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Motivation Literature review and goals of the paper Models Results Conclusions

Short and long-run time-of-use price elasticities in Swiss residential electricity demand Massimo Filippini. Outline. Motivation Literature review and goals of the paper Models Results Conclusions. A) Introduction and motivation of the paper.

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Motivation Literature review and goals of the paper Models Results Conclusions

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  1. Short and long-run time-of-use price elasticities in Swiss residential electricity demandMassimo Filippini

  2. Outline • Motivation • Literature review and goals of the paper • Models • Results • Conclusions

  3. A) Introduction and motivation of the paper Demand for electricity is subject to great cyclical variation (daily, seasonal,…) capacity problem during the peak period overcapacity problem during the off-peak period Different production costs

  4. Instruments • To shift peak demand to off-peak demand • Time-of-use rates (peak/off-peak/superpeak,..real time pricing,..) • Direct load control of electrical appliances (program  that can interrupt power supply to individual appliances ) • “Smart grids, smart appliances…smart pricing policies..”

  5. Electricity rate structure: Zurich Peak price Off- Peak price

  6. Can time-of-use rates be effective? Yes, if the cross-price elasticities are positive

  7. B) Literature review • Caves et al. (1980), Aubin et. al. (1995), Baladi et. al. (1998) , Faruqui and George 2002 for the US, utilize data from rates experiments • In Europe some experiments have been organized in France (see Aubin et. al. 1995) and in UK (see Henley 1994). • Filippini (1995a, 1995b) examined residential demand for electricity by time-of-use in Switzerland using revealed panel data • Lijesen (2007) estimates a real-time price elasticity for Netherland using time-series data • The cross-price elasticity are generally positive • No distinction bewtween short-run and long-run elasticities

  8. Goals of thepaper • Provides electricity companies with new values of price and cross-prices elasticities. • Estimation of short as well long-run price and cross-price elasticities • Use of several panel data econometric methods for dynamic model

  9. C) Model specification

  10. Model specification

  11. Data • The data covers seven annual periods from 2000 to 2006 and comes from a sample of Swiss cities. • Data was collected using a mailed questionnaire sent by the Swiss electric utilities operating in these cities. • A sample of 22 cities for the analysis. • The majority of these utilities are municipals, and provide power to their communities exclusively. The activities of these electric utilities are regulated by the canton and municipal public utility commissions.

  12. Econometricapproaches • Basicallywehavetwoproblemstwosolve: • Unobservedheterogeneity • Dynamicmodel: endogeneity of thelagged variable • Possiblesolutions: • Unobservedheterogeneity : FE, RE • endogeneity of thelagged variable:

  13. Econometricapproaches • Anderson and Hsiao (1982) proposed a simple instrumental variable estimator. • Arellano and Bond (1991) as well as Blundell and Bond (1998) have proposed two different estimators based on a general method of moment (GMM). Blundell–Bond (1998) propose a system GMM estimator (GMM-BB), which uses lagged first differences as instruments for equations in level as well as the lag variable in first-difference equations.

  14. Econometricapproaches • small samples  too many instruments can produce over-fitting of the instrumented variable and the resulting estimates are biased toward those of the OLS • properties hold for N large, so the estimation results can be biased in panel data with a small number of cross-sectional units. • An alternative approach proposed by Kviet (1995), which is based on the correction of the bias of LSDV, has recently been used in several studies. (T<20, N<50)

  15. Conclusions • Time-of-use rates can contribute to more efficient utilization of existing production capacity, allowing for build up of additional capacity to be postponed. • Dynamic panel data estimators (GMM) are sensitive

  16. Descriptivestatistics

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