An Econometric Assessment of Electricity Demand in the United States Using Panel Data and the Impact of Retail Competition on Prices


 Violet Elliott
 2 years ago
 Views:
Transcription
1 9 June 2015 An Econometric Assessment of Electricity Demand in the United States Using Panel Data and the Impact of Retail Competition on Prices By Dr. Agustin J. Ros This paper was originally presented at the Rutgers University Center for Research in Regulated Industries, 34th Annual Eastern Conference. Introduction Since the early 1970s electricity demand in the United States has been growing at an average annual rate of approximately 2%. In that period there have been major developments in the electricity sector including significant technological changes in generation services and the development of wholesale and retail competition. In this paper I use panel data covering 72 electricity distribution companies in the United States during the period from to econometrically estimate structural demand equations separately for residential, commercial, and industrial customers and to examine the impact that retail competition has had on electricity prices. I find the ownprice elasticity of demand for residential, commercial, and industrial consumers that are generally consistent with the published economics literature, ranging between and for residential demand, for commercial demand, and ranging between and for industrial demand. Regarding retail electricity competition, I econometrically examine the impact of the restructuring of the retail electricity sector in the US from the mid1990s. Since this period and up to 2009, a total of 21 states permitted retail customers (some states permitting only large industrial customers and some states also permitting smaller commercial and residential customers) to select their electricity generation supplier (retail competition) from a firm other than the incumbent electricity distribution company. As of 2009, 17 and 15 states still permitted retail competition for large and smaller customers, respectively. I estimate reducedform static and dynamic price equations controlling for demand and supply factors, and include a binary variable for those states and time periods where retail competition was permitted. I test the null hypothesis that retail competition had no statistically significant impact on real electricity prices. I find that retail electricity competition is associated with lower electricity prices for each customer class with the magnitude of the impact being greater for the larger customer classes.
2 Literature Review There is a large economic literature on electricity demand in the US and other countries. Using data on US residential electricity demand for , Silk and Joutz (1997) find that a 1% increase in electricity prices reduces electricity consumption by 0.62%. With respect to disposable income, they find a 1% increase in income leads to a 0.82% increase in electricity consumption. Using data on residential demand for electricity in the US, Dergiades and Tsoulfidis (2008) find short and longrun price elasticities of demand to be % and %, respectively. With regard to income, they find short and longrun income elasticities of demand to be 0.101% and 0.273%, respectively. Paul, Myers, and Palmer (2009) find US shortrun price elasticities of demand ranging between and (depending on customer class and region of the country), and longrun price elasticities of demand ranging between and (depending on customer class and region of the country). In the same paper, they summarize the results from previous studies, which I summarize below in Table 1. Table 1. Summary of OwnPrice Elasticities of Demand from the Literature Customer Class Reference Short Run Long Run Bohi and Zimmerman (1984) (consensus) Dahl and Roman (2004) Residential Supawat (2000) Espey and Espey (2004) Bernstein and Griffin (2005) Commercial Bohi and Zimmerman (1984) Bernstein and Griffin (2005) Bohi and Zimmerman (1984) Industrial Dahl and Roman (2004) Taylor (1977) All Dahl and Roman (2004) Source: Paul, Myers and Palmer (2009), Table 5 Finally, using data from the Korean service sector, Lim and Lim (2014) find the shortand longrun price elasticities of electricity demand to be and , respectively, and find short and longrun income elasticities of electricity demand to be and 1.090, respectively. With regard to the impact of electricity competition, a number of studies are focused on the wholesale sector but few studies focus on the retail sector. Regarding the former, Kleit and Terrell (2001) find that by eliminating technical inefficiencies, gasfired generation plant could reduce costs by up to 13%. Fabrizio et al. (2007) found evidence of reduced fuel and nonfuel expenses in fossilfueled plants in states that restructured their wholesale markets. Zhang (2007) finds that, in states that have restructured nuclearfueled plants, utilization is higher and operating costs are lower. 2
3 Regarding the impact of retail competition, a recent paper by Su (2014) uses a differenceindifference approach to estimate the policy impact for US states that restructured their electricity retail markets. Su finds that only residential customers have benefitted from significantly lower prices but not commercial or industrial customers. Furthermore, this benefit is transitory and disappears in the long run. Swadley and Yücel (2011) find that retail competition makes the market more efficient by lowering the markup of retail prices over wholesale costs, and it generally appears to lower prices in states with higher customer participation rates in retail choice. Data I use several different data sources for my study: (I) FERC Form 1 data that contains information on residential, commercial, and industrial revenues and sales volume; (II) data from the bureau of labor statistics on price indices used for deflating prices and other relevant variables; and (III) inputs and results from a total factor productivity study containing information on cost indices, and total factor productivity for the 72 US electricity firms that I use in this study. 1 In Table 2 below, I provide a description of the variables and data sources used for this study using average revenue per unit as a proxy for price. 2 Table 2. Description of Variables Variable Name Description Data Source Lntfp Natural log of index of total factor productivity TFP Study ln_rpres Natural log of deflated residential revenue per unit sales volume FERC Form 1, TFP Study, and author s calculations using US census region cpi and urban consumer as deflator ln_rpcom Natural log of deflated commercial revenue per unit sales volume FERC Form 1, TFP Study, and author s calculations using US census region cpi and urban consumer as deflator ln_rpindus Natural log of deflated industrial revenue per unit sales volume using FERC Form 1, TFP Study, and author s calculations US census region cpi and urban consumer as deflator ln_qres Natural log of residential sales volume FERC Form 1, TFP Study, and author s calculations ln_qcom Natural log of commercial sales volume FERC Form 1, TFP Study, and author s calculations ln_qindus Natural log of industrial sales volume FERC Form 1, TFP Study, and author s calculations labprcindex Real labor cost index FERC Form 1 and TFP Study caprcindex Real capital cost index FERC Form 1 and TFP Study t_hdd State heating degree day index National Oceanic and Atmospheric Administration t_cdd State cooling degree day index National Oceanic and Atmospheric Administration ln_pop Natural log of state population Bureau of Economic Analysis ln_income Natural log of real state personal income using US census region Bureau of Economic Analysis cpi and urban consumer as deflator ln_price_natural gas Natural log of real state natural gas price index using US census US Energy Information Administration region cpi and urban consumer as deflator compl Indicator variable 1 if competition for large industrial Author s construct customers is permitted comps Indicator variable 1 if competition Author s construct for residential and commercial customers is permitted ratecap Indicator variable 1 if state that permitted competition had a rate cap for residential and commercial customers Swadley and Yücel (2011) Table 1 3
4 In Table 3 below, I present summary statistics of the variables. Mean values over the period for deflated residential, commercial, and industry prices were kwh, 8.91 kwh, and 6.16 kwh, respectively. And over the time period, there was a downward trend in deflated prices for all customer classes beginning in the late 1980s and lasting through the early 2000s. Approximately 14% and 13% of observations of the data reflect retail competition for large and residential and commercial customers, respectively. Table 3. Summary Statistics Variable Obs Mean Std. Dev. Min Max lntfp unem ln_pop ln_rpres ln_rpcom ln_rpindus ln_qres ln_qcom ln_qindus labprcindex caprcindex t_hdd t_cdd ln_income ln_price_natural gas Compl Comps Rate cap Econometric Models Econometric estimation of US electricity demand I estimate demand equations for US electricity distribution services for a 72company panel sample from 1972 to 2009 to determine the price elasticity of demand, as well as the effects of other factors. I estimate demand equations separately for residential, commercial, and industrial electricity demand. In these demand equations, output (sales volume) is the lefthand side dependent variable. I am measuring how electricity output changes when other variables, such as price and income, change. The basic model is of the form: (1) y it = Y it γ + X it β + μ i + υ it 4
5 Where y it is the dependent variable (electricity consumption), Y it is a 1 x g 2 vector of observations on g 2 endogenous variables included as covariates (in my demand models, g 2 =1 since I assume that price is the only endogenous variables), and these variables are allowed to be correlated with the υ it, X it is a 1 x k 1 vector of observations on the exogenous variables included as covariates (such as income, population, price of natural gas, and heating and cooling degree days), γ is a g 2 x 1 vector of coefficients, β is a k 1 x 1 vector of coefficients, μ i is the individuallevel effect (i.e., the unobservable companylevel effects), and υ it is the disturbance term. I estimate demand models for each type of customer using four different estimators: fixedeffects, randomeffects, firstdifference, and the ArellanoBond estimator for dynamic models. The fixedeffect estimator fits the model after sweeping out the μ i by removing the panellevel means from each variable. The randomeffects estimator treats the μ i as random variables that are independent and identically distributed (i.i.d.) over the panels. The firstdifference estimator removes the μ i by fitting the model in first differences. The ArellanoBond model is a linear dynamic paneldata model that includes p lags of the dependent variables as covariates and contains unobserved panellevel effects, fixed or random. 3 I expect price to be endogenous, and for instruments I use data from the TFP Study of US distribution companies. Specifically, I use a deflated labor price index and a deflated capital price index. These two variables reflect changes in input costs for the 72 distribution companies over the period and are thus good candidates for instruments. In addition to price, I expect electricity demand to be positively related to population, real income, the state heating degree day index, the state cooling degree day index, and the deflated price of natural gas. 4 To capture the possibility of changing demand preferences over time, I include three decade binary variables. Residential demand models The first three models are static demand models, while the ArellanoBond model is a dynamic demand model. Table 4 contains the results of my static and dynamic residential demand equations. Since the lefthand side dependent variable is the log of residential demand, the coefficient on residential prices is the price elasticity of demand. The fixedeffects and randomeffects estimators provide very similar results for the price elasticity of demand and for all the variables in the model. The price elasticity of demand using the fixedeffects estimator is estimated to be and statistically significant (t statistic = 3.65). The price elasticity of demand using the randomeffects estimator is estimated to be and statistically significant (t statistic = 3.57). When I use the firstdifference estimator, the price elasticity of demand increases to (t statistic = 3.23). Finally, when I use the ArellanoBond dynamic estimator, the price elasticity of demand is estimated to be , and is estimated very precisely. 5 The econometric evidence, therefore, supports a price elasticity of demand for residential customers ranging between and These estimates are within the range found in the economic literature (see Section 2 above). With respect to the income elasticity of demand, the fixedeffects and randomeffects estimators also provide very similar results. The income elasticity of demand using the fixedeffects estimator is estimated to be and statistically significant (t statistic = 5.51). The income elasticity of demand using the randomeffects estimator is estimated to be and statistically significant (t statistic = 5.64). When I use the firstdifference estimator, the 5
6 income elasticity of demand decreases to (t statistic = 1.90). Finally, when I use the ArellanoBond dynamic estimator, the income elasticity of demand is estimated to be and is estimated very precisely. To control for changes in quantity demanded over time, I included three decade binary variables. All the decade variables are positive and statistically significant. Specifically, using the results from the fixedeffects estimator I find that, compared to the 1970s and holding all factors constant, residential demand in the 1980s, 1990s, and 2000s was approximately 10%, 12%, and 13% higher, respectively. The similarity in the coefficients suggests that, holding all factors in the model constant, residential demand has not changed much since Other variables in the model are population, the price of natural gas, and the heating and cooling degree day indices. With respect to population, the four models provide similar results: a 1% increase in population results in an increase in residential demand ranging between 0.786% and 0.873% and is estimated very precisely (in each case a pvalue of less than 0.001). I find natural gas to be a substitute for residential electricity consumption. A 1% increase in the real price of natural gas results in an increase in residential electricity consumption of approximately 0.090%. Finally, I find that the heating and cooling degree days index positively affects the demand for residential electricity. Table 4. Estimation of Static 2SLS and Dynamic Residential Demand Equations Using Panel Data Variable Fixed Effects Random Effects First Difference ArellanoBond ln_rpres *** *** *** decade_80s *** *** *** decade_90s *** *** *** decade_2000s *** *** ** ln_pop *** *** *** t_hdd *** *** *** t_cdd *** *** *** ln_income *** *** *** ln_price natural gas *** *** ln_rpres_cpi D ** decade_80s D decade_90s D decade_2000s D ln_pop D *** t_hdd D *** t_cdd D *** ln_income D ln_price natural gas D ln_qres L *** _cons *** *** *** *** N Wald χ 2 Prob > χ Prob > χ Prob > χ Prob > χ *p<0.05; **p<0.01; ***p<
7 Commercial demand models Table 5 contains the results of my static and dynamic commercial demand equations. The fixedeffects and randomeffects estimators provide very similar results for the price elasticity of demand and for all the variables in the model. The price elasticity of demand using the fixedeffects estimator is estimated to be but is not estimated precisely (t statistic = 1.71). The price elasticity of demand using the randomeffects estimator is estimated to be and is also not statistically significant (t statistic = 1.71). When I use the firstdifference estimator, the price elasticity of demand decreases to but is estimated with very poor precision (t statistic = 0.39). Finally, when I use the ArellanoBond dynamic estimator, the price elasticity of demand is and estimated very precisely. 7 The econometric evidence, therefore, supports a price elasticity of demand for commercial customers of , also within the findings in the economics literature (see Section 2). With respect to the income elasticity of demand, the fixedeffects and randomeffects estimators also provide very similar results. The income elasticity of demand using the fixedeffects estimator is estimated to be and statistically significant (t statistic = 3.90). The income elasticity of demand using the randomeffects estimator is estimated to be and statistically significant (t statistic = 3.98). When I use the firstdifference estimator, the income elasticity of demand decreases to (t statistic = 4.98). Finally, when I use the ArellanoBond dynamic estimator, the income elasticity of demand is estimated to be and is estimated very precisely. The econometric evidence thus supports an income elasticity of residential electricity demand ranging between and These estimates are within the range found in the economic literature (see Section 2). To control for changes in quantity demanded over time, I included three decade binary variables. All the decade variables are positive and statistically significant. Specifically, using the results from the fixedeffects estimator I find that, compared to the 1970s and holding all factors constant, commercial demand in the 1980s, 1990s, and 2000s was approximately 14%, 23%, and 20% higher, respectively. Other variables in the model are population, the price of natural gas, and the heating and cooling degree day indices. With respect to population, a 1% increase in population results in an increase in commercial demand ranging between 0.472% and 0.767%, depending on the model, and is estimated precisely, in three cases with a pvalue of less than I find evidence that natural gas is a substitute for commercial electricity demand in two of the four models, with a 1% increase in the real price of natural gas resulting in approximately a 0.10% increase in commercial demand. And I find some evidence that the heating and cooling degree days index positively impacts the demand for commercial electricity. 7
8 Table 5. Estimation of Static 2SLS and Dynamic Commercial Demand Equations Using Panel Data Variable Fixed Effects Random Effects First Difference ArellanoBond ln_rpcom_cpi *** decade_80s *** *** ** decade_90s *** *** * decade_2000s ** ** ln_pop *** *** *** t_hdd 4.084e e e06 t_cdd 1.707e e *** ln_income *** *** ** ln_price natural gas * * ln_rpcom_cpi D decade_80s D decade_90s D decade_2000s D * ln_pop D * t_hdd D * t_cdd D *** ln_income D *** ln_price natural gas D ln_qcom L *** _cons *** *** *** N Wald χ 2 Prob > χ Prob > χ Prob > χ Prob > χ *p<0.05; **p<0.01; ***p<0.001 Industrial demand models Table 6 contains the results of my static and dynamic industrial demand equations. The price elasticity of demand using the fixed and randomeffects estimator is contrary to economic theory, showing a positive price elasticity of demand. I therefore rely on the results from the firstdifference and ArellanoBond models. When I use the firstdifference estimator, I find the price elasticity of demand to be (t statistic = 2.80). When I utilize the ArellanoBond dynamic estimator, the price elasticity of demand is estimated to be and is estimated precisely. 8 The econometric evidence, therefore, supports a price elasticity of demand for industrial customers between and , also within the findings in the economics literature (see Section 2). With respect to the income elasticity of demand, the fixedeffects and randomeffects estimators also provide very similar results. The income elasticity of demand using the fixedeffects estimator is estimated to be and statistically significant (t statistic = 4.95). The income elasticity of demand using the randomeffects estimator is estimated to be and statistically significant (t statistic = 5.02). The income elasticity of demand using the 8
9 firstdifference estimator is significantly lower at (with a student t statistic = 4.26). When I use the ArellanoBond dynamic estimator, the income elasticity of demand is estimated to be 1.58 and is estimated precisely and closer to the estimates found from the fixed and randomeffects estimators. To control for changes in quantity demanded over time, I included three decade binary variables. The results are mixed. Three of the four models suggest that industrial electricity demand was lower in the 1980s (than in the 1970s) but higher in the 1990s and 2000s. The remaining model, however, suggests that industrial electricity demand was lower in each decade compared to the 1970s. Other variables in the model are population, the price of natural gas, and the heating and cooling degree day indices. With respect to population, the fixed and random effects model finds that a 1% increase in population results in an increase in industrial demand of 0.361% and 0.338%, respectively while for the firstdifference model a 1% increase in population results in an increase in industrial demand range of 0.882%. I find evidence in the ArellanoBond model that natural gas is a complement for industrial electricity consumption. A 1% increase in the real price of natural gas results in a decrease in industrial electricity consumption of approximately 1.236%. Finally, I do not find strong evidence that the heating and cooling degree days index impacts the demand for industrial electricity. Table 6. Estimation of static 2SLS and dynamic industrial demand equations using panel data Variable Fixed Effects Random Effects First Difference ArellanoBond ln_rpindus_cpi *** *** * decade_80s ** ** *** decade_90s ** ** *** decade_2000s *** ln_pop ** ** t_hdd e e06 t_cdd e06 ln_income *** *** * ln_price natural gas * ln_rpindus_cpi D ** decade_80s D ** decade_90s D * decade_2000s D * ln_pop D ** t_hdd D e06 t_cdd D ln_income D *** ln_price natural gas D ln_qindus L *** _cons *** N Wald χ 2 Prob > χ Prob > χ Prob > χ Prob > χ *p<0.05; **p<0.01; ***p<
10 Econometric Estimation of US Electricity Price Equations In this section, I estimate reducedform price equations for residential, commercial, and electricity customer classes. The lefthand side dependent variable is log of real price and is the same price variable that I used above for the demand equations. The righthand side independent variable comps is a binary variable with a value of one if the state permitted retail competition for residential and commercial (small) customers. I use this variable for the residential and commercial price equations. For the industrial price equations, the variable compl is a binary variable with a value of one if the state permitted retail competition for industrial (large) consumers. I estimate fixed and random effects models assuming that comps and compl are exogenous. In addition, I again estimate dynamic models using the ArellanoBond estimator and provide two results: the first treats the competition variable as exogenous, and the second assumes it is endogenous and uses the lagged values of the competition variable as instruments. Other independent variables in my reducedform price equation include binary variables for the 1980s, 1990s, and 2000s, log of population, log of the firm s total factor productivity (lntfp), heating and cooling degree day indices, real personal income, and the log of real price of natural gas. I also include the variable ratecap to control for the fact that some of the states that permitted competition imposed a rate cap on electricity prices for small customers (residential and commercial) for a period of time. Thus, it is important to control for this effect, otherwise the effect would be included within the comps coefficient. The variable ratecap is a binary variable with one in those states that permitted competition and had a rate cap in the year in question. Residential price equations Table 7 contains the results of my residential price model. The fixed and random effects estimators provide very similar results (and each having a pvalue of less than 0.001): holding all other factors constant, residential prices were approximately 8% lower in those states that permitted retail competition for residential and commercial consumers. The ArellanoBond estimator assuming that comps is exogenous (model 3) indicates that residential prices were approximately 3% lower in those states that permitted retail competition for residential and commercial consumers, but the effect was not significant. When I use the ArellanoBond estimator and consider comps as endogenous, I find the impact is practically zero and is not estimated precisely. Based upon these results, while there is some evidence that residential electricity competition is associated with lower residential prices, additional work should be performed on finding suitable instruments for the comps variable in order to ensure that the parameter estimates for comps are unbiased. Other significant findings include the impact of lntfp and personal income on residential electricity prices. In each of the models, an increase in tfp of 1% results in a decrease in residential prices, ranging from % to %. In each of the models, an increase in personal income results in a decrease in prices, ranging from % to %. 10
11 Table 7. Estimation of Static and Dynamic Residential Price Equations Using Panel Data Variable Fixed Effects (1) Random Effects (2) ArellanoBond (3) ArellanoBond (4) comps .089*** .084*** ratecap .105*** .101*** .085*** .081*** decade_80s.045***.043***.02***.02*** decade_90s .057*** .065*** decade_2000s .075*** .098***.06***.056*** ln_pop *** Lntfp .22*** .253*** .087*** .084*** t_hdd 2.2e05* 1.2e05* 6.78e e06 t_cdd 4.3e05*** 5.2e05*** 2.15e05* 2.5e05* ln_income .338*** .258*** .186*** .191*** ln_price natural gas .079*** *.045* ln_rpres_cpi L1..773***.780*** _cons ** 1.53** N *p<0.05; **p<0.01; ***p<0.001 Commercial price equations Table 8 contains the results of my commercial price model. The fixed and randomeffects estimators provide very similar results (each having a pvalue of less than 0.001): holding all other factors constant, commercial prices were approximately 16% lower in those states that permitted retail competition for residential and commercial consumers. The ArellanoBond estimator, assuming that comps is exogenous (model 3), indicates that commercial prices were approximately 19% lower in those states that permitted retail competition for residential and commercial consumers (with a pvalue of less than 0.001). When I use the ArellanoBond estimator and consider comps as endogenous, the impact is approximately 19%. Based upon these results, there is evidence that commercial electricity competition is associated with lower commercial prices. Other significant findings include the impact of lntfp and personal income on residential electricity prices. In each of the models, an increase in tfp results in a decrease in prices, ranging from % to %. In each of the models, an increase in personal income results in a decrease in prices, ranging from 0.68% to 0.29%. 11
12 Table 8. Estimation of Static and Dynamic Commercial Price Equations Using Panel Data Variable Fixed Effects (1) Random Effects (2) ArellanoBond (3) ArellanoBond (4) comps .176*** .171*** .038*** .035*** ratecap * decade_80s decade_90s .108*** .119*** decade_2000s .105*** .134***.051***.05*** ln_pop.075*.079*** lntfp .236*** .278*** .058*** .056** t_hdd 2.5e05** 1.0e e e06 t_cdd 3.2e e05* 2.2e e05 ln_income .683*** .567*** .296*** .292*** ln_price natural gas .087** * ln_rpcom_cpi L1..811***.821*** _cons 3.89*** 2.55*** 2.09*** 2.06*** N *p<0.05; **p<0.01; ***p<0.001 Table 9 contains the results of my industrial price model. The fixed and randomeffects estimators provide very similar results (each having a pvalue of less than 0.001): holding all other factors constant, industrial prices were approximately 24% lower in those states that permitted retail competition for industrial consumers. The ArellanoBond estimator indicates that industrial prices were approximately 30% lower in those states that permitted retail competition for industrial consumers (with a pvalue of less than 0.001). When I use the ArellanoBond estimator and consider compl as endogenous, the impact is approximately 29%. Based upon these results, there is evidence that industrial electricity competition is associated with lower industrial prices. Other significant findings include the impact of tfp and personal income on residential electricity prices. In each of the models, an increase in tfp results in a decrease in prices, ranging from % to %. In each of the models, an increase in personal income results in a decrease in prices, ranging from % to %. 12
13 Table 9. Estimation of Static and Dynamic Industrial Price Equations Using Panel Data Variable Fixed Effects (1) Random Effects (2) ArellanoBond (5) ArellanoBond (6) Compl .269*** .263*** *** *** decade_80s.0861***.0844*** decade_90s *** *** decade_2000s *.0813***.0799*** ln_pop.175***.0967*** Lntfp .193*** .263*** .075** ** t_hdd 3.0e05* 8.8e e e06 t_cdd 4.5e e05* 1.1e e05 ln_income .811*** .616*** .379*** .376*** ln_price natural gas .149*** ln_rpindus_cpi L1..791***.803*** _cons 3.38*** 2.44*** 3.39*** 3.37*** N *p<0.05; **p<0.01; ***p<0.001 References T. Dergiades and L. Tsoulfidis, Estimating residential demand for electricity in the United States, , Energy Economics, K. Lim and S. Lim, Short and longrun elasticities of electricity demand in the Korean service sector, Energy Policy, A. Paul, E. Myers, and K. Palmer, A Partial Adjustment Model of US Electricity Demand by Region, Season and Sector, Resources for the Future Discussion Paper, Available at J. Silk and F. Joutz, Short and long run elasticities in US residential electricity demand: a cointegration approach, Energy Economics, X. Su, Have Customers Benefited from Electricity Retail Competition, Journal of Regulatory Economics, 2015 (forthcoming). A. Swadley and M. Yücel, Did residential electricity rates fall after retail competition? A dynamic panel analysis, Energy Policy,
14 Notes 1 I was the coauthor (with Jeff Makholm) of an expert report entitled Total Factor Productivity in the United States Electricity Sector from We were expert witnesses on behalf of the Alberta Public Utility Commission in a proceeding in 2012 whose objective was setting tariffs for electricity and gas distribution companies. Our analysis was used to establish the Xfactor in a price cap plan. Throughout this paper, I refer to the study as TFP Study. 2 I did not have data on actual tariffs for the different customer classes over the time period. Instead, I use average revenue per unit as a proxy for price and assume that some errors exist in variable (EIV). Nevertheless, EIV should not present a significant problem because I treat the price variable as (jointly) endogenous in my structural demand equations, and I explicitly model price in my reducedform price equations. 3 The model is constructed so that by definition the unobserved panellevel effects are correlated with the lagged dependent variables, thus making standard estimators inconsistent. Arellano and Bond derived a consistent generalized methodofmoments (GMM) estimator for the parameters of the model. 4 I consider natural gas as a substitute for electricity and expect the crossprice elasticity of demand to be positive. 5 The total effect is /( ). 6 I also estimate every model by including interaction terms between the price variable and each decade variable to test whether there is evidence that the price elasticity of residential electricity demand changed significantly during the decades. For the fixedeffects model, the price elasticity of demand for the 1980s, 1990s, and 2000s ranged between to , similar to the models without the interaction effects but the parameters were not estimated precisely, none being significant at the 5% level of statistical significance. Results for the randomeffects estimator are similar. 7 The total effect is /( ). 8 The total effect is /( ).
15 About NERA NERA Economic Consulting (www.nera.com) is a global firm of experts dedicated to applying economic, finance, and quantitative principles to complex business and legal challenges. For over half a century, NERA s economists have been creating strategies, studies, reports, expert testimony, and policy recommendations for government authorities and the world s leading law firms and corporations. We bring academic rigor, objectivity, and real world industry experience to bear on issues arising from competition, regulation, public policy, strategy, finance, and litigation. NERA s clients value our ability to apply and communicate stateoftheart approaches clearly and convincingly, our commitment to deliver unbiased findings, and our reputation for quality and independence. Our clients rely on the integrity and skills of our unparalleled team of economists and other experts backed by the resources and reliability of one of the world s largest economic consultancies. With its main office in New York City, NERA serves clients from more than 25 offices across North America, Europe, and Asia Pacific. Contact For further information and questions, please contact the author: Dr. Agustin Ros Vice President The opinions expressed herein do not necessarily represent the views of NERA Economic Consulting or any other NERA consultant. Please do not cite without explicit permission from the author.
A Meta Analysis of U.S. Residential, Industrial, and Commercial Electricity Demand
A Meta Analysis of U.S. Residential, Industrial, and Commercial Electricity Demand Prepared for the 31 st st USAEE/IAEE North American Conference Nov. 7, 1  Austin, Texas By Carol Dahl Division of Economics
More informationElectricity Prices, Income and Residential Electricity Consumption
Electricity Prices, Income and Residential Electricity Consumption Yanming Sun School of Urban and Regional Science East China Normal University August, 2015 Dr. Yanming Sun School of Urban and Regional
More informationNERA Analysis of Energy Supplier Margins
7 December 2009 NERA Analysis of Energy Supplier Margins By Graham Shuttleworth Even though wholesale energy prices have fallen recently, gas and electricity suppliers are earning very little margin on
More informationInsurance Coverage Towers and Predicted Settlements
22 February 2012 Insurance Coverage Towers and Predicted Settlements By Dr. Patrick Conroy and Dr. Jordan Milev Introduction Insurance policy underwriters and litigators in securities class actions place
More informationDISCUSSION PAPER. A Partial Adjustment Model of U.S. Electricity Demand by Region, Season, and Sector. Anthony Paul, Erica Myers, and Karen Palmer
DISCUSSION PAPER April 2009 RFF DP 0850 A Partial Adjustment Model of U.S. Electricity Demand by Region, Season, and Sector Anthony Paul, Erica Myers, and Karen Palmer 1616 P St. NW Washington, DC 20036
More informationTechnical Efficiency Accounting for Environmental Influence in the Japanese Gas Market
Technical Efficiency Accounting for Environmental Influence in the Japanese Gas Market Sumiko Asai Otsuma Women s University 271, Karakida, Tama City, Tokyo, 26854, Japan asai@otsuma.ac.jp Abstract:
More informationAsbestos Payments Pulled Back Slightly in 2012, although Average Payments per Resolved Claim Remained High
3 June 2013 Asbestos Payments Pulled Back Slightly in 2012, although Average Payments per Resolved Claim Remained High Snapshot of Recent Trends in Asbestos Litigation: 2013 Update By Mary Elizabeth Stern
More informationThe Effectiveness of Mobile Wireless Service as a Competitive Constraint on Landline Pricing: Was the DOJ Wrong?
11 December 2008 The Effectiveness of Mobile Wireless Service as a Competitive Constraint on Landline Pricing: Was the DOJ Wrong? William E. Taylor and Harold Ware 1 The US Department of Justice (DOJ)
More informationRegional Differences in the PriceElasticity of Demand for Energy
National Renewable Energy Laboratory Innovation for Our Energy Future A national laboratory of the U.S. Department of Energy Office of Energy Efficiency & Renewable Energy Regional Differences in the PriceElasticity
More informationMarkups and FirmLevel Export Status: Appendix
Markups and FirmLevel Export Status: Appendix De Loecker Jan  Warzynski Frederic Princeton University, NBER and CEPR  Aarhus School of Business Forthcoming American Economic Review Abstract This is
More informationTesting for serial correlation in linear paneldata models
The Stata Journal (2003) 3, Number 2, pp. 168 177 Testing for serial correlation in linear paneldata models David M. Drukker Stata Corporation Abstract. Because serial correlation in linear paneldata
More informationResponse of Residential Electricity Demand to Price: The Effect of Measurement Error
Response of Residential Electricy Demand to Price: The Effect of Measurement Error Anna Alberini, Massimo Filippini CEPE Working Paper No. 75 July 2010 CEPE Zurichbergstrasse 18 (ZUE E) CH8032 Zurich
More informationESTIMATING AN ECONOMIC MODEL OF CRIME USING PANEL DATA FROM NORTH CAROLINA BADI H. BALTAGI*
JOURNAL OF APPLIED ECONOMETRICS J. Appl. Econ. 21: 543 547 (2006) Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/jae.861 ESTIMATING AN ECONOMIC MODEL OF CRIME USING PANEL
More informationDepartment of Economics Session 2012/2013. EC352 Econometric Methods. Solutions to Exercises from Week 10 + 0.0077 (0.052)
Department of Economics Session 2012/2013 University of Essex Spring Term Dr Gordon Kemp EC352 Econometric Methods Solutions to Exercises from Week 10 1 Problem 13.7 This exercise refers back to Equation
More informationAsbestos Payments Continued to Pull Back in 2013
22 May 2014 Asbestos Payments Continued to Pull Back in 2013 Snapshot of Recent Trends in Asbestos Litigation: 2014 Update By Mary Elizabeth Stern and Lucy P. Allen 1 Each year, we conduct an annual review
More informationDID RESIDENTIAL ELECTRICITY RATES FALL AFTER RETAIL COMPETITION? A DYNAMIC PANEL ANALYSIS
DID RESIDENTIAL ELECTRICITY RATES FALL AFTER RETAIL COMPETITION? A DYNAMIC PANEL ANALYSIS MINE YÜCEL AND ADAM SWADLEY RESEARCH DEPARTMENT WORKING PAPER 1105 Federal Reserve Bank of Dallas Did Residential
More informationSnapshot of Recent Trends in Asbestos Litigation
16 June 2009 Snapshot of Recent Trends in Asbestos Litigation By Lucy P. Allen and Mary Elizabeth C. Stern* Over the past few years, the asbestos litigation environment has undergone many changes, including
More informationINFRASTRUCTURE, SAFETY, AND ENVIRONMENT
INFRASTRUCTURE, SAFETY, AND ENVIRONMENT THE ARTS CHILD POLICY CIVIL JUSTICE EDUCATION ENERGY AND ENVIRONMENT This PDF document was made available from www.rand.org as a public service of the RAND Corporation.
More informationDemand, Supply and Elasticity
Demand, Supply and Elasticity CHAPTER 2 OUTLINE 2.1 Demand and Supply Definitions, Determinants and Disturbances 2.2 The Market Mechanism 2.3 Changes in Market Equilibrium 2.4 Elasticities of Supply and
More informationINDIRECT INFERENCE (prepared for: The New Palgrave Dictionary of Economics, Second Edition)
INDIRECT INFERENCE (prepared for: The New Palgrave Dictionary of Economics, Second Edition) Abstract Indirect inference is a simulationbased method for estimating the parameters of economic models. Its
More informationEstimates of the Price Elasticities of Natural Gas Supply and Demand in the United States
MPRA Munich Personal RePEc Archive Estimates of the Price Elasticities of Natural Gas Supply and Demand in the United States Vipin Arora 6. March 2014 Online at http://mpra.ub.unimuenchen.de/54232/ MPRA
More informationMoney Market Mutual Funds: Stress Testing and the New Regulatory Requirements
16 June 2015 Money Market Mutual Funds: Stress Testing and the New Regulatory Requirements By Dr. Jeremy Berkowitz, Dr. Patrick E. Conroy and Dr. Jordan Milev In July 2014, the Securities and Exchange
More informationFinding the GAAP in FCPA Enforcement: Challenges in Identifying the Impact of Alleged Bribery in Financial Statements
13 May 2013 Part I of a NERA Accounting Insights Series Finding the GAAP in FCPA Enforcement: Challenges in Identifying the Impact of Alleged Bribery in Financial Statements By Raymund Wong, CFA, CPA,
More informationGlobal Services and Capabilities
Global Services and Capabilities Our team of experts offers an unmatched combination of economic credentials, industry expertise, and testifying experience. GLOBAL SERVICES AND CAPABILITIES Insight in
More informationSnapshot of Recent Trends in Asbestos Litigation 2011 Update
21 July 2011 Snapshot of Recent Trends in Asbestos Litigation 2011 Update By Mary Elizabeth C. Stern, Lucy P. Allen, and Adelina Halim* Using publicly available data, we analyzed trends in asbestosrelated
More informationThe LifeCycle Motive and Money Demand: Further Evidence. Abstract
The LifeCycle Motive and Money Demand: Further Evidence Jan Tin Commerce Department Abstract This study takes a closer look at the relationship between money demand and the lifecycle motive using panel
More informationHave Customers Benefited from Electricity Retail Competition? *
Have Customers Benefited from Electricity Retail Competition? * Xuejuan Su October 2014 Abstract Compared to traditional costofservice (COS) regulation, electricity retail competition may lead to lower
More informationMethodology For Illinois Electric Customers and Sales Forecasts: 20162025
Methodology For Illinois Electric Customers and Sales Forecasts: 20162025 In December 2014, an electric rate case was finalized in MEC s Illinois service territory. As a result of the implementation of
More informationPRICE EFFECTS OF INDEPENDENT TRANSMISSION SYSTEM OPERATORS IN THE UNITED STATES ELECTRICITY MARKET
PRICE EFFECTS OF INDEPENDENT TRANSMISSION SYSTEM OPERATORS IN THE UNITED STATES ELECTRICITY MARKET Theodore J. Kury 1 Abstract In 1996, the Federal Energy Regulatory Commission (FERC) sought to remove
More informationFIGURE 1 AVERAGE UNLEADED RETAIL GAS PRICE, SAN DIEGO COUNTY, SEPTOCT 2012
Shortrun Driver Response to a Gasoline Price Spike: Evidence from San Diego, CA Andrew Narwold University of San Diego Dirk Yandell University of San Diego Drivers response to an unexpected gasoline price
More informationHave Renewable Portfolio Standards Raised Electricity Rates? Evidence from U.S. Electric Utilities
Have Renewable Portfolio Standards Raised Electricity Rates? Evidence from U.S. Electric Utilities Constant I. Tra CBER and Department of Economics University of Nevada, Las Vegas June 05, 2009 Contact:
More informationHave Renewable Portfolio Standards Raised Electricity Rates? Evidence from U.S. Electric Utilities
Have Renewable Portfolio Standards Raised Electricity Rates? Evidence from U.S. Electric Utilities Constant I. Tra CBER and Department of Economics University of Nevada, Las Vegas June 05, 2009 Contact:
More informationServices and Capabilities. Financial Risk Management
Services and Capabilities Financial Risk Management Our team of experts offers an unmatched combination of economic credentials, industry expertise, and testifying experience. Financial Risk Management
More informationESTIMATING AVERAGE TREATMENT EFFECTS: IV AND CONTROL FUNCTIONS, II Jeff Wooldridge Michigan State University BGSE/IZA Course in Microeconometrics
ESTIMATING AVERAGE TREATMENT EFFECTS: IV AND CONTROL FUNCTIONS, II Jeff Wooldridge Michigan State University BGSE/IZA Course in Microeconometrics July 2009 1. Quantile Treatment Effects 2. Control Functions
More informationShortTerm Energy Outlook Supplement: Summer 2013 Outlook for Residential Electric Bills
ShortTerm Energy Outlook Supplement: Summer 2013 Outlook for Residential Electric Bills June 2013 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 This report
More informationRegional Shortterm Electricity Consumption Models
Regional Shortterm Electricity Consumption Models Prepared for 25 th Annual North American Conference of the USAEE/IAEE, Denver September 1821, 25 Frederick L. Joutz, Department of Economics The George
More informationAn Empirical Analysis of Determinants of Commercial and Industrial Electricity Consumption
1 Business and Economics Journal, Volume 2010: BEJ7 An Empirical Analysis of Determinants of Commercial and Industrial Electricity Consumption Richard J Cebula*, Nate Herder 1 *BJ Walker/Wachovia Professor
More informationIntegrated Resource Plan
Integrated Resource Plan March 19, 2004 PREPARED FOR KAUA I ISLAND UTILITY COOPERATIVE LCG Consulting 4962 El Camino Real, Suite 112 Los Altos, CA 94022 6509629670 1 IRP 1 ELECTRIC LOAD FORECASTING 1.1
More informationAn Introduction to Time Series Regression
An Introduction to Time Series Regression Henry Thompson Auburn University An economic model suggests examining the effect of exogenous x t on endogenous y t with an exogenous control variable z t. In
More informationELECTRICITY DEMAND DARWIN ( 19901994 )
ELECTRICITY DEMAND IN DARWIN ( 19901994 ) A dissertation submitted to the Graduate School of Business Northern Territory University by THANHTANG In partial fulfilment of the requirements for the Graduate
More informationDEPARTMENT OF ECONOMICS CREDITOR PROTECTION AND BANKING SYSTEM DEVELOPMENT IN INDIA
DEPARTMENT OF ECONOMICS CREDITOR PROTECTION AND BANKING SYSTEM DEVELOPMENT IN INDIA Simon Deakin, University of Cambridge, UK Panicos Demetriades, University of Leicester, UK Gregory James, University
More informationThe impact of energy prices on energy efficiency: Evidence from the UK refrigerator market
Presentation Toulouse, June 4, 2014 The impact of energy prices on energy efficiency: Evidence from the UK refrigerator market François Cohen*, Matthieu Glachant** and Magnus Söderberg** * GRI, LSE, **CERNA,
More informationTrends in Wage and Hour Settlements: 2015 Update
14 July 2015 Trends in Wage and Hour Settlements: 2015 Update By Dr. Stephanie Plancich, Neil Fanaroff, and Janeen McIntosh In wage and hour litigation, current and/or former employees allege unpaid work,
More informationDamage Estimation in Wrongful Termination Cases: Impact of the Great Recession
29 March 2012 Damage Estimation in Wrongful Termination Cases: Impact of the Great Recession By Dr. Laila Haider and Dr. Stephanie Plancich 1 Introduction The recent financial crisis was marked by the
More informationChapter 4: Vector Autoregressive Models
Chapter 4: Vector Autoregressive Models 1 Contents: Lehrstuhl für Department Empirische of Wirtschaftsforschung Empirical Research and und Econometrics Ökonometrie IV.1 Vector Autoregressive Models (VAR)...
More informationTEMPORAL CAUSAL RELATIONSHIP BETWEEN STOCK MARKET CAPITALIZATION, TRADE OPENNESS AND REAL GDP: EVIDENCE FROM THAILAND
I J A B E R, Vol. 13, No. 4, (2015): 15251534 TEMPORAL CAUSAL RELATIONSHIP BETWEEN STOCK MARKET CAPITALIZATION, TRADE OPENNESS AND REAL GDP: EVIDENCE FROM THAILAND Komain Jiranyakul * Abstract: This study
More informationThe Relationship between Electricity Prices and Electricity Demand, Economic Growth, and Employment DRAFT REPORT
The Relationship between Electricity Prices and Electricity Demand, Economic Growth, and Employment DRAFT REPORT Prepared for: Kentucky Department for Energy Development and Independence Coal Education
More informationChapter 10: Basic Linear Unobserved Effects Panel Data. Models:
Chapter 10: Basic Linear Unobserved Effects Panel Data Models: Microeconomic Econometrics I Spring 2010 10.1 Motivation: The Omitted Variables Problem We are interested in the partial effects of the observable
More informationChapter 6: Multivariate Cointegration Analysis
Chapter 6: Multivariate Cointegration Analysis 1 Contents: Lehrstuhl für Department Empirische of Wirtschaftsforschung Empirical Research and und Econometrics Ökonometrie VI. Multivariate Cointegration
More informationRegional Shortterm Electricity Consumption Models
Regional Shortterm Electricity Consumption Models Prepared for Brown Bag Seminar on Forecasting of the Federal Forecasters Consortium (FFC), in alliance with Research Program on Forecasting at The George
More informationARKANSAS PUBLIC SERVICE COMMISSYF cc7 DOCKET NO. 001 90U IN THE MATTER OF ON THE DEVELOPMENT OF COMPETITION IF ANY, ON RETAIL CUSTOMERS
ARKANSAS PUBLIC SERVICE COMMISSYF cc7 L I :b; Ir '3, :I: 36 DOCKET NO. 001 90U 1.. T 3.  " ~...ij IN THE MATTER OF A PROGRESS REPORT TO THE GENERAL ASSEMBLY ON THE DEVELOPMENT OF COMPETITION IN ELECTRIC
More informationRestructuring European electricity markets  a panel data analysis
NOTICE: this is the author s version of a work that was accepted for publication in Utilities Policy. Changes resulting from the publishing process, such as peer review, editing, corrections, structural
More information1 Demand Estimation. Empirical Problem Set # 2 Graduate Industrial Organization, Fall 2005 Glenn Ellison and Stephen Ryan
Empirical Problem Set # 2 Graduate Industrial Organization, Fall 2005 Glenn Ellison and Stephen Ryan 1 Demand Estimation The intent of this problem set is to get you familiar with Stata, if you are not
More informationThe marginal cost of rail infrastructure maintenance; does more data make a difference?
The marginal cost of rail infrastructure maintenance; does more data make a difference? Kristofer Odolinski, JanEric Nilsson, Åsa Wikberg Swedish National Road and Transport Research Institute, Department
More informationFrom the help desk: Swamy s randomcoefficients model
The Stata Journal (2003) 3, Number 3, pp. 302 308 From the help desk: Swamy s randomcoefficients model Brian P. Poi Stata Corporation Abstract. This article discusses the Swamy (1970) randomcoefficients
More informationThe price elasticity of electricity demand in South Australia
The price elasticity of electricity demand in South Australia Shu Fan and Rob Hyndman Business and Economic Forecasting Unit, Monash University, Clayton, Victoria 3168, Australia Email: Shu.fan@buseco.monash.edu.au;
More informationDefense Costs Dropped in 2014, While Claim Filings, Dismissal Rates, and Indemnity Dollars Remained Steady
4 June 2015 Defense Costs Dropped in 2014, While Claim Filings, Dismissal Rates, and Indemnity Dollars Remained Steady Snapshot of Recent Trends in Asbestos Litigation: 2015 Update By Mary Elizabeth Stern
More informationCorrelated Random Effects Panel Data Models
INTRODUCTION AND LINEAR MODELS Correlated Random Effects Panel Data Models IZA Summer School in Labor Economics May 1319, 2013 Jeffrey M. Wooldridge Michigan State University 1. Introduction 2. The Linear
More informationSYSTEMS OF REGRESSION EQUATIONS
SYSTEMS OF REGRESSION EQUATIONS 1. MULTIPLE EQUATIONS y nt = x nt n + u nt, n = 1,...,N, t = 1,...,T, x nt is 1 k, and n is k 1. This is a version of the standard regression model where the observations
More informationDepartment of Econometrics and Business Statistics
ISSN 1440771X Australia Department of Econometrics and Business Statistics http://www.buseco.monash.edu.au/depts/ebs/pubs/wpapers/ The price elasticity of electricity demand in South Australia Shu Fan
More informationEconometric analysis of the Belgian car market
Econometric analysis of the Belgian car market By: Prof. dr. D. Czarnitzki/ Ms. Céline Arts Tim Verheyden Introduction In contrast to typical examples from microeconomics textbooks on homogeneous goods
More informationBusiness Cycles, Theory and Empirical Applications
Business Cycles, Theory and Empirical Applications Seminar Presentation Country of interest France Jan Krzyzanowski June 9, 2012 Table of Contents Business Cycle Analysis Data Quantitative Analysis Stochastic
More informationHow Does Political Instability Affect Economic Growth?
WP/11/12 How Does Political Instability Affect Economic Growth? Ari Aisen and Francisco Jose Veiga 2010 International Monetary Fund WP/11/12 IMF Working Paper Middle East and Central Asia Department How
More informationRecent patterns in natural gas prices have raised
Stephen P. A. Brown Assistant Vice President and Senior Economist Mine K. Yücel Senior Economist and Policy Advisor The Pricing of Natural Gas in U.S. Markets Recent patterns in natural gas prices have
More informationExamining the effects of exchange rates on Australian domestic tourism demand: A panel generalized least squares approach
19th International Congress on Modelling and Simulation, Perth, Australia, 12 16 December 2011 http://mssanz.org.au/modsim2011 Examining the effects of exchange rates on Australian domestic tourism demand:
More informationDemand for industrial and commercial electricity: evidence from Japan
Otsuka Journal of Economic Structures (2015) 4:9 DOI 10.1186/s4000801500218 RESEARCH Open Access Demand for industrial and commercial electricity: evidence from Japan Akihiro Otsuka Correspondence:
More informationServices and Capabilities. Financial Services Transfer Pricing
Services and Capabilities Financial Services Transfer Pricing Our team of experts offers an unmatched combination of economic credentials, industry expertise, and testifying experience. FINANCIAL SERVICES
More informationServices and Capabilities. Transfer Pricing Services
Services and Capabilities Transfer Pricing Services Our team of experts offers an unmatched combination of economic credentials, industry expertise, and testifying experience. Transfer Pricing Services
More informationThe Impact of Fuel Costs on Electric Power Prices
The Impact of Fuel Costs on Electric Power Prices by Kenneth Rose 1 June 2007 1 Kenneth Rose is an independent consultant and a Senior Fellow with the Institute of Public Utilities (IPU) at Michigan State
More informationRebalancing Act: A Primer on Leveraged and Inverse ETFs
7 October 2009 Rebalancing Act: A Primer on Leveraged and Inverse ETFs By Raymund Wong, CFA, CPA, ABV and Kara Hargadon* Overview A leveraged exchangetraded fund (ETF) is a financial instrument that seeks
More informationTHE U.S. CURRENT ACCOUNT: THE IMPACT OF HOUSEHOLD WEALTH
THE U.S. CURRENT ACCOUNT: THE IMPACT OF HOUSEHOLD WEALTH Grant Keener, Sam Houston State University M.H. Tuttle, Sam Houston State University 21 ABSTRACT Household wealth is shown to have a substantial
More informationStatistics in Retail Finance. Chapter 2: Statistical models of default
Statistics in Retail Finance 1 Overview > We consider how to build statistical models of default, or delinquency, and how such models are traditionally used for credit application scoring and decision
More informationDeterminants and development of electricity consumption of German households over time*
Determinants and development of electricity consumption of German households over time* *This is work in progress, please do not site without permission from the author. Dragana Nikodinoska 1 Abstract
More informationThe Art of Turning Wholesale Rates into Retail Rates. Paul Garcia The Prime Group, LLC
The Art of Turning Wholesale Rates into Retail Rates Larry Feltner/ Paul Garcia The Prime Group, LLC Wholesale Rates are Important! Represent 60%  70% of coops costs Based on structure, can promote or
More informationConsumer Protection and Regulatory Changes in the DoddFrank Bill
31 August 2010 Part II of A NERA Insights Series Consumer Protection and Regulatory Changes in the DoddFrank Bill By Dr. Ethan CohenCole Summary On 21 July 2010, President Obama signed into law the DoddFrank
More informationElasticities can be estimated from records of past experience or test markets by the statistical technique of multiple regression.
Chapter 3 Elasticity Chapter 3: Elasticity CHAPTER SUMMARY The elasticity of demand measures the responsiveness of demand to changes in a factor that affects demand. Elasticities can be estimated for price,
More informationRegression Analysis. Data Calculations Output
Regression Analysis In an attempt to find answers to questions such as those posed above, empirical labour economists use a useful tool called regression analysis. Regression analysis is essentially a
More informationDo Jobs In Export Industries Still Pay More? And Why?
Do Jobs In Export Industries Still Pay More? And Why? by David Riker Office of Competition and Economic Analysis July 2010 Manufacturing and Services Economics Briefs are produced by the Office of Competition
More informationWooldridge, Introductory Econometrics, 4th ed. Chapter 15: Instrumental variables and two stage least squares
Wooldridge, Introductory Econometrics, 4th ed. Chapter 15: Instrumental variables and two stage least squares Many economic models involve endogeneity: that is, a theoretical relationship does not fit
More informationFrom the help desk: Bootstrapped standard errors
The Stata Journal (2003) 3, Number 1, pp. 71 80 From the help desk: Bootstrapped standard errors Weihua Guan Stata Corporation Abstract. Bootstrapping is a nonparametric approach for evaluating the distribution
More information16 : Demand Forecasting
16 : Demand Forecasting 1 Session Outline Demand Forecasting Subjective methods can be used only when past data is not available. When past data is available, it is advisable that firms should use statistical
More informationTesting The Quantity Theory of Money in Greece: A Note
ERC Working Paper in Economic 03/10 November 2003 Testing The Quantity Theory of Money in Greece: A Note Erdal Özmen Department of Economics Middle East Technical University Ankara 06531, Turkey ozmen@metu.edu.tr
More informationThe Effects of Critical Peak Pricing for Commercial and Industrial Customers for the Kansas Corporation Commission Final Report
The Effects of Critical Peak Pricing for Commercial and Industrial Customers for the Kansas Corporation Commission Final Report Daniel G. Hansen David A. Armstrong April 11, 2012 Christensen Associates
More informationSolución del Examen Tipo: 1
Solución del Examen Tipo: 1 Universidad Carlos III de Madrid ECONOMETRICS Academic year 2009/10 FINAL EXAM May 17, 2010 DURATION: 2 HOURS 1. Assume that model (III) verifies the assumptions of the classical
More informationBusiness Cycles and Natural Gas Prices
Department of Economics Discussion Paper 200419 Business Cycles and Natural Gas Prices Apostolos Serletis Department of Economics University of Calgary Canada and Asghar Shahmoradi Department of Economics
More informationTraining Programme: Introduction to the Regulation of Electricity Markets June 1416, 16, 2010 Istanbul, Turkey. Electricity demand
INOGATE/ERRA Training Programme: Introduction to the Regulation of Electricity Markets June 1416, 16, 2010 Istanbul, Turkey Electricity demand András Kiss Research Associate Regional Centre for Energy
More informationStructural Econometric Modeling in Industrial Organization Handout 1
Structural Econometric Modeling in Industrial Organization Handout 1 Professor Matthijs Wildenbeest 16 May 2011 1 Reading Peter C. Reiss and Frank A. Wolak A. Structural Econometric Modeling: Rationales
More informationThe Bureau of Labor Statistics produces
Comparing energy indexes to alternative data sources The trend in measures constructed using alternative sources of price data for energy products tracks fairly well with s in the Producer Price Index
More informationIs It Possible to Charge MarketBased Pricing for Ancillary Services in a NonISO Market?
Is It Possible to Charge MarketBased Pricing for Ancillary Services in a NonISO Market? Regulation and Operating Reserves 33 rd Eastern Conference Center for Research in Regulated Industry Romkaew P.
More informationAs we explained in the textbook discussion of statistical estimation of demand
Estimating and Forecasting Industry Demand for PriceTaking Firms As we explained in the textbook discussion of statistical estimation of demand and statistical forecasting, estimating the parameters of
More informationPreparatory Paper on Focal Areas to Support a Sustainable Energy System in the Electricity Sector
Preparatory Paper on Focal Areas to Support a Sustainable Energy System in the Electricity Sector C. Agert, Th. Vogt EWE Research Centre NEXT ENERGY, Oldenburg, Germany corresponding author: Carsten.Agert@nextenergy.de
More informationHURDLE AND SELECTION MODELS Jeff Wooldridge Michigan State University BGSE/IZA Course in Microeconometrics July 2009
HURDLE AND SELECTION MODELS Jeff Wooldridge Michigan State University BGSE/IZA Course in Microeconometrics July 2009 1. Introduction 2. A General Formulation 3. Truncated Normal Hurdle Model 4. Lognormal
More informationPrice Elasticity of Demand DRAFT Working Paper. Kentucky Energy and Environment Cabinet. July 25, 2011
Price Elasticity of Demand DRAFT Working Paper Department for Energy Development & Independence Under Dr. Arne Bathke and Aron Patrick: Shaoceng Wei, Yang Luo, Edward Roualdes July 25, 2011 Overview 1
More informationThis article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal noncommercial research and
This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal noncommercial research and education use, including for instruction at the authors institution
More informationNormalization and Mixed Degrees of Integration in Cointegrated Time Series Systems
Normalization and Mixed Degrees of Integration in Cointegrated Time Series Systems Robert J. Rossana Department of Economics, 04 F/AB, Wayne State University, Detroit MI 480 EMail: r.j.rossana@wayne.edu
More informationFirmLevel Data Analysis of the Effects of Net Investment Income on Underwriting Cycles: An Application of Simultaneous Equations
FirmLevel Data Analysis of the Effects of Net Investment Income on Underwriting Cycles: An Application of Simultaneous Equations MinMing Wen and Patricia Born * Abstract: This study tests two major theories
More informationTime of use (TOU) electricity pricing study
Time of use (TOU) electricity pricing study Colin Smithies, Rob Lawson, Paul Thorsnes Motivation is a technological innovation: Smart meters Standard residential meters Don t have a clock Have to be read
More informationNERA s Labor and Employment Practice
NERA s Labor and Employment Practice NERA s Labor and Employment Practice Experts NERA economists have long been recognized as leaders and innovators in the employment and labor field. Our expertise and
More informationPollution Permit Systems and Firm Dynamics: Does the Allocation Scheme Matter?
Pollution Permit Systems and Firm Dynamics: Does the Allocation Scheme Matter? Evangelina Dardati March 22, 2013 Abstract Most capandtrade systems allocate permits for free. However, they differ dependent
More informationU.S. commercial electricity consumption
MPRA Munich Personal RePEc Archive U.S. commercial electricity consumption Sergio Contreras and Wm. Doyle Smith and Thomas M., Jr. Fullerton University of Texas at El Paso 11. January 2010 Online at http://mpra.ub.unimuenchen.de/34855/
More information