Nowcasting China Real GDP - Cirano · Nowcasting China Real GDP Domenico Giannoney Silvia Miranda...
Transcript of Nowcasting China Real GDP - Cirano · Nowcasting China Real GDP Domenico Giannoney Silvia Miranda...
Nowcasting China Real GDP
Domenico Giannone† Silvia Miranda Agrippino‡
Michele Modugno[
†ULB & CEPR‡NowCasting Economics[Federal Reserve Board
CIRANO - 11/10/2013
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Motivation & Intro
Forecast-Nowcast-Backcast: Q3-2013
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Motivation & Intro
Forecast Revision: Oct 1st, 2013
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Motivation & Intro
Why China?
relevance:
China is world’s second largest economy: fundamental internationalplayer
most of the available forecasts are annual and typically released twice ayear
production driven: commodities
challenges:
data quality: doubts and skepticism raised by scholars andcommentators
data types: no SA at source, no key release (IP index)
data dissemination & release schedule: Lunar Calendar
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Motivation & Intro
Other Contributions
maintained and regularly published diffusion indexes and indicators:
China Economic Monitoring and Analysis Centre (CEMAC-NBS) &Goldman Sachs: CNCILI, CNCICI, CNCILAI
OECD: Composite Leading Indicator
The Conference Board: Leading Indicator
research papers:
Maier (2011) [Bank of Canada Working Paper]
Yiu & Chow (2010) [China Economic Journal]
Curran & Funke (2006), Mehrotra & Paakkonen (2011) [Bank of Finland
(BOFIT) Working Paper]
Liu, Zhang & Shek (2007) [Hong Kong Monetary Authority (HKMA) Working Paper]
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Motivation & Intro
Our Contribution
use indicators released at higher frequency within a unified frameworkwhich handles efficiently all the characteristic of Chinese data:transformations, availability, dissemination, types
forecast accuracy of yoy figures comparable to the best availablesurveys of professional forecasters: financial market watchers andparticipants
forecast of annual figures ”connects the dots” of main institutionalbenchmarks but accuracy further improves
forecast is updated at very high (irregularly spaced) frequencywhenever relevant info is released
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Motivation & Intro
Mimicking the Market: Data Flow
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Motivation & Intro
Mimicking the Market: Interpreting the News
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Choice of Relevant Information
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Data types & dissemination
Chinese GDP: official release
GDP figures are compiled by the Chinese National Bureau ofStatistics (NBS) and released around 20 days after the end of thereference quarter
according to this schedule the NBS releases:
NSA YTD levels in billion CNY
NSA YTD-YoY rates at constant (1990) prices
NSA YoY rates at constant (1990) prices
from Q4-2010 SA QoQ rates at constant (1990) prices
the NBS also publishes annual GDP levels and rates within theStatistical Communiqu on the National Economic and SocialDevelopment due towards the end of February every year
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Data types & dissemination
Chinese GDP: criticism
lack of consistency between local and aggregate data arguably due toremuneration scheme of local officials: incentive to exaggerate growthperformance [Wu & Ozyildirim (2011)]
data revisions:
quarterly figures do not always add up to annual counterparts [Maier (2011)]
NBS only started benchmarking quarterly GDP to verified annual datain 2003 [Curran & Funke (2006)]
size of revision can be substantial [Goldman Sachs Asia Economic Group (2006)]
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Data types & dissemination
Chinese GDP: our stand 1
difference between first release and last vintage high in the past butlittle/no revision lately
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Data types & dissemination
Chinese GDP: our stand 2
despite all criticism there is no evidence of data manipulation in thelatest period
industrial production and output figures are consistent withalternative indicators:
electricity production
trade volume data from non-Chinese sources
alternative domestic and foreign indicators provide no evidence thatChina’s growth was lower than stated; sample period 2009-Q4 to2012-Q4 [FRBSF Economic Letter (2013)]
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Data types & dissemination
Chinese GDP: our stand 3
real time factor
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Data types & dissemination
Calendar Overview
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Model
Model for Nowcasting
yQYt : quarterly YoY GDP at time t
Ωv : vintage of data at different frequencies available at v where v isthe date-time of a particular economic release
Nowcasting of yQYt is the orthogonal projection of yQY
t on theavailable information Ωv :
E[yQYt |Ωv ] (1)
The information set Ωv :
has a ”ragged”/”jagged” edge due to different publication lags
contain mixed frequency variables: monthly and quarterly
can be potentially large
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Model
Other Features
projection are updated at every new data release:E[yQY
t |Ωv ], E[yQYt |Ωv+1]
where v and v + 1 denote two consecutive data releases
the interval between two consecutive releases can range from a fewminutes to a few days: v is both high frequency and irregularly spaced
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Model
News and Nowcast Revision
with new releases (abstracting from data revisions) the informationset expands: Ωv ⊆ Ωv+1
decompose the forecast into two orthogonal components:E[yQY
t |Ωv+1]new forecast
= E[yQYt |Ωv ]
old forecast
+ E[yQYt |Iv+1]revision
Iv+1 information in Ωv+1 not in Ωv
accounting for the joint dynamics of all the variables, the forecastrevision can be expressed asE[yQY
t |Ωv+1]−E[yQYt |Ωv ] =
∑j∈Jv+1
bj ,t,v+j(xj ,Tj,v+1−E[xj ,Tj,v+1
|Ωv ])[Banbura & Modugno (2010)]
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Model
Dynamic Factor Model
Let yt = [y1,t , y2,t , . . . , yn,t ]′, t = 1, . . . ,T denote a set of
standardized stationary monthly variables. We assume that yt has afactor structure:
yt = Λft + εt (2)
ft : unobserved common factors
εt : idiosyncratic component
Λ: factor loadings
factors follow a VAR process:
ft = A1ft−1 + . . .+ Apft−p + ut ut ∼ i .i .d .N(0,Q) (3)
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Model
Quarterly Variables
assume GDP level data for a given quarter are the sum of monthlyunobserved components and let yMY
t denote the unobserved monthlyYoY rate
yMYt admits the same factor structure as the monthly variables:
yMYt = ΛQ ft + εQt (4)
εQt = αQεQt−1 + εQt εQt ∼ i .i .d .N(0, σ2Q) (5)
link between monthly unobserved and partially observed quarterlyGDP
yQYt = Y Q
t − Y Qt−12 = (1− L12)Y Q
t
≈ (1− L12)(1 + L + L2)YMt
= (1 + L + L2)yMYt = yMY
t + yMYt−1 + yMY
t−2 (6)
(7)
[Mariano & Murasawa (2003)]
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Model
Model Characteristics
Missing Data: Kalman filter and smoother can be used to obtain, inan efficient manner, the projection of any pattern of data availabilityin Ωv as well as the news Iv+1 and expectation needed to computebj ,t,v+j
Mixed Frequency: consider low frequency data as higher frequencywith periodically missing values
Estimation: Quasi Maximum Likelihood:
robust and feasible [Doz, Giannone & Reichlin (2008)]
can handle missing data [Banbura & Modugno (2010)]
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Results
Pseudo real-time exercise
model estimation starts in 2000-Q1
out-of-sample evaluation span is 2008-Q1 to 2013-Q3
dataset includes releases up to Oct 1st 2013
over 550 pseudo vintages built applying release pattern to lastavailable vintage
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Results
YoY Growth Rate
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Results
YoY: Q1-2013
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Results
YoY: Q2-2013
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Results
YoY: Q3-2013
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Results
RMSFE
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Results
Impact of data releases
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Results
Implied Annual Growth Rate
principal institutional benchmarks forecast Chinese GDP in terms ofannual rate
the model-implied annual (calendar year) growth rate is built basedon the following approximation:
yAt = Y At − Y A
t−1
= (1− L12)Y At
≈ (1− L12)(1 + L3 + L6 + L9)Y Q4t
= (1 + L3 + L6 + L9)yQ4t
= yQ1t + yQ2
t + yQ3t + yQ4
t (8)
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Results
Components of Annual Growth Rate
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Results
Annual GDP Growth: 2008
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Results
Annual GDP Growth: 2008
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Results
Annual GDP Growth: 2009
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Results
Annual GDP Growth: 2009
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Results
Annual GDP Growth: 2010
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Results
Annual GDP Growth: 2010
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Results
Annual GDP Growth: 2011
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Results
Annual GDP Growth: 2011
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Results
Annual GDP Growth: 2012
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Results
Annual GDP Growth: 2012
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Results
Annual GDP Growth: 2013
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Results
Annual GDP Growth: 2013
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Results
Annual GDP Growth: RMSFE
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China Factor
Tracking China GDP in Real Time
maintained indices regularly published by institutions and tracked byfinancial markets:
CEMAC-GS LI: (1) money supply M2; (2) interest rate spread; (3) newly started investment projects;
(4) total floor space started; (5) area of land developed for real estate; (6) ratio of gross sales to output; (7)
freight traffic; (8) freight handled at major costal ports; (9) Hang Seng index; (10) NBS consumer expectation
index
CEMAC-GC CI: (1) industrial production; (2) investments in fixed assets; (3) industrial employment;
(4) profits of industrial enterprises; (5) government revenues; (6) retail sales; (7) HH disposable income; (8)
total trade value
OECD CLI: (1) money supply M2; (2) enterprise deposits; (3) steel production; (4) cement production;
(5) chemical fertilizer production; (6) motor vehicle production
TCB LI: (1) total loans; (2) total floor space started; (3) PMI exports; (4) PMI supplier deliveries; (5)
PBoC raw material supply index; (6) NBS consumer expectation index
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China Factor
Characteristics of Chinese Indices
static weighted averages which do not account for dynamic
estimates are not in real time: indices are produced when allcomponents are available
substantial publication delay
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China Factor
China Factor
simple: easily derived byproduct of the model
high frequency: updated in real time
timely: published with no delay, updates for current month
reliable: small revisions
accurate: tracks GDP growth
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China Factor
China Factor: overview
Table : China Indicesmean revision stdev revision publication lagfirst last first last (days to eorm)
ChinaFactor 0.000 -0.003 0.359 0.450 -15; -10 FebCEMAC-GS LI 0.000 -0.005 0.530 0.587 +30; no FebCEMAC-GS CI 0.000 0.003 0.365 0.397 +30; no Feb
OECD CLI 0.114 0.138 0.375 0.707 +30; no FebTCB LI* 0.051 – 0.084 – +20; MoM
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China Factor
CEMAC-GS Indices: Leading
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China Factor
CEMAC-GS Indices: Coincident
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China Factor
OECD Composite Leading Indicator
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China Factor
TCB Leading Indicator
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