Nowcasting China Real GDP - Cirano · Nowcasting China Real GDP Domenico Giannoney Silvia Miranda Agrippinoz Michele Modugno[ yULB & CEPR zNowCasting Economics [Federal Reserve BoardCIRANO

Nowcasting China Real GDP

Domenico Giannone† Silvia Miranda Agrippino‡

Michele Modugno[

†ULB & CEPR‡NowCasting Economics[Federal Reserve Board

CIRANO - 11/10/2013

Giannone, Miranda & Modugno (2013) Nowcasting China Real GDP CIRANO - 11/10/2013 1 / 52

Motivation & Intro

Forecast-Nowcast-Backcast: Q3-2013


Motivation & Intro

Forecast Revision: Oct 1st, 2013


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


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]


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


Motivation & Intro

Mimicking the Market: Data Flow


Motivation & Intro

Mimicking the Market: Interpreting the News


Choice of Relevant Information


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



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)]



Chinese GDP: our stand 1

difference between first release and last vintage high in the past butlittle/no revision lately




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)]




real time factor



Calendar Overview


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


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


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)]


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)


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)]


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)]


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


Results

YoY Growth Rate


Results

YoY: Q1-2013


Results

YoY: Q2-2013


Results

YoY: Q3-2013


Results

RMSFE


Results

Impact of data releases


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)


Results

Components of Annual Growth Rate


Results

Annual GDP Growth: 2008


Results



Results



Results



Results



Results



Results



Results



Results



Results



Results



Results



Results

Annual GDP Growth: RMSFE


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


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


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


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


China Factor

CEMAC-GS Indices: Leading


China Factor

CEMAC-GS Indices: Coincident


China Factor

OECD Composite Leading Indicator


China Factor

TCB Leading Indicator


China Factor

China Real Time Factor


Documents

Nowcasting China Real GDP - Cirano · Nowcasting China Real GDP Domenico Giannoney Silvia Miranda Agrippinoz Michele Modugno[ yULB & CEPR zNowCasting Economics [Federal Reserve BoardCIRANO