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

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 To do

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;

Paper II - Sectors IdentifierSectorsISIC Taxonomy 1MINMining and Quarrying FTFood and Tobacco TXTTextile and Leather PPPPulp, Paper and Printing CHEChemicals 24 6NMMNon-Metallic Minerals 26 7MACMachinery TRATransport Equipment CONConstruction 45 10METMetals 27

Samples & Variables Time span: UK and Germany: 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

Energy Consumption (UK)

Energy Price UK (index)

Economic Activity UK (index)

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

Time Series Estimators (UK 78-04) FTTXTPPPCHENMMMACCONMETTOT e t (3.41) 0.69 (4.75) 0.78 (9.82) 0.81 (7.40) (-0.76) 0.48 (3.82) 0.68 (3.46) (-1.50) 0.78 (7.38) ytyt 1.21 (2.31) (-0.95) 0.69 (2.81) (-2.38) 0.44 (2.71) 0.17 (0.73) (-3.45) 0.54 (2.34) (-0.03) y t (-3.07) 0.62 (2.32) 0.45 (1.79) (-1.74) ptpt 0.05 (0.31) (-2.13) (-2.48) (-2.70) (-3.18) (-1.77) (-3.82) (-3.27) (-0.15) p t (-1.62) (-2.00) trend (-2.68) (-1.81) (-6.47) (-3.72) (-5.91) LRY 2.46 (1.87) (-1.22) (-0.46) (-1.54) 0.93 (8.63) 1.20 (3.19) (-2.22) 0.09 (0.65) (-0.03) LR P (-1.63) (-1.76) (-1.87) (-1.39) (-2.92) (-1.90) (-1.90) (-5.22) (-0.15) Odd Dynamics Economic Theory and Size ??

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 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 Homogenous vs. Heterogeneous: similarities across sectors Homo: Imposing same coefficients on all subsectors Hetero: Allowing for sector-specific parameters

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

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

Panel Heterogeneous – static and dynamic Model How to allow for heterogeneity? 1)Mean-Group estimator 2)Random Coefficient estimator ….. using OLS coefficients  N and T big enough???

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 …..

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

Results – UK & D UK CMG only GVA: 0.60 ( ); Price: ( ) GERMANY : 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)

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

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

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

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

Results UK Economic Activity

Conclusions - I Inability to estimate time –series models at the sectoral level Value added: 1)Increasing confidence through comparison 2)Hetero models (rarely applied in the energy literature 3 examples) 3)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): Being conservative  neglecting heterogeneity of production functions; assuming within period adjustment to equilibrium

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 vs (here) - Price: vs : 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) : is a decent size elasticity for price-based policies Most likely it is an underestimate