrictions in table 1 are discarded and the remainingones are kept for inference.

The avid reader will note that our model is estimated on the log difference, but that we im-pose identification restrictions on the log level, of net output. This is necessary to disentanglethe two shocks, as the log level net output shock only implies a positive sign upon impact inthe net output log difference space and we impose restrictions upon impact and 4 years there-after.15 In other words, in the case of net output, we are imposing sign restrictions on the cu-mulative impulse responses of the log difference of this variable. Paustian (2007) points outthat this methodology will only recover the structural shocks if a sufficient number of restric-tions is imposed. To further ensure that this is the case, we also impose ‘shape’ restrictionson our impulse responses to rule out shocks to net output that do not correspond with thetheory.16 In particular, a stationary autoregressive log level (difference) net output process hasthe property that the response to an unexpected one-time shock does not increase (decrease)over time. Similarly, the theory implies that the current account response to any shock doesnot increase over time in absolute value. We therefore also discard structural decompositionswhich imply impulse responses that do not satisfy these shape restrictions.

Our proposed sign restrictions also emerge robustly from more complex open economy DSGEmodels. Koske and Fournier (2010) use the two-country New Open Economy Macroeconom-ics model of Ferrero, Gertler, and Svensson (2008), albeit without sticky prices, to investigate

15Imposing sign restrictions upon impact only does not produce substantially different results.16Identifying shocks with only sign, but no shape, restrictions yields similar results.

19

the effect of temporary and permanent productivity shocks on the current account throughthe savings channel. They find that the only scenario in which a temporary (permanent) pro-ductivity shock leads to a current account deficit (surplus) is by setting by their intertemporalelasticity of substitution parameter to 10, a high and unrealistic value compared to the exist-ing literature. 17 Otherwise, their model predicts a non-negative (non-positive) current ac-count response following a temporary (permanent) productivity shock. Similarly, Enders andMüller (2009) study the impact of permanent technology shocks on net exports18, allowingfor both the savings and investment channel, in a variant of the Backus, Kehoe, and Kyd-land (1994) international RBC model. Under the assumption of incomplete markets, whichis strongly supported by their time series evidence, they find that a permanent productivityshock leads to a current account deficit. Finally, Enders, Müller, and Scholl (2011) derive the-oretically robust sign restrictions from a very general two-country DSGE model. They as-sume a near-unit root process for their technology shock, meaning that it can be interpreted aspermanent, and find that with a low trade-price elasticity, a feature of consistent with the data(see Enders and Müller (2009)), a very large fraction of their net exports impulse responsesdisplay a negative reaction to the technology shock. This implies that the fact that a perma-nent technology shock has a negative effect on the current account is theoretically robust tomost parametrizations, given their very general two-country DSGE model with very wide pa-rameter intervals.19 Clearly, a temporary (permanent) technology shock would raise the level(growth rate) of output. We therefore argue that the log level (difference) net output shockidentified in our model probably reflects a temporary (permanent) productivity shock in thesemore complex DSGE models. If this is indeed the case, then our proposed identification re-strictions are consistent both with the standard intertemporal model of the current account aswell as more general open-economy DSGE models.

17In their survey of this parameter value in calibrated open-economy DSGE models, Enders, Müller, and Scholl(2011) report that the intertemporal elasticity of substitution is typically set between 0.5 and 1. Empirically, Gu-venen (2006) and Gruber (2006) estimate the elasticity of substitution to be 1 and 2, respectively. On the otherhand, Hall (1988) and Yogo (2004) conclude that tit is not statistically different from zero. While the empiri-cal literature has not reached a clear conclusion, an elasticity of 10 is a order of magnitude larger than what issupported by the existing empirical and theoretical work.18Net exports, or the trade balance, are linked to the current account through the following identity: CAt = T Bt +rBt .19Calibrated for annual data and with a perfect unit-root in the process for the productivity shock, it is probablethat all of their net exports impulse responses would react negatively to the permanent productivity shock.

20

C. Estimation and inference

There are different ways of estimating (17). One option is to pool the coefficients acrosscountries, assuming identical autoregressive dynamics across all units. If that assumption isviolated, the resulting dynamic heterogeneity bias will typically lead to an upward bias in theVAR coefficients (Canova, 2007), meaning that it is easy to mistake the temporary effect of ashock for a permanent one. Alternatively, Pesaran and Smith (1995) propose the mean groupestimator as a solution to the problem of heterogeneity in the lagged slope coefficients.20 Thismodel is implemented by estimating the VAR model country by country and then averagingthe country-specific VAR estimates to obtain the panel estimate. Rebucci (2003) points outthat in small time series, as in our case, mean group panel VAR estimates may be subject toserious small-sample bias. We therefore follow his prescription and pool the autoregressivecoefficients across countries. However, since we allow the VAR coefficients to depend on fi-nancial regulation, the dynamic heterogeneity bias should not be a significant problem in ourcase. Our theoretical model predicts that the variation in the persistence of the current accountis a result of different liquidity constraints across countries and our empirical strategy con-trols for this explicitly.

Following Uhlig (2005), we compute Bayesian error bands using a Normal-Wishart prior.21

Since the covariance matrix in model (17) is diagonal by construction, we can proceed equa-tion by equation. For each equation, we draw the parameters jointly from the posterior. Giventhe posterior draw, we then evaluate the interaction terms at the value of interest. For exam-ple, to obtain coefficients of the impact matrix, A0 , for a country with high financial regula-tion, we use A0,FinregHigh( j,h) = A0( j,h)+B1

0( j,h)FinregHIGH +B11( j,h)KAOpenMEDIAN ,

where FinregHIGH and KAOpenMEDIAN are high and median values of these indices. We usean analogous expression for each coefficient in the lagged dependent variable matrices. Wethen implement our identification procedure in the following manner: given A0,FinregHigh,which would correspond to the inverse of the Choleski decomposition in a reduced fromVAR, we follow Rubio-Ramirez, Waggoner, and Zha (2010) and obtain the QR decomposi-tion of a matrix of random numbers. We then verify if the structural decomposition A−1

0,FinregHighQ,together with the correspondingly evaluated lagged dependent variable matrices, generate im-pulse responses which are consistent with our sign and shape restrictions. If this is the case,

20See Sa, Towbin, and Wieladek (2011) for an application of the mean group estimator in interacted panel VARmodels.21Specifically, we implement algorithm 2.1 described in Del Negro and Schorfheide (2011). As in Cogley andSargent (2005), we discard draws that imply explosive VAR dynamics.

21

we keep this draw of A−10,FinregHighQ and the corresponding impulse responses. We repeat this

procedure until we have retained 100 draws which are used for inference.

D. Data

Our empirical strategy requires standard macroeconomic data and a good empirical mea-sure of financial regulation. There are a few data sets that measure financial liberalizationacross countries (Abiad, Detragiache, and Tressel, 2010; Kaminsky and Schmukler, 2003;Williamson and Mahar, 1998). We rely on the data set by Abiad, Detragiache, and Tressel(2010) for basically two reasons. First, their country and time coverage is very wide (91 coun-tries over the period 1973-2005) and thus appropriate for our panel VAR methodology. Sec-ond, their data set has seven graded components with special emphasis on domestic financialreform. Previous indices (eg, Kaminsky and Schmukler (2003)) put more weight on the lib-eralization of capital flows, which is not the central object of study in this paper. The sevencomponents of the dataset are: credit controls, interest rate controls, entry barriers, state own-ership in the banking sector, prudential regulation, securities market policy and capital ac-count restrictions. Each component can take the values {0,1,2,3} with higher values mean-ing less regulation/restrictions.22 We sum all components, but capital account restrictions, tocome up with the aggregate domestic financial regulation index we use in our empirical exer-cise. This index is normalized to one. Capital account restrictions are accounted for separatelyusing the index by Chinn and Ito (2008)). 23

Data on the following macroeconomic variables for the period 1973-2005 is necessary to testthe implications of the model: the current account balance, the fiscal balance, and a measureof net output. The IMF WEO gives us the current account balance and the fiscal balance incurrent US dollars. Net output is defined as GDP minus government consumption and in-vestment. We obtain the shares of government consumption and investment in GDP from theWorld Bank WDI and we combine them with GDP in current US dollars from the IMF WEOto calculate net output. To obtain real net output per capita, we divide this by the US GDPdeflator and total population. We remove outliers from the log difference of per capita netoutput, current account to net output ratio and fiscal balance to net output ratio by dropping

22In Abiad, Detragiache, and Tressel (2010), the prudential regulation component is an exception, as higher val-ues reflect more prudential rules. Since we want higher values of the aggregate index to reflect greater financialregulation, we change the numbering in this category such that higher values indicate less regulation. We notethat our results are robust to this change.23Using the capital account openness index provided by Abiad, Detragiache, and Tressel (2010) yields similarresults.

22

Table 2. Country sample

Albania Estonia MozambiqueArgentina Ethiopia MalaysiaAustralia Finland NetherlandAustria France NorwayBelgium United Kingdom NepalBurkina Faso Georgia New ZealandBangladesh Ghana PakistanBulgaria Greece PeruBelarus Guatemala PhilippinesBolivia Hungary PolandBrazil Indonesia PortugalCanada India ParaguaySwitzerland Ireland RomaniaChile Israel Russian FederationChina Italy SenegalCote d’Ivoire Jamaica SingaporeCameroon Jordan El SalvadorColombia Japan SwedenCosta Rica Kazakhstan ThailandCzech Republic Kenya TunisiaGermany Kyrgyz Rep. TurkeyDenmark Korea TanzaniaDominican Republic Sri Lanka UgandaAlgeria Lithuania UkraineEcuador Latvia UruguayEgypt Morocco Venezuela, RBSpain Madagascar Vietnam

Mexico South Africa

values outside the 98th and 2nd percentile of each variable’s distribution. Our sample consistsof 84 countries (table 2) and 1,756 annual observations.

IV. EMPIRICAL RESULTS

In this section we present the results from our panel VAR model and explore a number of ro-bustness checks. Evaluating the Finregi,t term at high and low values of financial regulationallows us to obtain average VAR coefficients under regimes of high and low financial regu-

23

lation.24 The remaining interaction term, KAOpeni,t , is evaluated at the median value of itsdistribution. We can then obtain impulse responses under both regimes and compare them toassess whether the reaction of the current account varies with the financial regulation regimeas predicted by the theory. The impulse response for the log level of net output was normal-ized to one in order to ensure that we are comparing current account responses to log levelnet output shocks of identical size across both regimes. This allows us to assess whether thereis a statistically significant difference between the impulse responses due to a change in thefinancial regulatory regime, rather than the nature of net output shocks. Figure 4 shows im-pulse responses to a log level net output shock with the financial regulation term evaluatedat both the 90th percentile (‘low finreg’ column) and 10th percentile (‘high fin reg’ column)of the distribution of this variable. These percentiles correspond to .23 and .83, respectively.The third column shows impulse responses obtained from a distribution of the difference inthe impulse responses obtained under the high and low financial regulatory regime. The redand green dashed lines indicate the 90 and 68 percent confidence bands, respectively. Figure 5repeats this exercise but for a log difference net output shock.

The median impact response of a log level net output shock on the current account is 0.57under high financial regulation and 0.9 under low financial regulation, which represents a 58percent increase in the impact of the shock if a country switches from high to low financialregulation (figure 4). As one can see in column three of figure 4, this difference is statisticallysignificant. Furthermore, column three in figure 4 shows that the median of the log net outputresponse is close to zero throughout. This suggests that the difference in the current accountresponse cannot be attributed to changes in the nature of the net output shock. For a log dif-ference net output shock (figure 5), the median impact response is -0.65 under high regulationand -1.05 under low regulation (61 percent increase). Again, the third column suggests thatthe difference in impulse responses is statistically significant and that the net output impulseresponses are not statistically different from each other. In both cases, the absolute value ofthe current account response is larger and more persistent under low financial regulation thanunder high regulation. Our theory predicted, that all else equal, the change in financial regula-tion should affect the impulse response of the current account balance to either type of shockin a similar way. Indeed, the change in the impact response and the persistence profile is verysimilar across both shocks, which provides additional verification for the theory.

We also evaluate the capital account openness term (KAOpen) at the 90th percentile (‘highKA Open’ column) and 10th percentile (‘low KA Open’ column) with the financial regulation

24We tried entering the banking supervision sub-indicator separately as it may affect liquidity constraints differ-ently than the other sub-components but the results were not significant.

24

term evaluated at the median. This yields less clear-cut results than for the financial regulationterm. As figure 6 shows, the response of the current account to a log level net output shockintensifies at high levels of capital account restrictions but the third column shows that thedifference between low and high capital account restrictions is not statistically significant. Forthe case of a log difference net output shock (figure 7), we find a more statistically significantrole for capital account restrictions. But the difference in impulse responses goes in the wrongdirection and as shown below is not robust.

A. Robustness

This subsection shows that the results presented so far are robust to the inclusion of the ex-change rate regime as an additional determinant of the panel VAR coefficients and also at-tempts to address potential concerns regarding the dynamic heterogeneity bias.

The empirical results presented so far rely on the assumption that financial regulation andcapital account restrictions are the only relevant determinants of the VAR coefficients. Butthere could also be other important empirical determinants of these coefficients that we arenot controlling for, leaving the findings presented thus far vulnerable to omitted variable bias.An additional natural determinant of current account dynamics is the exchange rate regime.In theory, one would expect that the speed of adjustment of the current account under a fixedand floating exchange rate regime would differ (Friedman, 1953). However, Chinn and Wei(2008) document that it is difficult to find a statistically significant relationship between cur-rent account persistence and the exchange rate regime. Nevertheless, we also introduce thefine defacto exchange rate classification by Reinhart and Rogoff (2010) as an additional de-terminant of the VAR coefficients.25 Figures 8-11 show that our previous results regarding theeffect of financial regulation on the current account to net output ratio are robust to control-ing for the exchange rate regime. Furthermore, the difference in the current account responsebetween high and low capital account restrictions, to either type of shock, is no longer statisti-cally significant.

The potential for dynamic heterogeneity bias in our panel VAR makes inference about the ad-justment dynamics of the current account with absolute certainty difficult. Recently, Jarocin-ski (2010) introduced a new Bayesian panel VAR estimator to deal with this problem. In hisapproach, there is a shrinkage parameter λ which determines the degree of pooling in thepanel VAR model. If λ = 0 then the VAR estimates will correspond to a pooled panel VAR

25Results using the de jure classification from the IMF AREAER database are very similar.

25

model as in our case and if λ → ∞, his model will estimate country-by-country VARs. Themain advantage of his approach is that λ , and therefore the degree to which pooling is sup-ported or not, is determined by the data. The disadvantage of his methodology is that it is notpossible to estimate panel VARs with different variables in each equation, as in (17). Nev-ertheless, to assess if the dynamic heterogeneity bias is likely to be a problem in our appli-cation, we estimated the reduced form version of (17) with the Jarocinski (2010) approach,dropping all the contemporaneous endogenous variables and the corresponding interactionterms from the model. We estimated this model with 150,000 repetitions of the Gibbs sam-pling chain, discarding the first 100,000 draws as burn in and keeping every 100th draw ofthe remainder for inference. While it is not possible to undertake similar structural VAR exer-cises with estimates from this model, the estimated λ is still likely to be informative about theappropriateness of pooling. Jarocinski (2010) suggest looking at the square root of λ for thispurpose. Figure 12 shows the distribution of

√λ from this model.

√λ is heavily clustered

around a value of .0014, which is close to zero. This suggests that dynamic heterogeneity biasis probably not a substantial problem in our application.

Finally, there is also an issue regarding the potential observational equivalence of the net out-put shock in log differences and a stationary shock to the fiscal balance, f bt . Following a sta-tionary negative shock to the fiscal balance, our theory predicts a current account deficit. Ifthis shock has a positive effect on the level of net output as well, it would not be possible toseparately identify it from a log difference net output shock using only sign restrictions. But,since the government’s budget has to be balanced in the long-run, a temporary fiscal balanceshock would only have a temporary effect on the level of net output. In other words, follow-ing a negative fiscal balance shock, one would expect a current account deficit, with the levelof net output increasing upon impact and then decreasing over time. This could be potentiallyobservationally equivalent to the log difference net output shock. Fortunately, our shape re-striction, that a log difference net output shock leads to a non-decreasing (permanent) effecton the level of log net output, allows us to separate these two shocks explicitly. Furthermore,our theory suggests that, all else equal, the easing of the liquidity constraint should have asimilar effect on the current account, regardless of the assumed process for the log net out-put shock. Indeed, the results do suggest that this is the case, which implies that our proposedshape restriction is probably ensuring that inference from the retained impulse responses isnot contaminated by observationally equivalent fiscal policy shocks.

26

V. CONCLUSION

Global current account imbalances and financial regulation have been at the forefront of eco-nomic policy debates since the global financial crisis broke out in late 2008. A growing liter-ature has analyzed the role of global imbalances and financial regulation in the run-up to thecrisis and the debate on the future regulatory landscape is equally active. However, the ideathat financial regulation might affect the size and dynamics of current account imbalances re-mains largely unexplored despite its potentially important implications. This paper providesthe first empirical evidence supporting the existence of a link between financial regulation andthe current account.

We introduce liquidity constraints, as a proxy for domestic financial regulation, into the stan-dard intertemporal model of the current account and show that the response of the current ac-count to net output shocks depends on the intensity of the liquidity constraints. More specifi-cally, the response of the current account to a net output shock increases as fewer householdsare liquidity constrained. This conclusion is robust to various standard modeling choices,such as the introduction of a stochastic world real interest rate or habit formation in consump-tion. In the case of external habit formation in consumption, the persistence of the currentaccount response increases with a smaller liquidity constraint as well. Using a sample of 84countries over the period 1973-2005, we test these theoretical predictions in an interactedpanel VAR framework, identifying net output shocks with sign and shape restrictions. Thestructural VAR coefficients are allowed to vary with the degree of domestic financial regu-lation at the individual-country level. This setting allows us to assess if financial regulationaffects the size and persistence response of the current account to a net output shock as pre-dicted by the theory. In our baseline specification, the median current account impulse re-sponse to a net output shock is 60 percent larger upon impact, as well as substantially morepersistent, under a low than under a high financial regulatory regime. This is robust to allow-ing the coefficients to vary with capital account openness and the exchange rate regime.

The results in this paper show that domestic financial reform has an important impact onthe size and dynamics of current account balances. To our knowledge, this insight is not aprominent part of the current global financial reform debate but our findings suggest that itcould have important implications for the functioning of the international monetary system.Changes in financial regulation may have a meaningful impact on current account imbalancesand could be a tool that helps mitigate the imperfections in the international monetary system.Most likely, we are just scratching the surface of a policy-relevant research topic. There aremany interesting questions left to explore, such as for example how domestic financial reg-

27

ulation affects the composition of capital flows to a given country, that future research couldaddress.

28

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32

APPENDIX A. APPENDIX

A.1. Deriving the linearized budget constraint

First, to go from (7) to (8), start with (7):

Et

∞

∑i=0

Rt,iCRt+i = (1+ rt)(BP

t +BGt )+Et

∞

∑i=0

Rt,iNOt+i

then note that Rt,1 = 1 and factor out CRt and NOt on the LHS and RHS, respectively:

CRt [1+

∞

∑i=1

EtRt,iCR

t+i

CRt] = (1+ rt)(BP

t +BGt )+NOt [1+

∞

∑i=1

EtRt,iNOt+i

NOt]

Then divide the whole expression by NOt

CRt

NOt[1+

∞

∑i=1

EtRt,iCR

t+i

CRt] = (1+ rt)(

BPt +BG

tNOt

)+ [1+∞

∑i=1

EtRt,iNOt+i

NOt]

Now recall that 1/(t+i∏

j=t+1(1+ r j)) and take logs and exponential in the infinite summations:

CRt

NOt[1+

∞

∑i=1

Et exp

{lnCR

t+i− lnCRt − ln(

t+i

∏j=t+1

(1+ r j))

}]

= exp(ln(1+ rt))(BP

t +BGt

NOt)+ [1+

∞

∑i=1

Et exp

{lnNOt+i− lnNOt− ln(

t+i

∏j=t+1

(1+ r j))

}]

Applying the log to the interest rate factorial, it is easy to see that:

CRt

NOt[1+

∞

∑i=1

Et exp

{lnCR

t+i− lnCRt − (

t+i

∑j=t+1

ln(1+ r j))

}]

= exp(ln(1+ rt))(BP

t +BGt

NOt)+ [1+

∞

∑i=1

Et exp

{lnNOt+i− lnNOt− (

t+i

∑j=t+1

ln(1+ r j))

}]

Now we can add and subtract infinitely many lnCRt and lnNOt :

33

CRt

NOt[1+

∞

∑i=1

Et exp

{lnCR

t+i− lnCRt+i−1 + lnCR

t+i−1...

− lnCRt+1 + lnCR

t+1− lnCRt − (∑t+i

j=t+1 ln(1+ r j))

}]

= exp(ln(1+ rt))(BP

t +BGt

NOt)+

[1+∞

∑i=1

Et exp

{lnNOt+i− lnNOt+i−1 + lnNOt+i−1...

− lnNOt+1 + lnNOt+1− lnNOt− (∑t+ij=t+1 ln(1+ r j))

}]

which is (8) in the main text:

Ct

NOt[1+

∞

∑i=1

Et exp

{t+i

∑j=t+1

(∆ lnC j− ln(1+ r j))

}]

= exp{ln(1+ rt)}Bt

NOt+[1+

∞

∑i=1

Et exp

{t+i

∑j=t+1

(∆ lnNO j− ln(1+ r j))

}]

To linearize this expression, we use the standard formula f (x) = f (a)+ f ′(x)(x−a)

c[1+∞

∑i=1

κi]+

Ct

NOt[1+

∞

∑i=1

κi]+ c[1+

∞

∑i=1

κi]

∞

∑i=1

κiEt

{∆ ˜lnCRt+i− ln ˜(1+ rt+i)

}=

1e−µ

Bt

NOt+

be−µ

˜ln(1+ rt)+ [1+∞

∑i=1

κi]+ [1+

∞

∑i=1

κi][

∞

∑i=1

κiEt

{∆ ˜lnNOt+i− ln ˜(1+ rt+i)

}]

Now note that [1+∑∞i=1 κ i] = [1+κ ∑

∞i=0 κ i] = [1+ κ

1−κ] = [ 1

1−κ] and also note that in steady

state, when all the hat variables take the value of 0, c[1+∑∞i=1 κ i] = [1+∑

∞i=1 κ i], then it is

easy to obtain the final linearized form:

CRt

NOt=

1−κ

e−µ(

BGt

NOt+

BPt

NOt)− c

∞

∑i=1

κiEt

{∆ ˜lnCRt+i− ln ˜(1+ rt+i)

}(18)

+∞

∑i=1

κiEt

{∆ ˜lnNOt+i− ln ˜(1+ rt+i)

}+

1−κ

e−µ(bP +bG) ln ˜(1+ rt)

34

A.2. Derivation of the current account reaction functionwith external habits and a constant world real interest rate

We start by linearizing the definition of aggregate consumption Ct = (1− λ )CRt + λCNR

t toobtain

Ct

NOt= (1−λ )

CRt

NOt+λ (

Gt

NOt− Tt

NOt). (19)

Following Obstfeld and Rogoff (1996), the current account can be expressed as as:

CAt = Bt+1−Bt = NOt + rBt−Ct

which, noting that Bt = (1−λ )BPt +BG

t and r = eln(1+r)−1, can be linearized as the followingthe current account to net output ratio:

cat = (eµ −1)((1−λ )BP

t

NOt+

BGt

NOt)− Ct

NOt(20)

where cat =CAtNOt

. Now plugging (18) into (19), then into (20) and simplifying yields26:

cat = λ [rBG

t

NOt+

Tt

NOt− Gt

NOt]− ((1−λ )(eµ −1)bG− eµbG) ln ˜(1+ rt)

+(1−λ )CR

NO

∞

∑i=1

κiEt

{∆ ˜lnCRt+i− ln ˜(1+ rt+i)

}−(1−λ )

∞

∑i=1

κiEt

{∆ ˜lnNOt+i− ln ˜(1+ rt+i)

}

The constant world real interest rate assumption implies ln ˜(1+ rt) = 0 ∀ i. We also add and

subtract (1−λ )hcat−1 and define f bt = [r BGt

NOt+ Tt

NOt− Gt

NOt] to obtain

26Where we used 1eγ = 1 and (eµ −1) = r as in Kano (2008).

35

cat = (1−λ )hcat−1 +λ f bt +(1−λ )CR

NO

∞

∑i=1

κiEt

{∆ ˜lnCRt+i

}(21)

−(1−λ )hλ f bt−1−h(1−λ )2 CR

NO

∞

∑i=1

κiEt−1

{∆ ˜lnCRt+i−1

}−(1−λ )

∞

∑i=1

κiEt

{∆ ˜lnNOt+i

}+h(1−λ )2

∞

∑i=1

κiEt−1

{∆ ˜lnNOt+i−1

}

Furthermore, log-linearizing the Euler equation gives

Et∆˜lnCRt = h

C

CR∆lnCt−1

which can be expressed as

Et∆˜lnCRt = h(1−λ )∆ ˜lnCRt−1 +hλ

NO

CR∆ ˜lnNOt−1 +hλ

G

CR∆lnGt−1−hλ

T

CR∆lnT t−1 (22)

Plugging (22) into (21) gives

cat = (1−λ )hcat−1 +λ f bt +h(1−λ )2c∞

∑i=1

κiEt

{∆ ˜lnCRt+i−1

}(23)

+hλ (1−λ )∞

∑i=1

κiEt

{∆ ˜lnNOt+i−1

}+hλ (1−λ )g

∞

∑i=1

κiEt

{∆lnGt+i−1

}−hλ (1−λ )t

∞

∑i=1

κiEt

{∆lnT t+i−1

}− (1−λ )hλ f bt−1

−h(1−λ )2c∞

∑i=1

κiEt−1

{∆ ˜lnCRt+i−1

}− (1−λ )

∞

∑i=1

κiEt

{∆ ˜lnNOt+i

}+h(1−λ )2

∞

∑i=1

κiEt−1

{∆ ˜lnNOt+i−1

}

where c = CR

NO; g = G

NOand t = T

NO.

At this point, it is convenient to linearize the government’s budget constraint. Using Et ∑∞i=0 Rt,iGt+i =

(1+ rt)(BGt )+Et ∑

∞i=0 Rt,iTt+i , the government’s intertemporal budget constraint can be writ-

ten as:

36

Gt

NOt[1+Et

∞

∑i=0

Rt,iGt+i

Gt] = (1+ rt)(

BGt

NOt)+ [1+Et

∞

∑i=0

Rt,iTt+i]Tt

NOt

The linearized version of this expression is

g[1+∞

∑i=1

κi]+

Gt

NOt[1+

∞

∑i=1

κi]+g[1+

∞

∑i=1

κi]

∞

∑i=1

κiEt

{∆lnGt+i

}=

1e−µ

BGt

NOt+ t[1+

∞

∑i=1

κi]+

Tt

NOt[1+

∞

∑i=1

κi]+ t[1+

∞

∑i=1

κi][

∞

∑i=1

κiEt

{∆lnT t+i

}]

Under the assumption that g = t, this can then be expressed as:

g∞

∑i=1

κiEt

{∆lnGt+i

}− t[

∞

∑i=1

κiEt

{∆lnT t+i

}] (24)

=1−κ

e−µ

BGt

NOt+

Tt

NOt− Gt

NOt

Plugging (24) into (23) yields

cat = (1−λ )hcat−1 +λ f bt +h(1−λ )2c∞

∑i=1

κi(Et−Et−1)

{∆ ˜lnCRt+i−1

}+hλ (1−λ )

∞

∑i=1

κiEt

{∆ ˜lnNOt+i−1

}−(1−λ )

∞

∑i=1

κiEt

{∆ ˜lnNOt+i

}+h(1−λ )2

∞

∑i=1

κiEt−1

{∆ ˜lnNOt+i−1

}Define ft = h(1−λ )2c∑

∞i=1 κ i(Et−Et−1)

{∆ ˜lnCRt+i−1

}and use ∑

∞i=1 κ iEt

{∆ ˜lnNOt+i−1

}=

κ ∑∞i=0 κ iEt

{∆ ˜lnNOt+i

}and ∑

∞i=1 κ iEt

{∆ ˜lnNOt+i

}+∆ ˜lnNOt = ∑

∞i=0 κ iEt

{∆ ˜lnNOt+i

}to

obtain

cat = (1−λ )hcat−1 +λ f bt + ft− (1−hκλ )(1−λ )∞

∑i=0

κiEt

{∆ ˜lnNOt+i

}+h(1−λ )2

∞

∑i=1

κiEt−1

{∆ ˜lnNOt+i−1

}+(1−λ )∆ ˜lnNOt

37

Now using ∑∞i=1 κ iEt

{∆ ˜lnNOt+i−1

}= κ ∑

∞i=0 κ iEt

{∆ ˜lnNOt+i

}and adding and subtracting

hκ(1−λ )2∑

∞i=0 κ iEt

{∆ ˜lnNOt+i

}

cat = (1−λ )hcat−1 +λ f bt− (1−hκ)(1−λ )∞

∑i=0

κiEt

{∆ ˜lnNOt+i

}+(1−λ )∆ ˜lnNOt−hκ(1−λ )2

∞

∑i=0

κi(Et−Et−1)

{∆ ˜lnNOt+i

}+ ft

which is (10) in the main text. To further solve this expression, it is necessary to assume aprocess for ˜lnNOt . Under the assumption of ∆ lnNOt = ρ∆ lnNOt−1 + εt (10) can be ex-pressed as:

cat = (1−λ )hcat−1 +λ f bt−(1−λ )(1−hκ)

1−ρκ∆ ˜lnNOt

+(1−λ )∆ ˜lnNOt−hκ(1−λ )(1−λ )

(1−κ)εt + ft

where we replaced hκ(1− λ )(1− λ )∑∞i=0 κ i(Et −Et−1)∆ ˜lnNOt+i with hκ(1−λ )(1−λ )

(1−κ) εt and

(1− λ )(1− hκ)∑∞i=0 κ iEt∆ ˜lnNOt+i with (1−λ )(1−hκ)

1−ρκ∆ ˜lnNOt . After further simplification,

one can then obtain (13) in the main text:

cat = (1−λ )hcat−1 +λ f bt−(1−λ )κ(ρ−h)

1−ρκρ∆ ˜lnNOt−1

−κ(1−λ )(ρ(1−κ)+h(κ−λ )−ρκh(1−λ ))

(1−κ)(1−ρκ)εt + ft

To derive (15) note that under the assumption that lnNOt = ρ lnNOt−1 + εt , ∆ ˜lnNOt =

(ρ − 1) lnNOt−1 + εt and follow the steps above. In this case hκ(1−λ )(1−λ )∑∞i=0 κ i(Et −

Et−1)∆ ˜lnNOt+i = −(1− ρ)hκ(1−λ )(1−λ )(1−κ) εt and (1− λ )(1− hκ)∑

∞i=0 κ iEt∆ ˜lnNOt+i =

−(1−ρ) (1−λ )(1−hκ)1−ρκ

˜lnNOt−1 .

38

A.3. Derivation of the current account reaction functionwith internal habits and a constant world real interest rate

Start with

cat = λ f bt +(1−λ )cκh

1−κh∆ ˜lnCRt− (1−λ )

∞

∑i=1

κiEt

{∆ ˜lnNOt+i

}In order to solve out for the growth rate of consumption in the current account equation, weadd and subtract hcat−1 on both sides of the equation. This yields

cat = hcat−1 +λ f bt−λh f bt−1− (1−λ )∞

∑i=1

κiEt

{∆ ˜lnNOt+i

}+(1−λ )h

∞

∑i=1

κiEt−1

{∆ ˜lnNOt+i

}+(1−λ )

cκh1−κh

(∆ ˜lnCRt−h∆ ˜lnCRt−1)

Now note that with internal habits in consumption, (5) can be log-linearized as Et∆˜lnCRt+1 =

h∆ ˜lnCRt . Then use ∑∞i=1 κ iEt

{∆ ˜lnNOt+i−1

}= κ ∑

∞i=0 κ iEt

{∆ ˜lnNOt+i

}, ∑

∞i=1 κ iEt

{∆ ˜lnNOt+i

}+

∆ ˜lnNOt = ∑∞i=0 κ iEt

{∆ ˜lnNOt+i

}and add and subtract (1−λ )(hκ)∑

∞i=0 κ iEt

{∆ ˜lnNOt+i

}to obtain (11) in the main text:

cat = hcat−1 +λ f bt−λh f bt−1 +(1−λ )∆ ˜lnNOt− (1−λ )(1−hκ)∞

∑i=0

κiEt

{∆ ˜lnNOt+i

}−(1−λ )(hκ)

∞

∑i=0

κi(Et−Et−1)

{∆ ˜lnNOt+i

}+ ft

where ft =(1−λ ) cκh1−κh(∆

˜lnCRt−Et−1∆ ˜lnCRt). Solving under the assumption that ∆ lnNOt =

ρ∆ lnNOt−1 + εt yields (14)

cat = hcat−1 +λ f bt−λh f bt−1 +(1−λ )κ(h−ρ)

1−ρκ∆ ˜lnNOt−1

−(1−λ )κρ(1−κ)+hκ(1−ρ)

(1−κ)(1−ρκ)εt + ft

39

where we replaced (1−λ )(1−hκ)∑∞i=0 κ iEt

{∆ ˜lnNOt+i

}with (1−λ )(1−hκ)ρκ

1−ρκ∆ ˜lnNOt , using

11−x =

∞

∑i=0

xi, and (1−λ )(hκ)∑∞i=0 κ i(Et−Et−1)

{∆ ˜lnNOt+i

}with (1−λ )(hκ)

(1−κ) εt . To derive (16)

note that under the assumption that lnNOt = ρ lnNOt−1 + εt , ∆ ˜lnNOt = (ρ−1) lnNOt−1 + εt

and follow the same steps as before.

A.4. Derivation of the current account reaction function under a stochastictime-varying world real interest rate and no habitual consumption

This section derives the current account reaction function under a stochastic world interestrate and shows the observational equivalence with the model with internal habits. As a firststep, linearize the definition of the current account (CAt = NOt + rtBt−Ct) under the assump-tion of a time-varying stochastic world real interest rate to obtain

cat = (eµ −1)((1−λ )BP

t

NOt+

BGt

NOt)+ eµ((1−λ )bP +bG) ln ˜(1+ rt)−

Ct

NOt(25)

Equation (5) can be written as

Et∆ lnCRt+1 = δ + lnβ +Et ln(1+ rt+1)

linearizing this expression yields: ∆ ˜lnCRt = ln ˜(1+ rt+i). Plugging this into (9) yields

CRt

NOt=

1−κ

e−µ(

BGt

NOt+

BPt

NOt)+

1−κ

e−µ(bP+bG) ln ˜(1+ rt)+

∞

∑i=1

κiEt

{∆ ˜lnNOt+i− ln ˜(1+ rt+i)

}Now, plugging this into (25) and simplifying27 yields:

cat = λ [rBG

t

NOt+

Tt

NOt− Gt

NOt]− ((1−λ )(eµ −1)bG− eµbG) ln ˜(1+ rt)

−(1−λ )∞

∑i=1

κiEt

{∆ ˜lnNOt+i

}+(1−λ )

∞

∑i=1

κiEt

{ln ˜(1+ rt+i)

}

27An important result, following Kano (2008), that we use throughout our derivations to simplify the equationsis (eµ −1) = (eµ − eγ) = eµ(1− eγ−µ) = (1−κ)

e−µ since eγ ≈ 1.

40

Now, to show observational equivalence between the above and the model with internal habits,note that ln ˜(1+ rt+i) ≡ rt+i . Assuming an AR(1) process for the world real interest rate,rt = ρr rt−1 +ηt , one can then rewrite the above as

cat = λ [rBG

t

NOt+

Tt

NOt− Gt

NOt]− ((1−λ )(eµ −1)bG− eµbG)ρr rt−1 (26)

−(1−λ )∞

∑i=1

κiEt

{∆ ˜lnNOt+i

}+

(1−λ )ρrκ

1−ρrκρ

r rt−1 + ft (27)

where ft = [((1−λ )(eµ −1)bG− eµbG)+ (1−λ )ρrκ

1−ρrκ]ηt . Comparing these last two expressions

and using ∑∞i=1 κ iEt

{∆ ˜lnNOt+i

}+∆ ˜lnNOt = ∑

∞i=0 κ iEt

{∆ ˜lnNOt+i

}, one can see that:

cat = ρrcat +λ [r

BGt

NOt+

Tt

NOt− Gt

NOt]−ρ

rλ [r

BGt−1

NOt−1+

Tt−1

NOt−1− Gt−1

NOt−1]+ (1−λ )∆ ˜lnNOt

−(1−λ )(1−ρrκ)

∞

∑i=0

κiEt

{∆ ˜lnNOt+i

}− (1−λ )(ρr

κ)∞

∑i=0

κi(Et−Et−1)

{∆ ˜lnNOt+i

}+ ft

which, as in Kano (2009)), is observationally equivalent to the solution under internal habits.

41

Figure 1. Size of current account imbalances(in percent of world GDP)

 

 

 

0

1

2

3

4

5

6

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

Absolute value of world current accounts

Source: IMF WEO

0

5

10

15

20

25

1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

OECD median

EM median

Source: Abiad et al. (2010)

Figure 2. Financial deregulation index

 

 

 

0

1

2

3

4

5

619

80

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

Absolute value of world current accounts

Source: IMF WEO

0

5

10

15

20

25

1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

OECD median

EM median

Source: Abiad et al. (2010)

42

Figure 3. Current account persistence(as a share of GDP)

�39 �

Figure 1 Figure 2

Size of current account imbalances over time Financial deregulation over time

0

5

10

15

20

1973 1977 1981 1985 1989 1993 1997 2001 2005

EME financial deregulation

OECD financial deregulation

Index

Source: IMF WEO. Source: Abiad et al (2010).

1973­1982y = 0.6515x ­ 0.0156

­0.5

­0.4

­0.3

­0.2

­0.1

0

0.1

0.2

0.3

­0.5 ­0.4 ­0.3 ­0.2 ­0.1 0 0.1 0.2 0.3

1983­1992y = 0.7958x ­ 0.0037

­0.5

­0.4

­0.3

­0.2

­0.1

0

0.1

0.2

0.3

­0.5 ­0.4 ­0.3 ­0.2 ­0.1 0 0.1 0.2 0.3

1993­2003y = 0.8241x ­ 0.0019

­0.5

­0.4

­0.3

­0.2

­0.1

0

0.1

0.2

0.3

­0.5 ­0.4 ­0.3 ­0.2 ­0.1 0 0.1 0.2 0.3

2003­2007y = 0.9969x ­ 0.0018

­0.5

­0.4

­0.3

­0.2

­0.1

0

0.1

0.2

0.3

­0.5 ­0.4 ­0.3 ­0.2 ­0.1 0 0.1 0.2 0.3 0.4

Source: IMF WEO and sta¤ calculations

43

Figure 4. Impulse response functions to log level net output shock - financial regulation

�40 �

Figure 4 - Log level net output shock- Financial Regulation

0 5 100

0.5

1

1.5C

A / 

Net

 YLow FIN REG

0 5 10­1

0

1

2

LN N

et Y

0 5 10­1

0

1

2

FB / 

Net

 Y

0 5 100

0.5

1

1.5High FIN REG

0 5 10­0.5

0

0.5

1

1.5

0 5 10­1

­0.5

0

0.5

0 5 10­0.5

0

0.5

1High minus Low FIN REG

0 5 10­2

­1

0

1

2

0 5 10­1

0

1

2

Median 5th 95th 16th 84th

44

Figure 5. Impulse response functions to log difference net output shock - financial regula-tion

�41 �

Figure 5 - Log di¤erence net output shock-Financial Regulation

0 5 10­1.5

­1

­0.5

0C

A / 

Net

 Y

Low FIN REG

0 5 100

1

2

3

4

LN N

et Y

0 5 10­1

­0.5

0

0.5

1

FB / 

Net

 Y

0 5 10­1.5

­1

­0.5

0High FIN REG

0 5 100

1

2

3

0 5 10­0.5

0

0.5

1

1.5

0 5 10­1.5

­1

­0.5

0

0.5High minus Low FIN REG

0 5 10­2

­1

0

1

2

0 5 10­2

­1

0

1

Median 5th 95th 16th 84th

45

Figure 6. Impulse response functions to log level net output shock - capital account open-ness

�42 �

Figure 6 - Log level net output shock - Capital Account Openess

0 5 100

0.5

1C

A / 

Net

 YHigh KA OPEN

0 5 10­1

0

1

2

LN N

et Y

0 5 10­1

­0.5

0

0.5

FB / 

Net

 Y

0 5 100

0.5

1

1.5Low KA OPEN

0 5 10­1

0

1

2

0 5 10­1

0

1

2

0 5 10­1

­0.5

0

0.5Low minus High KA OPEN

0 5 10­2

­1

0

1

0 5 10­4

­2

0

2

Median 5th 95th 16th 84th

46

Figure 7. Impulse response functions to log difference net output shock - capital accountopenness

�43 �

Figure 7 - Log di¤erence net output shock - Capital Account Openess

0 5 10­4

­2

0C

A / 

Net

 YHigh KA OPEN

0 5 100

5

10

LN N

et Y

0 5 10­2

024

FB / 

Net

 Y

0 5 10­2

­1

0Low KA OPEN

0 5 100

2

4

0 5 10­2

­101

0 5 10­4

­202

Low minus High KA OPEN

0 5 10­2

0

2

0 5 10­2

024

Median 5th 95th 16th 84th

47

Figure 8. Impulse response functions to log level output shock - financial regulation control-ing for fx regime

�44 �

Figure 8 - Log level net output shock -Financial Regulation - Controlling for FX Regime

0 5 100

0.5

1

1.5C

A / 

Net

 YLow FIN REG

0 5 10­1

0

1

2

LN N

et Y

0 5 10­1

­0.50

0.51

FB / 

Net

 Y

0 5 100

0.5

1

1.5High FIN REG

0 5 10­0.5

00.5

11.5

0 5 10­1

­0.5

0

0.5

0 5 10­0.5

0

0.5

1High minus Low FIN REG

0 5 10­2

­1012

0 5 10­1

0

1

2

Median 5th 95th 16th 84th

48

Figure 9. Impulse response functions to log difference output shock - financial regulationcontroling for fx regime

�45 �

Figure 9 - Log di¤erence net output shock - Financial Regulation - Controlling for FX Regime

0 5 10­2

­1

0C

A / 

Net

 YLow FIN REG

0 5 100

2

4

6

LN N

et Y

0 5 10­2

0

2

FB / 

Net

 Y

0 5 10­1.5

­1

­0.5

0High FIN REG

0 5 100

2

4

0 5 10­1

0

1

2

0 5 10­1

0

1High minus Low FIN REG

0 5 10­2

0

2

4

0 5 10­4

­2

0

2

Median 5th 95th 16th 84th

49

Figure 10. Impulse response functions to log level output shock - capital account opennesscontroling for fx regime

�46 �

Figure 10 - Log level net output shock - Capital Account Openess - Controlling for FX Regime

0 5 100

0.5

1C

A / 

Net

 YHigh KA OPEN

0 5 10­1

012

LN N

et Y

0 5 10­1

­0.50

0.5

FB / 

Net

 Y

0 5 100

0.51

1.5Low KA OPEN

0 5 10­1

012

0 5 10­1

012

0 5 10­1

­0.50

0.5Low minus High KA OPEN

0 5 10­2

­101

0 5 10­2

­101

Median 5th 95th 16th 84th

Figure 11. Impulse response functions to log difference output shock - capital accountopenness controling for fx regime

�47 �

Figure 11 - Log di¤erence net output shock - Capital Account Openess - Controlling for FX Regime

0 5 10­3

­2­10

CA

 / N

et Y

High KA OPEN

0 5 100

246

LN N

et Y

0 5 10­1

012

FB / 

Net

 Y

0 5 10­2

­1

0Low KA OPEN

0 5 100

2

4

0 5 10­2

­101

0 5 10­2

­101

Low minus High KA OPEN

0 5 10­2

0

2

0 5 10­2

024

Median 5th 95th 16th 84th

50

Figure 12. Histogram of the square root of λ

�48 �

Figure 12 - Histogram of square root 2p�

0.8 1 1.2 1.4 1.6 1.8 2 2.2x 10­3

0

20

40

60

80

100

120