1. How to Estimate Gasoline Price Elasticities of Automobile Travel Demand Julian Dieler a), Frank Goetzke b) and Colin Vance c) a) Ifo Institute for Economic Research at the University of Munich b) University of Louisville c) RWI Institute for Economic Research Essen 33 rd USAEE/IAEE North America ConferenceOctober 27, 2015 / Pittsburgh

2. Motivation 2 The magnitude of price elasticities of travel demand/fuel demand is broadly discussed in the literature Implications for the policy debate: CAFE standards vs. fuel taxes Discussion is mainly about the specification and identification:  Endogeneity of the price  Differentiation between tax and price elasticity BUT:There is only little to no discussion about the econometric methodology The methodology mostly applied in this literature: Estimating the log- linearized demand function with least squares estimators Dieler How to Estimate Gasoline Price Elasticities of Automobile Travel Demand

3. 3Dieler How to Estimate Gasoline Price Elasticities of Automobile Travel Demand Motivation

4. 4 1.Theoretical background on problems of log-linearization 2.Alternative estimation methods 3.Case study:German Mobility Panel 4.Model comparison tests 5.Results 6.Conclusion Dieler How to Estimate Gasoline Price Elasticities of Automobile Travel Demand Outline

5. 5Dieler How to Estimate Gasoline Price Elasticities of Automobile Travel Demand 1.Theoretical background on problems of log- linearization

6. 6Dieler How to Estimate Gasoline Price Elasticities of Automobile Travel Demand 1.Theoretical background on problems of log- linearization

7. 7Dieler How to Estimate Gasoline Price Elasticities of Automobile Travel Demand 2.Alternative estimation methods

8. 8 4)Two-Part models Accommodate the idea of modeling travel demand as a two-step process (buying a car/driving it) Binary first step consists of a Probit model a)Hurdle models Second step is modeled as a truncated-at-zero regression model (OLS/Poisson) b)Zero-inflated models  Also allow for zero-observations on the second stage  Second step consists of count-data model like Poisson or NB 5)Heckit model Equal to the Hurdle model with the difference that Heckit controls for a potential correlation of the errors from the two steps Dieler How to Estimate Gasoline Price Elasticities of Automobile Travel Demand 2.Alternative estimation methods

9. 9 German Mobility Panel Dieler How to Estimate Gasoline Price Elasticities of Automobile Travel Demand 3.Case study 1997 – 2013 Rotating panel of 1500 households Each household stays in the panel for 3 years Variables of interest:  daily kilometers driven  local gasoline price  monthly income N = 4891 Summary statistics

10. 10 Model Confidence Set (Hansen et al. 2011) Determines the best performing model given a certain dataset The MCS procedure: 1.Definition of a set of models to be compared 2.Test of the null hypothesis of equal predictive ability (EPA) of the models 3.If the null hypothesis is rejected, the worst performing models are eliminated from the comparison set and the null hypothesis is tested again. This process is repeated until the null hypothesis is accepted und thereby the MCS is determined. Criteria for the predictive ability are loss functions: 1.Absolute deviance of the predictions from the observations 2.Squared error Dieler How to Estimate Gasoline Price Elasticities of Automobile Travel Demand 4.Model comparison tests

11. 11Dieler How to Estimate Gasoline Price Elasticities of Automobile Travel Demand 4.Model comparison tests

12. 12 Model Selection Test Marginal Effects Dieler How to Estimate Gasoline Price Elasticities of Automobile Travel Demand 5.Results VariablesOLSOLS IHSPPML Hurdle OLS Hurdle Poisson HeckitNBZIPZINB Fuel price-0.340*-0.718**-0.457**-0.588*-0.343*-0.342*-0.477**-0.343*-0.408** Income (real)0.164**1.156**0.514**1.065**0.456**0.169**0.533**0.456**0.446** * p<0.05; ** p<0.01 Model Confidence SetRobustness check Loss function: LOOCV MSE Modelsquared errorabsolute value OLS776 OLS IHS999 PPML444 Hurdle OLS888 Hurdle Poisson1* 1 Heckit667 NB555 ZIP2* 2 ZINB33*3

13. 13Dieler How to Estimate Gasoline Price Elasticities of Automobile Travel Demand 6.Conclusion

14. 14 A.1 Summary statistics MOP (Germany)Germany Dieler How to Estimate Gasoline Price Elasticities of Automobile Travel Demand Appendix VariableVariable definitionMeanStd.Dev.MinMax Daily kilometers Daily kilometers driven in km on average in the observation period 28.4324.570202.6 Fuel priceReal fuel price in € per liter1.3890.1341.0851.687 Income (real) Real net monthly household income in € 2122961.2236.54376 Household size Number of people living in the household 1.8630.96717 Employed 1 if person is employed in full- or half-time job 0.4870.50001 N = 4891

15. 15 A.2 Regression results Dieler How to Estimate Gasoline Price Elasticities of Automobile Travel Demand Appendix VariablesOLSOLS IHSPPML Hurdle OLS Hurdle Poisson HeckitNBZIPZINB Fuel price -0.340 * -0.718 ** -0.457**-0.339*-0.256*-0.342*-0.477**-0.256*-0.322* (0.134)(0.267)(0.146)(0.133)(0.126)(0.134)(0.163)(0.126)(0.128) Income (real) 0.164 ** 1.156 ** 0.514**0.164**0.186**0.169**0.533**0.186**0.176** (0.034)(0.071)(0.039)(0.034) (0.037)(0.050)(0.034)(0.032) Household size 0.158 ** 0.622 ** 0.185**0.157**0.064*0.161**0.267**0.064*0.087** (0.032)(0.065)(0.037)(0.032) (0.033)(0.038)(0.032)(0.031) Employed 0.327 ** 0.170 ** 0.256**0.326**0.287**0.326**0.272**0.287**0.289** (0.029)(0.059)(0.033)(0.029)(0.028)(0.029)(0.033)(0.028)(0.027) Constant 1.940 ** -5.617 ** -0.665*2.634**2.015**1.897**-0.861*2.015**2.095** (0.250)(0.519)(0.285)(0.249)(0.247)(0.278)(0.370)(0.247)(0.237) N39684891 AIC7991.117946.1114805.511859.572284.211894.741884.972284.238221.8 BIC8022.517978.6114837.911924.572349.211972.741923.972349.238293.3 * p<0.05; ** p<0.01