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Massimo Filippini - One of the best experts on this subject based on the ideXlab platform.
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regional impact of changes in disposable income on spanish electricity Demand a spatial econometric analysis
Energy Economics, 2013Co-Authors: Letizia Blazquez, Massimo Filippini, Fabian HeimschAbstract:This paper presents an empirical analysis of residential electricity Demand considering the existence of spatial effects. This analysis has been performed using aggregate panel data at the province level for 46 Spanish provinces for the period from 2001 to 2010. For this purpose, we estimated a log–log Demand Equation using a spatial autoregressive model with autoregressive disturbances (SARAR). The purpose of this empirical analysis is to determine the influence of price, income, and spatial spillovers on residential electricity Demand in Spain. We are particularly interested in analyzing the impact of household disposable income variation across provinces observed during the economic crisis period 2009–2010. The estimation results show relatively low income elasticity and an inelastic Demand to prices. Furthermore, the results show the presence of spatial effects in Spanish residential electricity consumption.
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residential electricity Demand in spain new empirical evidence using aggregate data
Energy Economics, 2013Co-Authors: Leticia Blazquez, Nina Boogen, Massimo FilippiniAbstract:This paper presents an empirical analysis on residential Demand for electricity. This analysis has been performed using aggregate panel data at the province level for 47 Spanish provinces for the period from 2000 to 2008. For this purpose, we estimated a log–log Demand Equation for electricity consumption using a dynamic partial adjustment approach. This dynamic Demand function has been estimated using a two-step system GMM estimator proposed by Blundell and Bond (1998). The purpose of this empirical analysis is to highlight some of the characteristics of Spanish residential electricity Demand. Particular attention has been paid to the influence of price, income, and weather conditions on electricity Demand. The estimated short and long-run own price elasticities are negative, as expected, but lower than 1. Furthermore, weather variables have a significant impact on electricity Demand.
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response of residential electricity Demand to price the effect of measurement error
Energy Economics, 2011Co-Authors: Anna Alberini, Massimo FilippiniAbstract:Abstract In this paper we present an empirical analysis of the residential Demand for electricity using annual aggregate data at the state level for 48 US states from 1995 to 2007. Earlier literature has examined residential energy consumption at the state level using annual or monthly data, focusing on the variation in price elasticities of Demand across states or regions, but has failed to recognize or address two major issues. The first is that, when fitting dynamic panel models, the lagged consumption term in the right-hand side of the Demand Equation is endogenous. This has resulted in potentially inconsistent estimates of the long-run price elasticity of Demand. The second is that energy price is likely mismeasured. To address these issues, we estimate a dynamic partial adjustment model using the Kiviet corrected Least Square Dummy Variables (LSDV) (1995) and the Blundell–Bond (1998) estimators. We find that the long-term elasticities produced by the Blundell–Bond system GMM methods are largest, and that from the bias-corrected LSDV are greater than that from the conventional LSDV. From an energy policy point of view, the results obtained using the Blundell–Bond estimator where we instrument for price imply that a carbon tax or other price-based policy may be effective in discouraging residential electricity consumption and hence curbing greenhouse gas emissions in an electricity system mainly based on coal and gas power plants.
Guglielmo Weber - One of the best experts on this subject based on the ideXlab platform.
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consumer credit evidence from italian micro data
Journal of the European Economic Association, 2005Co-Authors: Rob Alessie, Stefan Hochguertel, Guglielmo WeberAbstract:In this paper we analyse unique data on credit applications received by the leading provider of consumer credit in Italy (Findomestic). The data set covers a five-year period (1995-1999) during which the consumer credit market rapidly expanded in Italy and a new law (the usury law) came into force that set a limit on interest rates charged to consumers. We compute behavioural changes by controlling for changes in the observable characteristics of the Findomestic clientele and argue that, under suitable identifying assumptions, these changes can be given a structural interpretation. If the usury shock is assumed to have affected credit supply but not credit Demand-that is, if the usury law had a differential impact on the supply of various types of credit but a uniform impact on Demand-then we can identify and estimate a Demand Equation. Our key finding is that Demand is interest-rate elastic, particularly in the more affluent North. © 2005 by the European Economic Association.
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consumer credit evidence from italian micro data
Social Science Research Network, 2001Co-Authors: Rob Alessie, Stefan Hochguertel, Guglielmo WeberAbstract:In this Paper we analyse unique data on credit applications received by the leading provider of consumer credit in Italy (Findomestic). The data set covers a five year period (1995-99) during which the consumer credit market rapidly expanded in Italy and a new law came into force that set a limit to interest rates charged to consumers (the usury law). We investigate ways in which the law may have affected the consumer credit market and show how the applicants' pool has changed over time in comparison to a representative sample of the Italian population. We compute behavioural changes by controlling for changes in the observable characteristics of the Findomestic clientele and argue that, under suitable identifying assumptions, these changes can be given a structural interpretation. If the usury shock is assumed to have affected credit supply but not credit Demand, that is if the usury law had a differential impact on the supply of various types of credit but a uniform impact on Demand, we can identify and estimate a Demand Equation. Our key finding is that Demand is interest rate elastic, particularly in the North, where the consumer credit market is more competitive.
Badi H. Baltagi - One of the best experts on this subject based on the ideXlab platform.
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prediction in the panel data model with spatial correlation the case of liquor
Spatial Economic Analysis, 2006Co-Authors: Badi H. BaltagiAbstract:Abstract This paper considers the problem of prediction in a panel data regression model with spatial autocorrelation in the context of a simple Demand Equation for liquor. This is based on a panel of 43 states over the period 1965–1994. The spatial autocorrelation due to neighbouring states and the individual heterogeneity across states is taken explicitly into account. We compare the performance of several predictors of the states’ Demand for liquor for 1 year and 5 years ahead. The estimators whose predictions are compared include OLS, fixed effects ignoring spatial correlation, fixed effects with spatial correlation, random-effects GLS estimator ignoring spatial correlation and random-effects estimator accounting for the spatial correlation. Based on RMSE forecast performance, estimators that take into account spatial correlation and heterogeneity across the states perform the best for forecasts 1 year ahead. However, for forecasts 2–5 years ahead, estimators that take into account the heterogeneity a...
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prediction in the panel data model with spatial correlation the case of liquor
Social Science Research Network, 2006Co-Authors: Badi H. BaltagiAbstract:This paper considers the problem of prediction in a panel data regression model with spatial auto-correlation in the context of a simple Demand Equation for liquor. This is based on a panel of 43 states over the period 1965-1994. The spatial auto-correlation due to neighboring states and the individual heterogeneity across states is taken explicitly into account. We compare the performance of several predictors of the states Demand for liquor for one year and five years ahead. The estimators whose predictions are compared include OLS, fixed effects ignoring spatial correlation, fixed effects with spatial correlation, random effects GLS estimator ignoring spatial correlation and random effects estimator accounting for the spatial correlation. Based on RMSE forecast performance, estimators that take into account spatial correlation and heterogeneity across the states perform the best for one year ahead forecasts. However, for two to five years ahead forecasts, estimators that take into account the heterogeneity across the states yield the best forecasts.
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prediction in the panel data model with spatial correlation
2004Co-Authors: Badi H. Baltagi, Dong LiAbstract:The econometrics of spatial models have focused mainly on estimation and testing of hypotheses, see Anselin (1988b), Anselin et al. (1996) and Anselin and Bera (1998) to mention a few. In this chapter we focus on prediction in spatial models based on panel data. In particular, we consider a simple Demand Equation for cigarettes based on a panel of 46 states over the period 1963–1992. The spatial autocorrelation due to neighboring states and the individual heterogeneity across states is taken explicitly into account. In order to explain how spatial autocorrelation may arise in the Demand for cigarettes, we note that cigarette prices vary among states, primarily due to variation in state taxes on cigarettes. For example, in 1988, state excise taxes ranged from 2 cents per pack in a producing state like North Carolina, to 38 cents per pack in the state of Minnesota. In 1997, these state taxes varied from a low of 2.5 cents per pack for Virginia to $1.00 per pack in Alaska and Hawaii. Since cigarettes can be stored and are easy to transport, these varying taxes result in casual smuggling across neighboring states. For example, while New Hampshire had a 12 cents per pack tax on cigarettes in 1988, neighboring Massachusetts and Maine had a 26 and 28 cents per pack tax. Border effect purchases not explained in the Demand Equation can cause spatial autocorrelation among the disturbances.1
Piet Rietveld - One of the best experts on this subject based on the ideXlab platform.
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a meta analysis of the price elasticity of gasoline Demand a sur approach
Energy Economics, 2008Co-Authors: Martijn Brons, Peter Nijkamp, Eric Pels, Piet RietveldAbstract:Automobile gasoline Demand can be expressed as a multiplicative function of fuel efficiency, mileage per car and car ownership. This implies a linear relationship between the price elasticity of total fuel Demand and the price elasticities of fuel efficiency, mileage per car and car ownership. In this meta-analytical study we aim to investigate and explain the variation in empirical estimates of the price elasticity of gasoline Demand. A methodological novelty is that we use the linear relationship between the elasticities to develop a meta-analytical estimation approach based on a Seemingly Unrelated Regression (SUR) model with Cross Equation Restrictions. This approach enables us to combine observations of different elasticities and thus increase our sample size. Furthermore, it allows for a more detailed interpretation of our meta-regression results. The empirical results of the study demonstrate that the SUR approach leads to more precise results (i.e., lower standard errors) than a standard meta-analytical approach. We find that, with mean short run and long run price elasticities of - 0.34 and - 0.84, respectively, the Demand for gasoline is not very price sensitive. Both in the short and the long run, the impact of a change in the gasoline price on Demand is mainly driven by responses in fuel efficiency and mileage per car and to a slightly lesser degree by changes in car ownership. Furthermore, we find that study characteristics relating to the geographic area studied, the year of the study, the type of data used, the time horizon and the functional specification of the Demand Equation have a significant impact on the estimated value of the price elasticity of gasoline Demand.
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a meta analysis of the price elasticity of gasoline Demand a system of Equations approach
2006Co-Authors: Martijn Brons, Peter Nijkamp, Eric Pels, Piet RietveldAbstract:Automobile gasoline Demand can be expressed as a multiplicative function of fuel efficiency, mileage per car and car ownership. This implies a linear relationship between the price elasticity of total fuel Demand and the price elasticities of fuel efficiency, mileage per car and car ownership. In this meta-analytical study we aim to investigate and explain the variation in empirical estimates of the price elasticity of gasoline Demand. A methodological novelty is that we use the linear relationship between the elasticities to develop a meta-analytical estimation approach based on a system of Equations. This approach enables us to combine observations of different elasticities and thus increase our sample size. Furthermore it allows for a more detailed interpretation of our meta-regression results. The empirical results of the study demonstrate that the system of Equations approach leads to more precise results (i.e., lower standard errors) than a standard! meta-analytical approach. We find that, with a mean price elasticity of -0.53, the Demand for gasoline is not very price sensitive. The impact a change in the gasoline price on Demand is mainly driven by a response in fuel efficiency and car ownership and to a lesser degree by changes in the mileage per car. Furthermore, we find that study characteristics relating to the geographic area studied, the year of the study, the type of data used, the time horizon and the functional specification of the Demand Equation have a significant impact on the estimated value of the price elasticity of gasoline Demand. See 'A meta-analysis of the price elasticity of gasoline Demand. A SUR approach' in Energy Economics (2008). Volume 30, pages 2105-2122.
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a meta analysis of the price elasticity of gasoline Demand a system of Equations approach
Research Papers in Economics, 2006Co-Authors: Martijn Brons, Peter Nijkamp, Eric Pels, Piet RietveldAbstract:Automobile gasoline Demand can be expressed as a multiplicative function of fuel efficiency, mileage per car and car ownership. This implies a linear relationship between the price elasticity of total fuel Demand and the price elasticities of fuel efficiency, mileage per car and car ownership. In this meta-analytical study we aim to investigate and explain the variation in empirical estimates of the price elasticity of gasoline Demand. A methodological novelty is that we use the linear relationship between the elasticities to develop a meta-analytical estimation approach based on a system of Equations. This approach enables us to combine observations of different elasticities and thus increase our sample size. Furthermore it allows for a more detailed interpretation of our meta-regression results. The empirical results of the study demonstrate that the system of Equations approach leads to more precise results (i.e., lower standard errors) than a standard! meta-analytical approach. We find that, with a mean price elasticity of -0.53, the Demand for gasoline is not very price sensitive. The impact a change in the gasoline price on Demand is mainly driven by a response in fuel efficiency and car ownership and to a lesser degree by changes in the mileage per car. Furthermore, we find that study characteristics relating to the geographic area studied, the year of the study, the type of data used, the time horizon and the functional specification of the Demand Equation have a significant impact on the estimated value of the price elasticity of gasoline Demand.
Joaquim Vieira Ferreira Levy - One of the best experts on this subject based on the ideXlab platform.
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euro area money Demand measuring the opportunity costs appropriately
Social Science Research Network, 2001Co-Authors: Alessandro Calza, Dieter Gerdesmeier, Joaquim Vieira Ferreira LevyAbstract:The existence of a well-specified and stable relationship between money and prices has long been perceived as a prerequisite for the use of monetary aggregates in the conduct of monetary policy. This paper contributes to the ongoing discussion about the stability of euro area money Demand by constructing an own rate of return on euro area M3 and by analyzing its implications in a standard money Demand system. Over the sample period, one cointegrating vector relating real M3, real GDP and the spread between the short-term interest rate and the own rate of M3 can be identified and interpreted as a long-run euro area money Demand Equation. A dynamic money Demand system is subsequently estimated. Standard diagnostics stability tests and out-of-sample forecasts confirm the good statistical performance of the model.