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GDP flash estimates based on ESI: Does it work? An econometric approach using real data for Slovakia Ján Haluška Institute of Informatics and Statistics.

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Presentation on theme: "GDP flash estimates based on ESI: Does it work? An econometric approach using real data for Slovakia Ján Haluška Institute of Informatics and Statistics."— Presentation transcript:

1 GDP flash estimates based on ESI: Does it work? An econometric approach using real data for Slovakia Ján Haluška Institute of Informatics and Statistics (INFOSTAT) Bratislava

2 Content Business and consumer tendency surveys (BCTS) in Slovakia Methodology of GDP flash estimates based on econometric approach Results of econometric modelling Conclusion

3 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

4 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

5 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

6 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

7 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

8 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

9 BCTS in SLOVAKIA BCTS results for Slovakia can be found on the website of the Statistical Office of the SR www.statistics.sk

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15 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)

16 Coefficient of correlation = 0.734 (40 observations)

17 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

18 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 or log (GDP) = a + b * TIME + c * ESI 2.ECM relationship is estimated using the stationary time series of residuals derived from LTE

19 Methodology of GDP flash estimates based on econometric approach methodology applied in BUSY model has been used by the European Commission since 1996

20 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

21 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(5)*SD1+C(6)*SD3+C(7)*ESI*(TIME>=11.) CoefficientStd. Errort-StatisticProb. C(1)5.3544190.014491369.50780.0000 C(2)0.0039070.0007785.0246550.0000 C(3)0.0097770.00058216.802370.0000 C(4)0.0200860.00075326.685480.0000 C(5)-0.0577900.007874-7.3390790.0000 C(6)0.0220630.0079432.7775200.0085 C(7)0.0017800.0007592.3457620.0245 R-squared0.984756Mean dependent var5.576203 Adjusted R-squared0.982284S.D. dependent var0.155578 S.E. of regression0.020708Akaike info criterion-4.771704 Sum squared resid0.015866Schwarz criterion-4.487855 Log likelihood111.9775Durbin-Watson stat1.729483 LTE with broken linear trend

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23 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) CoefficientStd. Errort-StatisticProb. C(2)0.0002300.0001032.2308290.0317 C(3)-0.6688060.145691-4.5905830.0000 C(4)-0.0339690.005543-6.1278510.0000 C(5)0.0304680.0034348.8717990.0000 C(6)0.0489200.0072656.7337300.0000 R-squared0.908791Mean dependent var0.014100 Adjusted R-squared0.899190S.D. dependent var0.049568 S.E. of regression0.015738Akaike info criterion-5.356524 Sum squared resid0.009412Schwarz criterion-5.151733 Log likelihood120.1653Durbin-Watson stat1.681018

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25 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.) CoefficientStd. Errort-StatisticProb. C(1)5.4463700.006775803.91550.0000 C(2)0.0047220.0007666.1641890.0000 C(3)0.0002303.03E-057.5830510.0000 C(4)-0.0587390.007475-7.8584180.0000 C(5)0.0232150.0076273.0438510.0042 C(6)0.0014550.0007052.0637350.0459 R-squared0.985518Mean dependent var5.576203 Adjusted R-squared0.983613S.D. dependent var0.155578 S.E. of regression0.019916Akaike info criterion-4.868468 Sum squared resid0.015073Schwarz criterion-4.625169 Log likelihood113.1063Durbin-Watson stat1.705885

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27 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) CoefficientStd. Errort-StatisticProb. C(2)0.0002310.0001012.2988050.0271 C(3)-0.7457470.151497-4.9225100.0000 C(4)-0.0359060.005520-6.5045210.0000 C(5)0.0311010.0033689.2334580.0000 C(6)0.0469850.0071706.5528030.0000 R-squared0.913419Mean dependent var0.014100 Adjusted R-squared0.904305S.D. dependent var0.049568 S.E. of regression0.015334Akaike info criterion-5.408597 Sum squared resid0.008935Schwarz criterion-5.203807 Log likelihood121.2848Durbin-Watson stat1.638412

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30 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

31 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

32 FOR YOUR ATTENTION THANK YOU


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