GDP flash estimates based on ESI: Does it work?

An econometric approachusing real data for Slovakia

Ján HaluškaInstitute of Informatics and Statistics

(INFOSTAT) Bratislava

Content

Business and consumer tendency surveys (BCTS) in Slovakia

Methodology of GDP flash estimates based on econometric approach

Results of econometric modelling

Conclusion

Motivation since 2005 the Statistical Office of the

Slovak Republic (SR) has been obliged to compute and publish flash estimates of GDP (and total employment) always within 45 days after the end of each quarter

it is 15 days earlier than preliminary data about economic development in a given quarter are released

main objective is to create a specific model framework based on BCTS results supporting preparation of GDP flash estimates

Motivation BCTS results are published the last working day

of each reference month (quarter) while GDP figure is published on a quarterly basis 60 days after the end of each reference quarter

Economic Sentiment Indicator (ESI) is the most popular composite indicator primarily used to anticipate or forecast the performance of key economic variables

ESI is being used as a reference (explanatory) variable in econometric model for GDP flash estimates

BCTS in SLOVAKIA in Slovakia BCTS have been conducted on the

monthly basis by the Statistical Office of the SR for industry, construction and retail trade since 1993 and for services since 2002

fully harmonised form with the methodology recommended by the European Commission was reached in 1998

ESI follows a common methodological approach developed by the U.S. National Bureau of Economic Research (NBER) for U.S. indicator

BCTS in SLOVAKIA ESI summarizes information gained from

BCTS among economic actors

respondents have the choice of fixed set of answers for their assessment of the current or future economic situation (positive, neutral and negative)

ESI facilitates the interpretation of BCTS results as it summarizes the answers for different variables in a single number and in a simple time series

BCTS in SLOVAKIA ESI4 is calculated as a weighted average

of four confidence indicators

in industry (40%), construction (20%), retail trade (20%) and consumer confidence indicator (20%)

ESI4 started in Slovakia in January 1996, i.e. 52 observations exist on the quarterly basis

BCTS in SLOVAKIA ESI5 is calculated as a weighted average

of five confidence indicators

in industry (40%), services (30%), construction (5%), retail trade (5%) and consumer confidence indicator (20%)

ESI5 started in Slovakia in January 2002, i.e. 24 observations exist on the quarterly basis

BCTS in SLOVAKIA BCTS results for Slovakia can be found on the

website of the Statistical Office of the SR

www.statistics.sk

-40

-30

-20

-10

0

10

20

30

1998 2000 2002 2004 2006 2008

Industrial confidence indicator, balanceIndustrial confidence indicator, long-term average (1997-2008)

-80

-60

-40

-20

0

20

40

1998 2000 2002 2004 2006 2008

Construction confidence indicator, balanceConstruction confidence indicator, long-term average (1997-2008)

-20

-10

0

10

20

30

40

50

1998 2000 2002 2004 2006 2008

Retail trade confidence indicator, balanceRetail trade confidence indicator, long-term average (1997-2008)

-50

-40

-30

-20

-10

0

10

1998 2000 2002 2004 2006 2008

Consumer confidence indicator, balanceConsumer confidence indicator, long-term average (1997-2008)

-30

-20

-10

0

10

20

1998 2000 2002 2004 2006 2008

Economic sentiment indicator, balanceEconomic sentiment indicator, long-term average (1997-2008)

Methodology of GDP flash estimates based on econometric approach

ESI should be compared with the reference variable recording movements in the economy as a whole, i.e. real GDP growth (compared to the same period of the previous year)

the initial hypothesis: it is assumed that there exists statistically significant dependency between percentage growth rate of GDP (compared to the same quarter of the previous year) and ESI

quarterly time series of ESI created from its original, i.e. monthly time series (simple arithmetic mean)

-30

-20

-10

0

10

20

-4

0

4

8

12

16

98 99 00 01 02 03 04 05 06 07

Real GDP growth, % (RHS)Economic sentiment indicator, balance (LHS)

Correlation between GDP growth and ESI

Coefficient of correlation = 0.734(40 observations)

Methodology of GDP flash estimates based on econometric approach

time series of both GDP and ESI are I(1), i.e. non-stationary; using OLS regression provides incorrect conclusions (spurious regression)

the starting hypothesis: real GDP is assumed to grow at a constant rate, however, changes in ESI are supposed to make this rate variable

construction of the ECM relationship based on two steps: the Engle-Granger approach

Methodology of GDP flash estimates based on econometric approach

1. long-term equilibrium (LTE) between the non-stationary variables is estimated

GDP = * e b * TIME + c * ESI

orlog (GDP) = a + b * TIME + c * ESI

2. ECM relationship is estimated using the stationary time series of residuals derived from LTE

Methodology of GDP flash estimates based on econometric approach

methodology applied in BUSY model has been used by the European Commission since 1996

Results of econometric modelling two ECMs created and estimated for GDP flash

estimates using original quarterly time series covering the period from the 1st quarter 1997 to the 4th quarter 2007, i.e. 44 observations in combination with seasonal dummies

ECM with broken linear long-term trend

ECM with quadratic long-term trend

Dependent Variable: LOG(GDP)

Method: Least Squares

Sample: 1997:1 2007:4

Included observations: 44

LOG(GDP)=C(1)+C(2)*TIME*(TIME<8.)+C(3)*TIME

*(TIME>=8.)*(TIMEQ<32.)+C(4)*TIME*(TIME>=32.)

+C(5)*SD1+C(6)*SD3+C(7)*ESI*(TIME>=11.)

Coefficient Std. Error t-Statistic Prob.

C(1) 5.354419 0.014491 369.5078 0.0000

C(2) 0.003907 0.000778 5.024655 0.0000

C(3) 0.009777 0.000582 16.80237 0.0000

C(4) 0.020086 0.000753 26.68548 0.0000

C(5) -0.057790 0.007874 -7.339079 0.0000

C(6) 0.022063 0.007943 2.777520 0.0085

C(7) 0.001780 0.000759 2.345762 0.0245

R-squared 0.984756 Mean dependent var 5.576203

Adjusted R-squared 0.982284 S.D. dependent var 0.155578

S.E. of regression 0.020708 Akaike info criterion -4.771704

Sum squared resid 0.015866 Schwarz criterion -4.487855

Log likelihood 111.9775 Durbin-Watson stat 1.729483

LTE with broken linear trend

-.06

-.04

-.02

.00

.02

.04

.06

5.2

5.4

5.6

5.8

6.0

97 98 99 00 01 02 03 04 05 06 07

Residual Actual Fitted

ECM with broken linear long-term trend

Dependent Variable: DLOG(GDP)

Method: Least Squares

Sample(adjusted): 1997:2 2007:4

Included observations: 43 after adjusting endpoints

DLOG(GDP)=C(2)*D(IEED)+C(3)*RESIDGDP(-1)+C(4)*D(SD1)

+C(5)*D(SD3)+C(6)*SD1(-1)

Coefficient Std. Error t-Statistic Prob.

C(2) 0.000230 0.000103 2.230829 0.0317

C(3) -0.668806 0.145691 -4.590583 0.0000

C(4) -0.033969 0.005543 -6.127851 0.0000

C(5) 0.030468 0.003434 8.871799 0.0000

C(6) 0.048920 0.007265 6.733730 0.0000

R-squared 0.908791 Mean dependent var 0.014100

Adjusted R-squared 0.899190 S.D. dependent var 0.049568

S.E. of regression 0.015738 Akaike info criterion -5.356524

Sum squared resid 0.009412 Schwarz criterion -5.151733

Log likelihood 120.1653 Durbin-Watson stat 1.681018

-.04

-.02

.00

.02

.04

.06

-.12

-.08

-.04

.00

.04

.08

.12

97 98 99 00 01 02 03 04 05 06 07

Residual Actual Fitted

LTE with quadratic trend Dependent Variable: LOG(GDP)

Method: Least Squares

Sample: 1997:1 2007:4

Included observations: 44

LOG(GDP)=C(1)+C(2)*TIME+C(3)*TIME*TIME

+C(4)*SD1+C(5)*SD3+C(6)*ESI*(TIME>=11.)

Coefficient Std. Error t-Statistic Prob.

C(1) 5.446370 0.006775 803.9155 0.0000

C(2) 0.004722 0.000766 6.164189 0.0000

C(3) 0.000230 3.03E-05 7.583051 0.0000

C(4) -0.058739 0.007475 -7.858418 0.0000

C(5) 0.023215 0.007627 3.043851 0.0042

C(6) 0.001455 0.000705 2.063735 0.0459

R-squared 0.985518 Mean dependent var 5.576203

Adjusted R-squared 0.983613 S.D. dependent var 0.155578

S.E. of regression 0.019916 Akaike info criterion -4.868468

Sum squared resid 0.015073 Schwarz criterion -4.625169

Log likelihood 113.1063 Durbin-Watson stat 1.705885

-.06

-.04

-.02

.00

.02

.04

.06

5.2

5.4

5.6

5.8

6.0

97 98 99 00 01 02 03 04 05 06 07

Residual Actual Fitted

ECM with quadratic long-term trend

Dependent Variable: DLOG(GDP)

Method: Least Squares

Sample(adjusted): 1997:2 2007:4

Included observations: 43 after adjusting endpoints

DLOG(HDP00)=C(2)*D(IOVD)+C(3)*RESIDGDP(-1)+C(4)*D(SD1)

+C(5)*D(SD3)+C(6)*SD1(-1)

Coefficient Std. Error t-Statistic Prob.

C(2) 0.000231 0.000101 2.298805 0.0271

C(3) -0.745747 0.151497 -4.922510 0.0000

C(4) -0.035906 0.005520 -6.504521 0.0000

C(5) 0.031101 0.003368 9.233458 0.0000

C(6) 0.046985 0.007170 6.552803 0.0000

R-squared 0.913419 Mean dependent var 0.014100

Adjusted R-squared 0.904305 S.D. dependent var 0.049568

S.E. of regression 0.015334 Akaike info criterion -5.408597

Sum squared resid 0.008935 Schwarz criterion -5.203807

Log likelihood 121.2848 Durbin-Watson stat 1.638412

-.04

-.02

.00

.02

.04

.06

-.12

-.08

-.04

.00

.04

.08

.12

97 98 99 00 01 02 03 04 05 06 07

Residual Actual Fitted

-.04

-.03

-.02

-.01

.00

.01

.02

.03

.04

.05

98 99 00 01 02 03 04 05 06 07

Residuals from ECM with broken linear long-term trendResiduals from ECM with quadratic long-term trend

Comparison of accuracy

Conclusion ESI can be considered as a statistically

significant indicator of real GDP from a long-term point of view

strictly speaking, ESI can be considered as a statistically significant indicator of real GDP deviations from its long-term trend, which can be approximated by either broken linear trend or quadratic trend

Conclusion short-term changes of the indicator of

expected external demand (IEED) can be considered as statistically significant indicator of real GDP in short-term period

both ESI and IEED can be used as explanatory factors for construction of model relationship in ECM form for flash estimates of real GDP

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