MAIN MACROECONOMIC INDICATORS

Macroeconomic indicators are statistics that indicate the current status of the economy of a state depending on a particular area of the economy (industry, labor

market, trade, etc.). They are published regularly at a certain time by governmental agencies and the private sector.

Markets.com provides an Economic Calendar for the dates of critical fundamental announcements and events. When properly used, these indicators can be an

invaluable resource for any Forex trader.

In truth, these statistics help Forex traders monitor the economy's pulse; thus it is not surprising that these are religiously followed by almost everyone in the financial

markets. After publication of these indicators we can observe volatility of the market. The degree of volatility is determined depending on the importance of an

indicator. That is why it is important to understand which indicator is important and what it represents.

Interest Rates Announcement

Interest rates play the most important role in moving the prices of currencies in the foreign exchange market. As the institutions that set interest rates, central banks are

therefore the most influential actors. Interest rates dictate flows of investment. Since currencies are the representations of a country’s economy, differences in interest

rates affect the relative worth of currencies in relation to one another. When central banks change interest rates they cause the forex market to experience movement

and volatility. In the realm of Forex trading, accurate speculation of central banks’ actions can enhance the trader's chances for a successful trade.

Gross Domestic Product (GDP)

The GDP is the broadest measure of a country's economy, and it represents the total market value of all goods and services produced in a country during a given year.

Since the GDP figure itself is often considered a lagging indicator, most traders focus on the two reports that are issued in the months before the final GDP figures: the

advance report and the preliminary report. Significant revisions between these reports can cause considerable volatility.

Consumer Price Index

The Consumer Price Index (CPI) is probably the most crucial indicator of inflation. It represents changes in the level of retail prices for the basic consumer basket.

Inflation is tied directly to the purchasing power of a currency within its borders and affects its standing on the international markets. If the economy develops in

normal conditions, the increase in CPI can lead to an increase in basic interest rates. This, in turn, leads to an increase in the attractiveness of a currency.

Employment Indicators

Employment indicators reflect the overall health of an economy or business cycle. In order to understand how an economy is functioning, it is important to know how

many jobs are being created or destructed, what percentage of the work force is actively working, and how many new people are claiming unemployment. For

inflation measurement, it is also important to monitor the speed at which wages are growing.

Retail Sales

The retail sales indicator is released on a monthly basis and is important to the foreign exchange trader because it shows the overall strength of consumer spending and

the success of retail stores. The report is particularly useful because it is a timely indicator of broad consumer spending patterns that is adjusted for seasonal variables.

It can be used to predict the performance of more important lagging indicators, and to assess the immediate direction of an economy.

Balance of Payments

The Balance of Payments represents the ratio between the amount of payments received from abroad and the amount of payments going abroad. In other words, it

shows the total foreign trade operations, trade balance, and balance between export and import, transfer payments. If coming payment exceeds payments to other

countries and international organizations the balance of payments is positive. The surplus is a favorable factor for growth of the national currency.

Government Fiscal and Monetary policy

Stabilization of the economy (e.g., full employment, control of inflation, and an equitable balance of payments) is one of the goals that governments attempt to

achieve through manipulation of fiscal and monetary policies. Fiscal policy relates to taxes and expenditures, monetary policy to financial markets and the supply of

credit, money, and other financial assets.

Conclusion: There are many economic indicators, and even more private reports that can be used to evaluate the fundamentals of forex. It's important to take the time

to not only look at the numbers, but also understand what they mean and how they affect a nation's economy.

MEASURING MACROECONOMIC INSTABILITY: A CRITICAL SURVEY ILLUSTRATED WITH EXPORTS SERIES

Authors

Joël Cariolle,1.

Michaël Goujon1.

First published: 9 July 2013Full publication history

DOI: 10.1111/joes.12036View/save citation

Cited by: 0 articles Check for new citations

Abstract

For at least 40 years, the analysis of the causes and consequences of macroeconomic instability has greatly deepened our understanding of the handicaps faced by developing countries. This concern on economic instability is evidenced by a broad spectrum of indicators, based on the deviation of observed values of a given economic aggregate from its reference or trend value. In general, the choice of this or that indicator is not discussed advocating that the resulting instability indicators are closely correlated. Focusing on measurements of instability in export revenue data for 134 countries from 1970 to 2005, this paper finds that this assertion may be true for variance-based indicators, measuring the average magnitude of deviations from the trend. However, great discrepancies may arise between different measures of the asymmetry or of the occurrence of extreme deviations around the trend when different trend computation methods are used. Our purpose is, therefore, to invite further discussions regarding the use of these indicators, and to highlight the different dimensions of instability, which have been so far unheeded by the economic literature.

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1. Introduction

Global economic crises in the 20th century have made macroeconomic instability a key issue in the analyses of economic growth and development. Most empirical researches conclude that instability has a negative impact on growth (see Hnatkovska and Loayza, 2005; Koren and Tenreyro, 2007; Loayza et al., 2007. Indeed, instability reduces consumption (Aizenman and Pinto, 2005; Wolf, 2005), investment and factor productivity (Azeinman and Marion, 1999; Dehn, 2000), and deteriorates the quality of economic policies (Fatas and Mihov, 2007; Afonso and Furceri, 2010). Instability may, however, positively affect the return on investment, and growth, but only in good institutional context (Hnatkovska and Loayza, 2005; Imbs, 2007; Rancière et

al., 2008). Furthermore, if good institutions reduce instability (Acemoglu et al., 2003; Mobarak, 2005; Fatas and Mihov, 2006) or allow a better absorption of shocks (Rodrik, 2000), financial development has in contrast an ambiguous role in transmitting or attenuating instability (Beck et al., 2001; Aghion et al., 2004; Aghion et al., 2005). These channels explain why developing countries are more vulnerable to macroeconomic instability, by being more exposed to shocks and less able to absorb them (Loayza and Raddatz, 2007; Guillaumont, 2009a,b; Malik and Temple, 2009; di Giovanni and Levchenko, 2010).Instability is a complex and multidimensional phenomenon as witnessed by the large array of methods by which it is measured. Economic instability refers to the notion of economic disequilibrium, which is also used for instances in the analyses of output gap and exchange rate misalignment (see Egert et al., 2006): its measurement is then generally based on the extent to which observed values of an economic variable deviate transitorily from the trend or reference value. Therefore, measuring instability requires in a first stage to look carefully at the data and to choose the appropriate method of calculating the trend around which a series fluctuates. Measures of instability that are discussed in this paper must be distinguished from measures of uncertainty or risk aimed at reflecting unpredictable variations only (Wolf, 2005), and

which are based on conditional variance GARCH (Servén, 1998; Dehn, 2000; Dehn et al.,2005; Chua et al., 2011).The second stage consists in summing deviations from this trend. Traditionally, indicators of instability are confined to the average amplitude of deviations, such as the standard deviation. However, this masks other important dimensions of instability, such as the asymmetry of deviations (predominance of positive or negative shocks) or the occurrence of extreme deviations, which are expected to have specific consequences (Alderman, 1996; Dercon, 2002; Rancière et al., 2008).Usually there is little discussion about alternative methods for measuring instability, which are then indiscriminately applied to economic series with different patterns of evolution (see annex 1). Amongst available measures of instability, variance-based indicators are the most common ones. However, confining the analysis of instability to the analysis of a variable's variance may mask other important dimensions of economic instability. In fact, other important dimensions of economic instability are the asymmetry of fluctuations and the likelihood of crisis or booms, and are addressed in this paper.The most common measure of instability is the standard deviation of the

growth rate of a variable (see annex 1), which assumes, sometimes without any prior testing, that the variable is stationary in first difference. Other measures consist in calculating the standard deviation ofthe residuals of a

regression of the variable on a deterministic and/or stochastic trend (Servén, 1998; Pritchett, 2000; Lensink and Morrissey, 2006; Chauvet and Guillaumont, 2009). Alternatively, the reference value can be computed as a moving average (Dawe, 1996), aHodrick-Prescott filter (Becker and Mauro, 2006; Chauvet and Guillaumont, 2009), or aBaxter-King

filter (Hnatkovska and Loayza, 2005; Afonso and Furceri, 2010).The aim of this paper is therefore to discuss and apply popular techniques for the measurement of instability to export revenue data (in constant value), for a sample of 134 countries over the period 1970–2005. We focus on exports instability since it is a widely debated source of output fluctuations, with strong destabilizing effects on growth, tax, and redistribution policy (Bevan et al., 1993; Easterly et al., 1993; Guillaumont, 2009a, b). First, we present and compare suitable

parametric and non-parametric univariate approaches for calculating the trend. Then, on the basis of these trends, we compute three indicators of instability for the magnitude, the asymmetry and the occurrence of extreme deviations, respectively. Results show that (1) indicators of the magnitude of export instability are strongly correlated with each other; (2) correlations between indicators of asymmetry, and between indicators of occurrence of extreme deviations, are low; (3) whatever the trend calculation method, the average magnitude of instability is found to be unrelated to its asymmetry and to the occurrence of extreme deviations; while (4) the two latter are strongly associated.The next section discusses parametric and non-parametric approaches for calculating the trend component of export series. Based on these trend computation methods, the third section outlines the various ways of summing deviations from the trend, presents and compares indicators reflecting the average magnitude of economic fluctuations, their asymmetry and the occurrence of extreme variations.

2. Trend Computation Methods

Measures of instability typically rely on the extent to which observed values of time series deviate transitorily from their permanent (or long-run) state. However, when modelling the permanent state by the simple average of the observed values (a constant), most economic aggregates show deviations that are not temporary, that is, series are not stationary. As a consequence, it is then necessary to specify a more complex permanent state, like a trend, around which fluctuations are stationary.1

A preliminary diagnostic of the pattern of change in export series is very informative when trying to deduce an appropriate form of the trend. A useful tool is the spectrum density of a series which represents the contributions of each frequency of variation to the total variation of the series. Because low-medium (medium-high) frequency variations are likely to reflect the permanent (transitory) component, observing series’ spectrum would help to point what should be an appropriate form of the trend.

Figure 1 displays the evolution of exports in six countries, and their associated spectrum densities. A peak at a given frequency (or periodicity2) indicates that a significant proportion of the total variance in a series can be explained by the variations at this frequency.

Figure 1.

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Export Series and Spectrum Densities for South Korea, Argentina, Venezuela, Kenya, Ivory Coast and Burundi.

The Figure 1 suggests that export series spectrum is located at long periodicities of around 20 years (with a peak in density at a frequency of around 0.05), except for South Korea.3 Variations of 5 to 10-year periodicity (a frequency between 0.1 and 0.2) also represent an important part of total variability in exports for this sample of countries. The spectrum of export series points to the existence of density peaks at high-frequency variations (between 0.3 and 0.5), corresponding to periodicities of around 2–3 years, in all countries except South Korea. Thus, while export series are dominated by low-frequency variations, short or medium-run export movements are also visible for this sample of developing countries.In what follows, we compute four popular trends that differ in their ability to separate low-frequency (permanent) from high-frequency (transitory) variations in export revenue data. Two trends are computed following a standard parametric univariate approach, the two others with a univariate filter approach.

2.1 Parametric Approaches4

Univariate parametric approaches are commonly used to model deterministic and/or stochastic trends (Harvey, 1997; White and Granger, 2011).2.1.1 Export Fluctuations around a Linear Deterministic Trend

The traditional approach consists in using a deterministic trend, assuming that, if not tested, the variable is trend-stationary. Like many macroeconomic variables, export series are dominated by low-frequency variations which can be modelled by a deterministic trend (linear here, while a polynomial or exponential trend would be other options). This takes the form:

(1)

where yt is the observations of the economic variable, α a constant, t a linear trend, and εt a zero mean error term. The estimated trend or reference value is then   and the deviations are

With εt having no permanent effects on yt. This implies that (1) trend values increase at a constant rate, (2) the long-term change of the series is perfectly predictable and (3) all deviations from the trend are transitory.Beveridge and Nelson (1981) and Harvey (1997) highlighted the limitations of this approach. To illustrate this, Figure 2 shows the actual change in exports and their trend in Belize between 1980 and 2004, and in Argentina between 1970 and 2005 (residuals are reported in Figure 3). Although the trend in Belize seems linear, some deviations from the trend are somewhat longstanding. This is more obvious for Argentina, where the trend is clearly nonlinear. Assuming a linear trend would then lead to overstate instability as residuals may wrongly include a part of the non-constant trend.

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Linear Trends of Export Series in Belize and Argentina.

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Residuals of Linear Trends in Belize and Argentina.

2.1.2 Export Fluctuations around a Global Mixed Trend

Export variables may then fluctuate around a trend that varies over time. In this case, a reference value may include a stochastic trend, represented by the following first-order autoregressive AR(1) process (Harvey, 1997),(2)

which can be rewritten as:

Changes in yt are determined by a successive history of random shocks: a shock occurring in the distant past has the same effect than a current shock. The trend component then follows a random walk process. A time series with such a variation pattern exhibits a unit root process and is said difference-stationary:(3)

This hypothesis underpinned indicators of instability based on the standard deviation in the series’ growth rate, a (too) strong assumption given the often observed low-frequency variations in macroeconomic variables like GDP, exports or public spending (Nelson and Kang, 1981). White and Granger (2011) reconcile the two approaches by

stressing that trends commonly observed in macroeconomic variables display both stochastic and deterministic movements. This takes the form:(4)

Using 134 country exports series, we perform the Maddala-Wu panel unit root test on εt (p-value are reported in Table 1). We also compute Fisher statistics for the joint null hypothesis onα, β and δ (Table 1, second column). Maddala-Wu unit root test suggests that the series contain a unit root, and F-test statistics reject the joint null hypothesis on coefficients. Modelling export series by equation (4) hence seems an appropriate approach, even if the above tests have low power in cases of near unit-root, structural breaks, or limited time-length (Sarris, 2000).Table 1. Specification and Unit Root Test on Panel Data

H0: The Series Is Non-Stationary Prob > Chi2 F-test

1. Countries (Observations): 134(3693).

1.000 47.47

0.000 36.61

As illustrated in Figure 4 and annex 2 this method produces a trend that is a slightly smoothed version of the observed change in real export values. Correlograms of residuals for Belize and Argentina in Figure 4 suggest that deviations from the trend are transitory, since residuals are not significantly autocorrelated (for other country examples, see annex 2). However, this trend seems to reproduce in t the change in exports observed in t − 1, which is not consistent with the conclusion of White and Granger (2011), who stress that one desirable

property of trend values is to be pointedly smoother than the gross series. We address this issue in the next sub-section.

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Export Revenue, Global Mixed Trend and the Correlogram of Residuals in Belize and Argentina.

2.1.3 Estimate Based on a Rolling Mixed Trend

The above mixed trend is ‘global’, in the sense that estimated coefficients of equation (4) are assumed to be constant over the whole period of data availability. It therefore excludes the possibility for regime changes in the deterministic and stochastic components of the trend. Although tests of structural breaks in time series do exist (for

example, CUSUM, Max Chow tests), an alternative and more practical solution with panel dataset may consist in estimating a mixed trend on a ‘rolling’ basis (Guillaumont, 2007), allowing the estimated coefficients to change from year to year, thus reflecting recent changes in the trend regime. This ‘rolling’ mixed trend is estimated each year for each country over the period T = [t − k; t], rather than the whole period of data availability:(5)

Figure 5 plots the estimated ‘rolling’ trend when k = 12 against the ‘global’ mixed trend in Argentina (see annex 2 for more country results). The ‘rolling’ mixed trend is visibly smoother and does not exhibit a ‘saw-tooth’ profile that results from time-constant parameters in the case of global trend. Similarly, correlograms in Figures 5 and 6, and in annex 2, suggest that the resulting residuals are stationary, for this sample of countries.

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Export Revenue, Global and Rolling Mixed Trends, and the Correlogram of Residuals in Argentina.

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Export Revenue, Global and Rolling Mixed Trends, and Correlograms of Residuals in Burundi.

Nonetheless, this technique presents some drawbacks. First, it reduces the time coverage of instability indicators, since the first trend value is only available from t = 1 + k. Second, estimates based on a rolling mixed trend do not fully account for a structural break in the trend, since the prior trend regime may still exhibit inertia after the break. Moreover, by estimating the trend over a shorter period than the global mixed trend, it is more sensitive to medium-periodicity than to long-periodicity fluctuations, with the risk of including the latter in the residuals. This point is illustrated by the behaviour of the global and rolling mixed trends in exports from Burundi between 1985 and 1995, in Figure 6. In this example, the rolling trend tends to underestimate the trend downfall after 1985 relative to the global mixed trend.Choosing an appropriate timeframe for the rolling trend is then a critical step because of its serious implications. Instead of a 12-years estimation period chosen for the sake of methods’ comparison, a rolling trend calculated over a period of 15 or 20 years would be a reasonable basis. As suggested by spectrum densities in Figure 1, variations of such periodicity strongly contribute to the overall variation of the series. Thus, the estimation period is an arbitrary choice intending to reflect at best the evolution of a series for a sample of countries. Such a choice

possibly induces misspecification problems when looking at particular countries’ export series and trends.

2.2 The Filter Approach

Statistical filters are used to isolate the deviations as a cyclical component by removing the trend components of series. The standard deviation of the resulting cycle is then a popular measure of economic instability (Hnatkovska and Loayza, 2005; Becker and Mauro, 2006; Chauvet and Guillaumont, 2009). Unlike the parametric approach, the filter approach does not require a priori assumptions on the form of the trend and is sensitive to structural breaks. The Hodrick-Prescott (HP) filter (Hodrick-Prescott, 1997) is amongst the most popular (another example is the Band Pass filter of Baxter and King, 1999). The filter breaks down the change in a series into a trend component (yP

t), and a cyclical component (yC

t):(6)

The HP filter isolates the cyclical component by optimising the following programme:

(7)

giving the deviation  . This method is close to a symmetrical moving average filter with an infinite time horizon. λ is a smoothing parameter which can be either estimated or determined ad hoc. The first term of equation (7) minimises the variance in the cyclical component whilst the second term smoothes the change in the trend component. When λ tends to infinity, the variance in the growth of the trend component converges to zero, which implies that the trend component – or the filtered series – is close to a simple linear trend. Conversely, when λ tends to zero, the filtered series is close to the original series. Consequently, the lower the value of λ, the shorter the periodicity of isolated cyclical fluctuations is. The choice of the value of λ is still debated in the literature. While Hodrick and Prescott (1997) advocate a parameter λ equal to 100 for annual data, Baxter and King (1999)

suggest a value up to 400, while Maravall and Del Rio (2001) choose a value down to 6.We, hereafter, compare results obtained with λ set at 100 (long-periodicity trend, or HP100) and 6.5 (medium-periodicity trend, or HP65). As expected, Figure 7 shows that the HP65 trend fluctuates more than the HP100 trend. Annex 3 reports correlograms of the isolated cycles for these countries. These results suggest that extracted cycles are stationary and not significantly autocorrelated, for this sample of countries.

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Export Revenue Data Smoothed by the HP Filter.

The filtering approach nonetheless presents some drawbacks compared to the other approaches. First, it relies on the hypothesis that the cyclical

and the trend components are independent, which seems rather restrictive (see Ramey and Ramey, 1995 for the impact of macroeconomic fluctuations on long-term growth). Moreover, the HP filter may suffer fromcompression effects when the smoothing parameter is set at a low value: part of short-periodicity cyclical variations can wrongly be attributed to trend variations. The resulting cyclical component – upon which instability measures are based – may be understated. Conversely, choosing a high-smoothing parameter may cause leakage effects: some of the long-periodicity variations may be included into the cyclical component, and instability may be overstated. In our case, the contribution of medium-periodicity fluctuations in the total variance, seen in Figure 1, suggest the choice of a lower smoothing parameter (it would also be relevant for samples of developing countries with shorter business cycles and more variable trend components, see Rand and Tarp, 2002; Aguiar and Gopinath, 2007).

2.3 Interim Discussion

Parametric and filter approaches of computing the trend are conceptually different. While the former models the process followed by trends on the basis of the past evolution of the data, two-sided filter methods use both past and future data, logically affecting measurements of instability.

On the one hand, the filter approach is probably more efficient in isolating temporary deviations by being more sensitive to regime changes, and is more flexible when series exhibit very diverging patterns between countries. On the other hand, because trend values are predicted according to past data, instability indicators based on the parametric approach better reflects the impact of unusual economic events such as economic crisis or booms, which is a key feature for the study of the consequences of instability on economic outcomes.

Conversely, the two-sided filter approach using both past and future data makes the trend to be more sensitive to outliers and may hence produce deviations that are less sensitive to extreme events. This approach was

originally used to identify business cycles with the underlying assumption of economic agents form rational expectations. By contrast, parametric approaches are popular to analyse the impacts of shocks on economic agents’ decisions that follow extrapolative or adaptive expectations.

As a final word, both approaches imply arbitrary choices, regarding the estimation period of the trend in the parametric approach, and the value of the smoothing parameter in the filter approach. These choices should at best reflect broad trend movements in economic time series, but inevitably induce misspecification problems when applied to wide panel datasets (this problem is discussed in another context by Egert et

al., 2006).

3. Quantifying Instability: Ways of Summing Deviations from the Trend

Variance-based indicators are the most common measures of instability. However, they mask other important dimensions of instability like the asymmetry of deviations and the occurrence of extreme deviations, which can be quantified by exploiting moments two, three and four of the series. Annex 4 plots the comparative evolution of export deviations from trends values in Argentina, Belize, Burundi, Ivory Coast, South Korea and Venezuela.

3.1 Calculation Period

Because indicators of instability are intended to reflect a past history of shocks, it is first necessary to set the period over which deviations are summed.5 Wolf (2005) suggests aligning the period to the approximate duration of the episodes of instability observed in the series, if any.

3.2 The Magnitude of Export Fluctuations

The standard deviation is the most common indicator to estimate the ‘average deviation’ from the reference (trend) value:

(8)

with T = 1, …, t.   is the observed value, and   is the reference value.We compute indicators of the average magnitude of export instability over the period 1982–2005, based on the standard deviation, for each reference values. Results are shown in Tables 2and 3, and Figure 8. Standard deviations of export around mixed trends and HP-filtered values display high correlations exceeding 0.8, with the HP100 filter standard deviation being the less correlated with the others. Standard deviations around the global mixed trend and the HP100 filter tend to be higher than the others in average, while standard deviations around the HP65 filtered values are lower in average than the others. Thus, the choice of the reference value does not seem to have serious implications for the measurement of the average magnitude of export instability.

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Graphical Illustrations of Correlations between Standard Deviations of Exports around Different Trend Values.

Table 2. Correlations between Standard Deviations of Exports around Different Trend Values

  (1) (2) (3) (4)

 Global mixed trend

Rolling mixed trend HP6.5 HP100

1. *Significant at 5%. Observations: 134. Standard deviations calculated over the period 1982–2005.

(1) 1.00      

(2) 0.92* 1.00    

(3) 0.96* 0.95* 1.00  

(4) 0.87* 0.80* 0.87* 1.00

Table 3. Descriptive Statistics of Standard Deviations of Exports around Different Trend Values

 Global Mixed trend

Rolling Mixed trend

HP6.5 HP100

1. Observations: 134. Standard deviations calculated over the period 1982–2005

Mean 13.6 11.5 9.2 13.3

 Global Mixed trend

Rolling Mixed trend

HP6.5 HP100

Standard deviation 8.7 7.8 6.1 7.8

3.3 The Asymmetry of Export Fluctuations

Variance-based indicators of instability do not reflect the asymmetry of shocks, a very important dimension of instability given the expected asymmetry in agents’ responses to adverse and favourable shocks (Dercon, 2002; Wolf, 2005; Elbers et al., 2007). In this regard, the coefficient of asymmetry in a series – or skewness – is an indicator of the predominance of adverse or favourable shocks, which can be calculated as follow (in% of the trend):(9)

with T = 1, …, t. A symmetrical distribution displays a coefficient of skewness close to 0%, while a positive (negative) skewness indicates that instability is dominated by positive (negative) shocks. Figure 9 is a graphical illustration of a positively skewed, a centred, and a negatively skewed distribution of export in Argentina, Algeria and Mexico, respectively. We can observe that a positive (negative) skewness increases with the size or the frequency of positive (negative) shocks.

Figure 9.

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Kernel Densities of the Distribution of Exports around a 12-year Rolling Mixed Trend and Its Corresponding Moments in Argentina, Algeria and Mexico.

Table 4 and annex 5 suggest that the coefficients of skewness display important discrepancies when reference values differ, except between HP-based measures. Consequently, the choice of the reference value is likely to influence the generated ‘picture’ of shocks.Table 4. Correlations between Coefficients of Skewness Calculated over the Period 1982–2005

  (1) (2) (3) (4)

 Global Mixed Trend

Rolling Mixed Trend

HP6.5 HP100

1. *Significant at 10%. Sample = 134 countries. Skewness calculated over the period 1982–2005.

Skewness calculated over the period 1982–2005

(1) 1      

(2) 0.23* 1    

(3) 0.08* 0.14* 1  

(4) 0.29* 0.02 0.65* 1

Moreover, for a similar magnitude of instability, asymmetry may strongly differ. Figure 10illustrates the fairly weak correlation between the standard deviations and the skewness of exports for each of the four reference value, particularly once some outliers are excluded.

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Graphical Illustrations of Correlations between the Standard Deviation and the Skewness of Exports, by Trend Value.

3.4 The Occurrence of Extreme Shocks in Exports

A third dimension of instability is the occurrence of extreme, but infrequent, deviations in a series, measured by the fourth moment of the distribution around the reference value, or the kurtosis, computed as follows (as a percentage of the reference value):

(10)

The kurtosis is a measure of the relative peakedness or tails’ fatness of a statistical distribution. A low value indicates that the distribution tends to be uniformly distributed around the mean, while a high value indicates a distribution pointed around the mean with thick tails. Figure 11illustrates three types of flattening of distributions of exports around their trend – platykurtic (kurtosis <300%), mesokurtic (kurtosis = 300%), and leptokurtic (kurtosis >300%).

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Graphical Illustrations of Platykurtic, Mesokurtic, and Leptokurtic Distributions of Exports around a Rolling Mixed Trend.

As underlined by Rancière et al. (2008), a high value of kurtosis should be interpreted with caution since it may result from either extreme variations or a cluster of observations around the mean. Table 5 shows descriptive statistics for kurtosis-based indicators of instability for exports series. Distributions around reference values are slightly leptokurtic on average (higher than 300%). Distributions around the HP filter display a similar degree of flattening, which is lower than those displayed by distributions around mixed trends.Table 5. Descriptive Statistics of Coefficients of Kurtosis Calculated over 1982–2005

 Global Mixed Trend

Rolling Mixed Trend

HP6.5 HP100

1. Total sample: 134 countries. Kurtosis calculated over the period 1982–2005.

Mean (%) 352.7 367.9 320.0 312.2

Standard deviation 155.7 173.0 139.0 146.2

Table 6 and annex 6 report correlations between kurtoses based on the four reference values. These correlations are slightly stronger than the correlations between asymmetry coefficients, the calculated occurrence of infrequent large-scale positive or negative deviations being logically less influenced by the choice of the reference value. However, correlations between rolling mixed trend-based and HP-based kurtoses are weak, suggesting again that the different trend computation methods generate different histories of shocks, even in the case of sharp and infrequent shocks.Table 6. Correlations between Coefficients of Kurtosis Calculated over 1982–2005

  (1) (2) (3) (4)

 Global Mixed Trend

Rolling Mixed Trend

HP6.5 HP100

1. *Significant at 5%. Sample = 134 countries. Kurtosis calculated over the period 1982–2005.

(1) 1      

  (1) (2) (3) (4)

 Global Mixed Trend

Rolling Mixed Trend

HP6.5 HP100

(2) 0.39* 1    

(3) 0.38* 0.28* 1  

(4) 0.49* 0.22* 0.62* 1

As suggested by Figure 12, whatever the reference values, the average magnitude of deviations seems quite unrelated to the occurrence of extreme ones. In Figure 13, a U-shaped relationship can be observed between asymmetry and kurtosis, with a turning point located close to a null skewness. Table 7 shows a strong positive correlation between these two indicators when the sample is limited to countries with a positive asymmetry, and a strong negative correlation for countries with a negative asymmetry. Moreover, it appears that high values of kurtosis are often associated with high positive values of skewness. For an asymmetry between 0 and 100% in absolute value, these two dimensions of instability are however less related. Therefore, a low asymmetry would reflect deviations characterized by a smaller magnitude and a higher frequency, while a high value would reflect large-magnitude low-frequency deviations.

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Graphical Illustrations of the Correlation between the Standard Deviation and the Kurtosis of Export Distributions, by Trend Value.

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Graphical Illustrations of the Correlation between the Skewness and the Kurtosis of Export Distributions, by Trend Value.

Table 7. Correlations between the Skewness and the Kurtosis of Export Distributions, by Trend Value, 1982–2005

  Global Mixed Rolling Mixed    

  Trend Trend HP6.5 HP100

1. *Significant at 5%. Sample = 134 countries. Instability indicators calculated over the period 1982–2005.

Total sample +0.65* +0.51* +0.12* +0.35*

  Global Mixed Rolling Mixed    

  Trend Trend HP6.5 HP100

Positive asymmetry skewness > 0%

+0.85* +0.83* +0.84* +0.91*

Negative asymmetry skewness < 0%

−0.58* −0.48* −0.70* −0.45*

Weak asymmetry skewness = [−100%; 100%]

+0.24* +0.16* +0.33* +0.35*

4. Conclusion

The literature on macroeconomic instability is extensive and uses a wide range of instability indicators. Approaches of measuring instability vary according to the choice of the reference value and the way deviations around it are summed. Moreover, beyond the standard measure of the average magnitude of economic instability, the asymmetry of fluctuations and the occurrence of extreme shocks are separate dimensions, largely overlooked by the applied literature. We have used exports data on 134 countries to illustrate these points, following three main steps: (1) the examination of export series’ patterns of evolution, (2) the computation of the trend component of export series, and (3) the computation of instability indicators based on the moments of the distribution of exports around their trend.

We applied four popular trend calculation methods. The parametric approach allows us selecting an appropriate form of the trend, a mixed

trend including a deterministic and a stochastic component, which we declined in two versions: a ‘global’ mixed trend and a ‘rolling’ mixed trend. In our view, when data coverage is long enough, a ‘rolling’ trend should be preferred, as estimated coefficients better account for structural breaks. The filter approach is also declined in two versions, with a low- or a high-smoothing parameter. This approach does not require an a priori choice of the form of the trend, which is also more sensitive to structural breaks than parametric approaches.Each trend computation method is not exempt from misspecification problems. The trend estimation period or smoothing parameter value must be chosen in accordance with the typical trend pattern in the country sample. As developing countries compose a significant part of our sample, a rolling trend estimated over a 15–20 year period or a smoothing parameter set between 6 and 10 seemed appropriate initially, since the trend component in these countries is less stable than in developed countries.

Three types of indicators of exports instability are computed using the four forms of the trend: the magnitude of fluctuations (measured by the standard deviation from the trend), asymmetry (skewness) and the occurrence of extreme shocks (kurtosis). First, results show that the indicators of magnitude are strongly correlated. Second, in contrast, the indicators of asymmetry are weakly correlated, as are the indicators of the occurrence of extreme deviations. Third, whatever the form of the trend, the average magnitude of instability seems unrelated to asymmetry and to the occurrence of extreme deviations. And fourth, inversely, asymmetry is strongly associated to the occurrence of extreme events. Thus, while low absolute values of skewness reflects the occurrence of asymmetric, frequent but small fluctuations, high absolute values of skewness reflect the occurrence of asymmetric, infrequent but large fluctuations. As stressed by Alderman (1996) and Dercon (2002) besides asymmetry, the frequency and intensity of economic fluctuations clearly shape economic agents’ responses to them.6 Then, using skewness-based measure along with variance-based of instability should therefore allow researchers to go further in the study of the consequence

of economic instability on economic behaviours and economic outcomes (Cariolle, 2012; Rancière et al., 2008).

Acknowledgements

Authors are grateful to Patrick Guillaumont, Jean-Louis Combes, Laurent Wagner and an anonymous referee for helpful comments and suggestions. This paper benefited from the support of the FERDI (Fondation pour les études et recherches sur le développement international) and of the Programme d'investissements d'avenir of the French government.

Notes

1. 1If the series is poorly stationarized, variations which are attributable to a long-term (or permanent) change may be included in the residual.

2. 2The calculation for switching from frequency (F) to periodicity (T) is as follows: T = 1/F.

3. 3Like many western countries, South Korea's export series exhibits a ‘Granger profile’ (Granger,  1966), with most of the power of the spectrum located at zero frequency. This pattern suggests that a strictly decreasing or increasing trend in exports represent the principal source of total export variance.

4. 4Along this section, we estimate trend values using data in logarithm, and then rescale them using an exponential transformation.

5. 5This period does not necessarily correspond to the period chosen for trend estimation.

6. 6As Dercon (2002, p. 2) points out, ‘other characteristics of income risk include the frequency and intensity of shocks, and the persistence of their impact (…). Relatively small but frequent shocks are more easily to deal than large, infrequent negative shocks’.

Annexes

Table Annex 1. Overview of Indicators of Instability and Their Applications in the Literature

Indicators Authors to ReflectVariables Concerned (yt)

Growth rate/first difference      

Indicators Authors to ReflectVariables Concerned (yt)

Standard deviation or coefficient of variation of a variable's growth rate Servén (1997), Acemoglu et al. (2003), Mobarak (2005), Koren and Tenreyro (2007), Raddatz (2007), di Giovanni and Levchenko (2010), Malik and Temple (2009), Ploeg and Poelhekke (2009)

Variability GDP/inhabitant, inflation, terms of trade, actual exchange rate, black-market premium, international interest rate (LIBOR), development aid/inhabitant, public spending, ratio of wheat cultivation yields to national yield

5-year variance of a variable's growth rate Koren and Tenreyro (2007)

Variability Annual growth rate of work productivity

Filters      

Decline in GDP: decrease of more than 1% of the (annual) series log smoothed by the HP filter (lambda = 1000)

Becker and Mauro (2006)

Variability GDP

Standard deviation of the cyclical component, i.e. the standard deviation of the difference between series smoothed by the HP or BK filter and actual series.

Hnatkovska and Loayza (2005), Chauvet and

VariabilityVariability

Aid, export revenues, GDPGDP, budget variables (transfers,

Indicators Authors to ReflectVariables Concerned (yt)

Guillaumont (2009), Afonso and Furceri (2010)

subsidies, public spending, tax revenues, etc.)

Average over five years of the ratio of the absolute deviation between the observed value of export revenues (X) and the value filtered using the ratio of export five-year moving average process over GDP (Y):

Dawe (1996)

Variability Export revenues

     

Forecasts/Estimates      

Ramey and Ramey (1995)

Uncertainty Growth rate of GDP

The standard deviation of the residual , is seen as a measure of volatility reflecting uncertainty. This approach is adopted for the whole of the sample and for all countries, to produce an estimate of coefficients specific to each country.

     

The standard deviation of the residual εt, obtained using a regression of y t on a

linear trend:

Pritchett (2000), Mobarak

Variability Growth rate of GDP per inhabitant,

Indicators Authors to ReflectVariables Concerned (yt)

(2005) growth rate of capital per worker

Rolling standard deviation or average absolute deviation of the residual εt obtained based on a regression of y t on a rolling mixed trend,

Guillaumont (2007), Servén (1998)

Variability/ uncertainty  

Development aid, exports, terms of trade, inflation rate, relative price of capital, actual exchange rate, growth rate of GDP.

Standard deviation of the error in a regression of FDI over three lags and a temporal trend:

Lensink and Morrissey (2006)

Uncertainty FDI/GDP, FDI

They estimate volatility measures for each country based on the following GARCH (1,1) model:

where t = 1…,T, and D the vector of mute quarterly variables. By imposing the following constraint on conditional variance

The estimated value ofrepresents the uncertainty ofyit.  

Dehn (2000), Servén (1998)

Uncertainty Prices of raw materials, inflation rate, price of capital relative to actual exchange rate

http://onlinelibrary.wiley.com/enhanced/figures/doi/10.1111/joes.12036#figure-viewer-joes12036-fig-0014

Annex 2. Open in figure viewer Download Powerpoint slide

Mixed Trends and Correlograms of Residuals.

Annex 3. Open in figure viewer Download Powerpoint slide

Correlograms of Export Revenue Cycles Smoothed by the HP Filter.

Annex 4. Open in figure viewer Download Powerpoint slide

Comparative Evolution of Deviations (Residuals or Cycles), in % of Trend Values.

Annex 5. Open in figure viewer Download Powerpoint slide

Graphical Illustration of Correlations between Coefficients of Skewness of Export Distributions Around Different Trend Values.

Annex 6. Open in figure viewer Download Powerpoint slide

Graphical Illustration of Correlations between Coefficients of Kurtosis of Export Distributions Around Different Trend Values.

Ancillary

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Types of Inflation: 4 Different Types Plus MoreInflation is when the prices of goods and services increase. There are four main types of inflation, categorized by their speed: creeping, walking, galloping, and hyperinflation. There are also many types of asset inflation and, of course, wage inflation. Many experts consider demand-pull and cost-push to be types of inflation, but they are actually causes of inflation, as is expansion of the money supply.

People know that next year's car model will probably cost more. (Photo: Bill Pugliano/Getty Images)

1.  Creeping Inflation Creeping or mild inflation is when prices rise 3% a year or less. According to the U.S. Federal Reserve, when prices

rise 2% or less, it's actually beneficial to economic growth. That's because this mild inflation sets expectations that prices will continue to rise. As a result, it sparks increased demand as consumers decide to buy now before prices rise in the future. By increasing demand, mild inflationdrives economic expansion.

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Health care costs rise faster than 3% a year. (Photo: Jason Greenspan/Getty Images)

2.  Walking Inflation This type of strong, or pernicious, inflation is between 3-10% a year. It is harmful to the economy because it heats

upeconomic growth too fast. People start to buy more than they need, just to avoid tomorrow's much higher prices. This drives demand even further, so that suppliers can't keep up. More important, neither can wages. As a result,  common goods and services are priced out of the reach of most people.

Galloping inflation occurred during WWII. (Photo: U.S. National Archives and Records Administration)

3.  Galloping Inflation When inflation rises to ten percent or greater, it wreaks absolute havoc on the economy. Money loses value so fast that

business and employee income can't keep up with costs and prices. Foreign investors avoid the country, depriving it of needed capital. The economy becomes unstable, and government leaders lose credibility. Galloping inflation must be prevented.

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Hans and Greta playing with worthless marks during the Weimar Republic. Photo: Albert Harlingue/Getty Images

4.   Hyperinflation Hyperinflation is when the prices skyrocket more than 50% -- a month. It is fortunately very rare. In fact, most

examples of hyperinflation have occurred when the government printed money recklessly to pay for war. Examples of hyperinflation include Germany in the 1920s, Zimbabwe in the 2000s, and during the American Civil War. More »

Federal Reserve Chairman Paul Volcker ended stagflation (Photo: Win McNamee/Getty Images)

5.   Stagflation Stagflation  is just like its name says: when economic growth is stagnant, but there still is price inflation. This seems

contradictory, if not impossible. Why would prices go up when there isn't enough demand to stoke economic growth? It happened in the 1970s when the U.S. went off the gold standard. Once the dollar's value was no longer tied to gold, the number of dollars in circulation skyrocketed. This increase in the money supply was one of the causes of inflation. Stagflation didn't end until then-Federal Reserve Chairman Paul Volcker raised the Fed funds rate to the double-digits -- and kept it there long enough to dispel expectations of further inflation. Because it was such an unusual situation, it probably won't happen again.More »

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Food prices are not included in the core inflation rate. Photo: Monashee Frantz/Getty Images

6.   Core Inflation The core inflation rate measures rising prices in everything except food and energy. That's because gas prices tend to

escalate every summer, usually driving up the price of food and often anything else that has large transportation costs. TheFederal Reserve uses the core inflation rate to guide it in setting monetary policy. The Fed doesn't want to adjust interest rates every time gas prices go up -- and you wouldn't want it to. More »

Deflation in housing prices trapped many homeowners.(Photo: Peter Dazeley/Getty Images)

7.   Deflation Deflation is the opposite of inflation  -- it's when prices fall. It's caused when an asset bubble bursts. That's what

happened in housing in 2006. Deflation in housing prices trapped those who bought their homes in 2005. In fact, the Fed was worried about overall deflation during the recession. That's because deflation can turn a recession into a depression. During the Great Depression of 1929, prices dropped 10% -- a year. Once deflation starts, it is harder to stop than inflation. More »

Most U.S. workers have not experienced wage inflation. (Photo: Getty Images)

8.  Wage Inflation Wage inflation is when workers' pay rises faster than the cost of living. This occurs when there is a shortage of

workers, when labor unions negotiate ever-higher wages, or when workers effectively control their own pay. A worker shortage occurs whenever unemployment is below 4%. Labor unions negotiated higher pay for auto workers in the 90s. CEOs effectively control their own pay by sitting on many corporate boards, especially their own. All of these situations created wage inflation. Of course, everyone thinks their wage increases are justified. However, higher wages are one element of cost-push inflation, and can cause prices of the company's goods and services to rise.

In 2005, there was an asset bubble in housing. (Photo: Justin Sullivan/Getty Images)

9.   Asset Inflation An asset bubble, or asset inflation, occurs in one asset class, such as housing, oil orgold. It is often overlooked by

the Federal Reserve and other inflation-watchers when the overall rate of inflation is low. However, as we saw in the subprime mortgage crisis and subsequent global financial crisis, asset inflation can be very damaging if left unchecked. More »

Inflation in gas prices affect people dramatically. (Photo: Mark Renders/Getty Images)

10.   Asset Inflation -- Gas Gas prices rise each spring in anticipation of the summertime vacation driving season. In fact, you can expect  gas

pricesto rise ten cents per gallon each spring. However, political uncertainty in the oil-exporting countries drove gas prices higher in 2011 and 2012. Prices hit an all-time peak of $4.17 in July 2008, thanks to economic uncertainty. For more on that, see Gas Prices in 2008.

  What do oil prices have to do with gas prices? A lot. In fact, oil prices are responsible for 72% of gas prices. The rest is

distribution and taxes, which aren't as volatile as oil prices. For more, see How Do Crude Oil Prices Affect Gas Prices? More »

Oil price inflation affects many other asset classes. (Photo: David McNew/Getty Images).

11.   Asset Inflation -- Oil Crude oil prices  hit an all-time high of $143.68 a barrel in July 2008. This was in spite of a decrease in

global demand and an increase in supply. Oil prices are determined by commodities traders, both speculators and corporate traders hedging their risks. Traders will bid up oil prices if they think there are threats to supply, such as unrest in the Middle East, or an uptick in demand, such as growth in China. More »

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Food price inflation can cause food riots. (Photo: Elly Lange/ Getty Images).

12.   Asset Inflation -- Food Food prices soared 6.8% in 2008, causing food riots in India and other emerging markets. They spiked again in 2011,

rising 4.8% and leading to the Arab Spring, according to many economists. Food riots caused by inflation in this important asset class could continue to reoccur. More »

Inflation in gold prices occurred in 2011.(Photo: David McNew/Getty Images)

13.   Asset Inflation -- Gold An asset bubble occurred when gold prices hit the all-time high of $1,895 an ounce on September 5, 2011. Although

many investors might not call this inflation, it sure was. That's because prices rose without a corresponding shift in gold's supply or demand. Instead, investors drove up gold prices as a safe haven. They were concerned about the declining dollar, hyperinflation in U.S. goods and services, and uncertainty about global stability. What spooked investors? In August, thejobs report showed absolutely zero new jobs gains. During the summer, the eurozone debt crisis looked like it might not get resolved and there was stress about whether the U.S. would  default on its debt. Gold prices go up in response to uncertainty, whether it's tohedge against inflation or its exact opposite, the resurgence of recession. Article updated May 6, 2013 More »

Different Types of Inflation Tejvan Pettinger December 4, 2013  inflation

Inflation means a sustained increase in the general price level. However, this increase in the

cost of living can be caused by different factors. The main two types of inflation are

1. Demand pull inflation – this occurs when the economy grows quickly and starts to ‘overheat’ –

Aggregate demand (AD) will be increasing  faster than aggregate supply (LRAS).

2. Cost push inflation – this occurs when there is a rise in the price of raw materials, higher taxes,

e.t.c

1. Demand Pull Inflation

This occurs when AD increases at a faster rate than AS. Demand pull inflation will typically

occur when the economy is growing faster than the long run trend rate of growth. If demand

exceeds supply, firms will respond by pushing up prices.

Simple diagram showing demand pull inflation

The UK experienced demand pull inflation during the Lawson boom of the late 1980s. Fuelled

by rising house prices, high consumer confidence and tax cuts, the economy was growing by

5% a year, but this caused supply bottlenecks and firms responded by increasing  prices.

This graph shows inflation and economic growth in the UK during the 1980s. High growth in

1987, 1988 of 4-5% caused an increase in the inflation rate. It was only when the economy went

into recession in 1990 and 1991, that we saw a fall in the inflation rate.

2. Cost Push Inflation

This occurs when there is an increase in the cost of production for firms causing aggregate

supply to shift to the left. Cost push inflation could be caused by rising energy and commodity

prices. See:Cost Push inflation

Simple Diagram showing cost push inflation.

Example of Cost push inflation in the UK

In early 2008, the UK economy entered a deep recession(GDP fell 6%). However, at the same

time, we experienced a rise in inflation. This inflation was definitely not due to demand side

factors; it was due to cost push factors, such as rising oil prices, rising taxes and rising import

prices (as a result of depreciation in  the Pound) By 2013, cost push factors had mostly

disappeared and inflation had fallen back to its target of 2%.

Sometimes cost push inflation is known as the ‘wrong type of inflation‘ because this inflation is

associated with falling living standards. It is hard for the Central Bank to deal with cost push

inflation because they face both inflation and falling output.

3. Wage Push Inflation

Rising wages tend to cause inflation. In effect this is a combination of demand pull and cost

push inflation. Rising wages increase cost for firms and so these are passed onto consumers in

the form of higher prices. Also rising wages give consumers greater disposable income and

therefore cause increased consumption and AD. In the 1970s, trades unions were powerful in

the UK. This helped cause rising nominal wages; this was a significant factor in causing

inflation.

4. Imported Inflation.

A depreciation in the exchange rate will make imports more expensive. Therefore, the prices will

increase solely due to this exchange rate effect. A depreciation will also make exports more

competitive so will increase demand.

5. Temporary Factors.

The inflation rate can also increase due to temporary factors such as increasing indirect taxes. If

you increase VAT rate from 17.5% to 20%, all goods which are VAT applicable will be 2.5%

more expensive. However, this price rise will only last a year. It is not a permanent effect.

Core Inflation

One measure of inflation, is known as ‘core inflation‘ This is the inflation rate that excludes

temporary ‘volatile’ factors, such as energy and food prices. The graph below shows inflation in

the EU. The headline inflation rate (HICP) is more volatile rising to 4% in 2008, and then falling

to -0.5% in 2009. However, the core inflation (HCIP – energy, food, alcohol and tobacco) is

more constant.

Example of Inflation in UK

This shows that energy prices were very volatile in this period, contributing to cost push inflation

in 2008.

Different measures of inflation

There are different measures of inflation. RPI includes mortgage interest payments. In 2009,

interest rates were cut, therefore, RPI measure of inflation became negative. CPI excludes the

effect of mortgage interest payments. The ONS now produce a statistic CPIH, which is CPI –

owner occupier costs.