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Pooled Time Series Cross-Section Estimation of Demand for Gasoline and Diesel in G7 Countries

Pooled Time Series Cross-Section Estimation of Demand for Gasoline and Diesel in G7 Countries. 2009 International Energy Workshop 17-19 June Venice Italy Mehdi Asali (Ph.D.) Petroleum Studies Department OPEC. Outline. Introduction Modeling Approach Disaggregation of data

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Pooled Time Series Cross-Section Estimation of Demand for Gasoline and Diesel in G7 Countries

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  1. Pooled Time Series Cross-Section Estimation of Demand for Gasoline and Diesel in G7 Countries 2009 International Energy Workshop 17-19 June Venice Italy Mehdi Asali (Ph.D.) Petroleum Studies Department OPEC

  2. Outline • Introduction • Modeling Approach • Disaggregation of data • Structure of the Models and Estimation Results • Concluding Remarks

  3. Introduction: Crude and Products Prices

  4. Introduction: Per Car Gasoline Consumption

  5. Introduction: Real Consumer Prices of Gasoline

  6. Introduction: Real Per Capita GDP

  7. Modeling Approach: VAR, ARDL and Pooled Time Series Cross-Section • VAR and ARDL models are used for individual country estimates. Pooled time series cross-section models are estimated for all G7 countries data combined • Pooled time series cross-section is a method of studying a particular subject (e.g. demand for products) within multiple sites (e.g. countries) periodically observed over a time frame • The combination of time series with cross-section can enhance quality and quantity of estimations

  8. Structural of the VAR Model

  9. General Structural of the Pooled Model (ARDL)

  10. Individual Country Specific and Polled Estimation • Two VAR and ARDL type econometric models are estimated for each G7 country individually and a pooled time series cross-section model is estimated for all countries as a whole • Here we have used about (80*7*6) 3360 observations for our panel estimations and compared the findings with that of individual countries’ estimation results

  11. Disaggregation of Data • Different frequency of time series: annual car ownership, quarterly demand for gasoline • Lack of time series with high frequency for developing countries (China, India,..) • Temporal disaggregation of data in applied econometrics: Mathematical, Statistical and State Space Aproach • Handbooks by IMF and EC Eurostat, ECOTRIM

  12. State Space Algorithm of Temporal Disaggregation . Process

  13. State Space Approach

  14. Statistical Approach, Fernandez and Litterman

  15. Statistical Approach, Fernandez and Litterman

  16. De-seasonalisation of the Quarterly Time Series

  17. De-seasonalisation of the Quarterly Time Series

  18. Estimation Results • Individual Country Estimates (Unrestricted VAR Models and Auto-Regressive Distributed Lag ARDL Models) • Pooled Time Series Cross-Section Estimates

  19. Individual Country Estimates • VAR and ARDL(1,1,1,1,1,1) models are estimated for each one of the G7 countries • The main results include (estimates of) the short and long-run price and income elasticities of demand for gasoline and diesel and speed of adjustment in economies of concerned • This allows comparison of elasticities of demand for gasoline and diesel in these countries and G7 as a whole

  20. Individual Country Estimates: Canada

  21. Individual Country Estimates: France

  22. Individual Country Estimates: Germany

  23. Individual Country Estimates: Italy

  24. Individual Country Estimates: Japan

  25. Individual Country Estimates: UK

  26. Individual Country Estimates: USA

  27. Price and Income Elasticities of (Per- Car) Demand for Gasoline (G7)

  28. Pooled Model Estimation Results

  29. Pooled Model Estimation Results

  30. Pooled Model Estimation Results

  31. Estimating a Partial Adjustment Model of Demand for Oil in G7 Countries

  32. Summary of Estimation Results for Demand for Oil in G7 Countries

  33. Quarterly Changes of GDP Elasticity of Demand for Oil in G7 Countries

  34. Summary and Concluding Remarks 1 of 2 • In this report (per car) demand for gasoline and diesel in G7 countries for the period of 1990-2009 is investigated using VAR, ARDL and Pooled Time Series Cross0Section methods • Models are of quarterly frequency and we had to disaggregate some of the time series that were only available annually (car ownership) to arrive at quarterly data

  35. Summary and Concluding Remarks 2 of 2 • Statistically significant negative relations between (per car) demand for gasoline and increases in per capita income in 5 out of 7 countries under study • Only for USA and Canada a positive relation between per capita income growth was found • Gasoline prices, and particularly its ratio to prices of diesel appear to exert significant negative impact on demand for gasoline • An increase in demand for diesel reduces demand for gasoline with one period lag and not at the same period

  36. Thank you

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