ENERGY DEMANDS IN INDUSTRIAL SECTORS

1. ENERGY DEMANDS IN INDUSTRIAL SECTORS AGF Conferences Friday 30th November, 2007 Berlin

2. Some Background: Aim and implementation • Aim: explore relationship between energy consumption, energy prices, environmental taxation and energy pollution • Three workstreams • Can cross-sectoral policies like an ETR be justified on the basis of the dynamic properties of the data? • Are price-based policies like an ETR likely to have a considerable effect on consumption? • What is the shape of the relationship between pollution, economic activity and resources prices done done To do

3. Paper I • Can cross-sectoral policies like an ETR be justified on the basis of the dynamic properties of the data? • Take energy intensity of each sector • Subtract the average energy intensity in the industrial sector i.e. time invariant differential; linear trend; structural breaks; transitory components Run unit root tests (panel, breaks, panel + breaks) Implications: nature of the difference and ability to foresee • No rejecting  difference among sectors is stochastic and persistent  sectoral policies needed to accommodate deviations and persistent shocks • Rejecting  difference among sectors is deterministic  cross-sectoral policies are fine Results: 1) Reject when allowing for breaks both at the panel and single time series level; 2) price out of the equation;

4. Paper II - Sectors

5. Samples & Variables Time span: UK 1978 -2004 and 1991-2004 Germany: 1991-2004 Sources: ONS, IEA, DeStatis Economic activity: index of GVA Energy consumption: sum of Coal, Electricity, Natural Gas and Petroleum Products Energy price sector-based weighted average of fuel consumption s= sector; i = fuel

6. Energy Consumption (UK)

7. Energy Price UK (index)

8. Economic Activity UK (index)

9. Time Series Estimators • ARDL(1,1,1), ARDL(1,0,1), ARDL(1,1,0), ARDL(1,0,0) w & w/o time trend • Static model ARDL(0,0,0) • SC-based selection • Rather dubious coefficients

10. Time Series Estimators (UK 78-04) Odd Dynamics Economic Theory and Size ??

11. Issues in a panel time series estimation (N, T) Static vs. Dynamic: speed of adjustment to equilibrium, Static = within period Dynamic = allowing for adjustment period Homogenous vs. Heterogeneous: similarities across sectors Homo: Imposing same coefficients on all subsectors Hetero: Allowing for sector-specific parameters Cross Sectional Dependency: common shocks Common latent factors in the errors (not modelled explicitly by the xs) Common factors in the regressors Examples: Common institutional factors; common technological change; common world/national price

12. Panel Homogenous - static Static Fixed and Random effect + : consistent if parameters are heterogeneous -: assuming within period adjustment First Differences Estimators Different approach to get rid of unobserved individual effects

13. Panel Homogenous - Dynamic - : Nickell bias (removed asymptotically as T goes to inf) -: heterogeneity bias +: allowing dynamic adjustment Dynamic FE and RE Anderson-Hsiao Take FD; instrument for lagged FD GMM Gain in efficiency compared to AH Additional instruments (W) + weighting matrix (A) One-two steps

14. Panel Heterogeneous – static and dynamic Model How to allow for heterogeneity? • Mean-Group estimator • Random Coefficient estimator • ….. • using OLS coefficients  N and T big enough???

15. Cross Sectional Dependency - I Model FE/MG - PC Get 1-2 principal components from residuals from OLS time series models Run FE / MG N and T big enough …..

16. Cross Sectional Dependency Demeaned Mean Group Removing X-section error dependency & latent common factors by demeaning Could affect negatively the variance; Error if heterogeneity is present Common correlated Mean Group Rather general in terms of settings – MG-related issues

17. Results – UK & D 1991-2004 UK 1991-2004 CMGonly GVA: 0.60 (0.05-1.15); Price: -0.47 (-0.79 - -0.15) GERMANY 1991-2004: GVA only FD: 0.37 (0.17 – 0.58) Dynamic FE : 0.49 (0.11 – 0.87) AH : 0.88 (0.18 – 1.59) GMM : 0.57 (0.05–1.09) DMG : 0.55 (0.26 – 0.85)

18. Results UK - 1978-2004 – Price - I Dynamics: increasing size of the coefficient but also stand. err. Nickell effect vs. “dynamic” bias vs. heterogeneity bias Static models: Other models:

19. Results UK - 1978-2004 – Price - I RCM similar to MG: s you would expect Dynamic heterogeneous: too much to cope with it Static models: Other models:

20. Results UK - 1978-2004 – Price - I Common (tech, institutional) factors  not big effect in this dataset Bimodal distribution of estimator  small overlap: Static model: Other models:

21. Results UK - 1978-2004 – Price - I OVERALL: Bimodal distribution of estimator  small overlap: Static models: Other models:

22. Results UK - 1978-2004 - Economic Activity

23. Conclusions - I • Inability to estimate time –series models at the sectoral level • Value added: • Increasing confidence through comparison • Hetero models (rarely applied in the energy literature 3 examples) • Allowing for common factors (never applied in the energy literature) • Heterogeneity and Dynamics: increasing the response to price changes • Value: Conservative (static): -0.40; Common-ground (overlap): -0.58; Average (all estimators): -0.74 • Being conservative  neglecting heterogeneity of production functions; assuming within period adjustment to equilibrium

24. Conclusions - II Other recent sources in the literature: Agnolucci (2007) and Hunt et al (2003): both implementing STSMs on industry in UK - GVA: 0.39 vs. 0.72 vs. 0.55 (here) - Price: -0.74 vs. -0.20 : high vs. low again (reconciling different results) Some support for high because of the restrictions when adopting estimators indicating low in this study However, even when being conservative (static) : -0.40 is a decent size elasticity for price-based policies Most likely it is an underestimate