The Bias in Price Elasticity Estimates Under Homothetic Separability: Implications for Analysis of Peak-Load Electricity Pricing

This article demonstrates that the assumption of a homothetically separable utility function places a priori restrictions on the parameters of the demand system. If these restrictions are unwarranted, an open question if they are not explicitly tested, they will lead to biased price elasticity estimates. In particular, we show that the uncompensated own-price elasticities must be smaller than the negative of the expenditure shares; that is, the price elasticity of peak electricity demand must be less than the negative of the share of expenditure devoted to peak electricity. This finding is probably not new to economists familiar with consumer demand analysis. Nevertheless, many recent studies of consumer demand for electricity under time-of-day rates explicitly impose this restriction. The resulting price elasticity estimates are usually quite large in absolute value (.5 to .8); but they are the product of restrictive a priori assumptions as well as information embodied in the sample data. The results of two analyses of time-of-day experiments, where the researchers imposed the untested assumption of homothetic separability, are examined more closely. We find that the reported price elasticities are strongly influenced by that a priori assumption. A Monte Carlo experiment demonstrates that using this model will lead to the reported price elasticities even if the consumption data are perfectly random with respect to price.     

Age,Trend, and Cohort Effects in a Macro Model of Canadian Expenditure Patterns

A quadratic almost ideal demand system allowing for age, cohort, and trend effects is developed at the macro level. The model is estimated by maximum likelihood, using a three-tier iterative/search method applied to pooled 1961–1992 time series for five regions of Canada and six categories of expenditure. Hypothesis tests indicate support for the model specification. Elasticities are compared with those reported in other studies, with special attention to food. Effects of demographic and trend variables on elasticities and expenditure shares are investigated. An overall conclusion is that such effects can be very important in a macro demand system.     

The Effects of Price Aggregation Bias in Systems of Demand Equations

This article illustrates the importance of maintaining consistent levels of aggregation between prices and quantities when estimating consumer demand functions. The impact of violating this condition is quantified by using summary performance measures and estimates of demand elasticity biases. Results derived from an application of 1972–1973 Consumer Expenditure Survey data and supported with a quasi-Monte Carlo experiment consistently indicate that the point estimates of demand elasticities are significantly biased. Thus the study indicates the importance of developing and maintaining price indexes disaggregated to the same level as the expenditure data in consumer expenditure and budget surveys.     

粮食主产区农村居民食物消费行为的计量分析

“民以食为天”,食物消费是人类生存和发展首要的物质基础。农村居民的食物消费水平是决定农村居民身体健康的物质基础,是农村居民生活水平的重要标志。研究和掌握食物消费结构特征,有利于农业产业结构调整和制定正确的农业产业政策,从而促进农民增收,实现农业的可持续发展。粮食主产区的粮食产量占全国粮食总产量的60%以上,对中国的粮食供给和安全具有举足轻重的作用,增加主产区农民收入有利于提高农民种粮食积极性,保证中国的粮食供给和粮食安全。为此,借助几乎理想的需求系统模型(Almost Ideal Demand System,简称AIDS),建立联立方程…     

Censored Quantile Instrumental Variable Estimates of the Price Elasticity of Expenditure on Medical Care

Efforts to control medical care costs depend critically on how individuals respond to prices. I estimate the price elasticity of expenditure on medical care using a censored quantile instrumental variable (CQIV) estimator. CQIV allows estimates to vary across the conditional expenditure distribution, relaxes traditional censored model assumptions, and addresses endogeneity with an instrumental variable. My instrumental variable strategy uses a family member’s injury to induce variation in an individual’s own price. Across the conditional deciles of the expenditure distribution, I find elasticities that vary from ?0.76 to ?1.49, which are an order of magnitude larger than previous estimates. Supplementary materials for this article are available online.     

Interest-Rate Arbitrage in Currency Baskets: Forecasting Weights and Measuring Risk

This article addresses the problem of the bias of income and expenditure elasticities estimated on pseudopanel data caused by measurement error and unobserved heterogeneity. We gauge these biases empirically by comparing cross-sectional, pseudo-panel, and true panel data from both Polish and U.S. expenditure surveys. Our results suggest that unobserved heterogeneity imparts a downward bias to cross-section estimates of income elasticities of at-home food expenditures and an upward bias to estimates of income elasticities of away-from-home food expenditures. “Within” and first-difference estimators suffer less bias, but only if the effects of measurement error are accounted for with instrumental variables.     

Energy Use in Institutional Buildings: Estimates From State Energy-Audit Surveys

This article examines the determinants of energy demand for nearly 9,000 institutional buildings in the United States. The data were collected, as part of the federal Institutional Conservation Program, by state energy offices using mail surveys. The article presents energy demand estimates adjusted for differences in state surveys as well as for nonresponse bias, as functions of energy prices, building characteristics, and fuel-type variables for approximations of the installed heating ventilation and air-conditioning equipment. Energy price elasticities are found to vary from ?.28 for schools to ?1.05 for hospitals.     

Comment

This article tests the hypothesis of perfect competition in the consumer nondurables sector of the U.K. economy. First, it uses household-level data to estimate time-varying price elasticities of demand for disaggregated commodity groups. U.S. product prices are used as instruments for U.K. prices in the demand equation. Then it matches the product definitions to the Standard Industry Classification and uses firm-level data, combined with the estimated elasticities, to estimate a price model of firms operating in different industries. Household characteristics are used as instruments for the demand effects in the firms' supply equation. The results reject perfect competition and appear to be consistent with the argument that less competition increases profits through collusion.     

Money-Demand Variability: A Demand-Systems Approach

This article tests the Fourier flexible form on quarterly U.S. monetary data. The data have been prescreened for consistency with the general axiom of revealed preference, and subindexes are formed using the Divisia approach. In this article, the global Fourier model fits well, although there is a potential problem of overfitting and certain data points exhibit behavior inconsistent with the model. The elasticities are variable over time, particularly around business-cycle troughs. It appears that financial asset demand surfaces are highly nonlinear and the many unsuccessful existing attempts to estimate money demand may not have worked well for this reason.     

Problems in estimating non-linear Engel parameters in the absence of appropriate grouped means and average elasticity of variable elasticity Engel functions

Household Expenditure Survey (HES) data are widely reported in grouped form for a number of reasons. Only within-group arithmetic means (AMs) of the household expenditures on various consumption items, total expenditure, income . and other variables are reported in the tabular form. However, the use of such within-group AMs introduces biases when the parameters of various commonly used non-linear Engel functions are estimated by the Aitken's generalized least squares (GLS) method. This is because the within-group geometric means (GMs)/harmonic means (HMs) are needed in order to estimate unbiased parameters of those non-linear Engel functions. Kakwani (1977) estimated the within-group GMs/HMs from the Kakwani-Podder (1976) Lorenz curve for Indonesian data. We have extended his method to estimate within-group GMs/HMs to a set of variables, based on a general type of concentration curve. It is shown that our estimated within-group GMs/HMs based on concentration curves are not entirely suitable for the Australian HES data. However, these GMs/HMs are then used to estimate Engel parameters for various non-linear Engel functions and it is seen that these elasticities are different for some items of certain non-linear Engel functions than those when the reported within-group AMs are used as proxies for within-group GMs/HMs in order to estimate those non-linear Engel functions. The concept of the average elasticity of a variable elasticity Engel function is discussed and computed for various Australian household consumption items. It is empirically demonstrated that the average elasticities are more meaningful than the traditional elasticity estimates computed at some representative values for certain functions.     

Standard Errors for Elasticities: A Comparison of Bootstrap and Asymptotic Standard Errors

This article compares two methods of deriving standard errors for elasticities in a linear expenditure demand system with first-order autoregressive errors. The first is the ordinary Taylor series method and the second is Efron's bootstrap. In an example problem, these two methods yielded similar values for the standard errors, with the exception of the income elasticity standard errors, for which the asymptotic standard errors were apparently too large by a factor of two.     

Estimation of Short-Run and Long-Run Elasticities of Energy Demand From Panel Data Using Shrinkage Estimators

This article discusses the problem of obtaining short-run and long-run elasticities of energy demand for each of 49 states in the United States using data for 21 years. Estimation using the time series data by each state gave several wrong signs for the coefficients. Estimation using pooled data was not valid because the hypothesis of homogeneity of the coefficients was rejected. Shrinkage estimators gave more reasonable results. The article presents in a unified framework the classical, empirical Bayes, and Bayes approaches for deriving these estimators.     

Specification of household engel curves by nonparametric regression

This paper demonstrates the usefulness of nonparametric regression analysis for functional specfication of houshold Engel curves.

After a brief review in section 2 of the literature on demand functions and equivalence scales and the functional specifications used, we first discuss in section 3 the issues of using income versus total expenditure, the origin and nature of the error terms in the light of utility theroy, and the interpretation of empirical demand functions. we shall reach the unorthodox view that household demand functions should be interpreted as conditional expectations relative to prices, household composition and either income or the conditional expectation of total expenditure (rather that total expenditure itself), where the latter conditional expectation is taken relative to income, prices and household composition. these two forms appear to be equivalent. this result also solves the simultaneity problem: the error variance matrix is no longer singular. Moreover, the errors are in general heteroskedastic.

In section 4 we discuss the model and the data, and in section 5 we review the nonparametric kernal regression approach.

In section 6 we derive the functional form of our household engel curves from nonparametric regression results, using the 1980 budget survey for the netherlands, in order to avoid model misspecification. thus the modl is derived directly from the data, without restricting its functional form. the nonparametric regression results are then translated to suitable parametric functional specifications, i.e., we choose parametric functional forms in accordance with the nanparametric regression results. these parametric specification are estimated by least squares, and various parameter restrictions are tested in order to simplify the models. this yields very simple final specifications of the household engel curves involved, namely linear functions of income and the number of children in two age groups.     

Specification of household engel curves by nonparametric regression

This paper demonstrates the usefulness of nonparametric regression analysis for functional specfication of houshold Engel curves.

After a brief review in section 2 of the literature on demand functions and equivalence scales and the functional specifications used, we first discuss in section 3 the issues of using income versus total expenditure, the origin and nature of the error terms in the light of utility theroy, and the interpretation of empirical demand functions. we shall reach the unorthodox view that household demand functions should be interpreted as conditional expectations relative to prices, household composition and either income or the conditional expectation of total expenditure (rather that total expenditure itself), where the latter conditional expectation is taken relative to income, prices and household composition. these two forms appear to be equivalent. this result also solves the simultaneity problem: the error variance matrix is no longer singular. Moreover, the errors are in general heteroskedastic.

In section 4 we discuss the model and the data, and in section 5 we review the nonparametric kernal regression approach.

In section 6 we derive the functional form of our household engel curves from nonparametric regression results, using the 1980 budget survey for the netherlands, in order to avoid model misspecification. thus the modl is derived directly from the data, without restricting its functional form. the nonparametric regression results are then translated to suitable parametric functional specifications, i.e., we choose parametric functional forms in accordance with the nanparametric regression results. these parametric specification are estimated by least squares, and various parameter restrictions are tested in order to simplify the models. this yields very simple final specifications of the household engel curves involved, namely linear functions of income and the number of children in two age groups.     

中国居民消费对二氧化碳排放的影响——基于碳排放投入产出模型的分析

中国把战略重点转移到扩大内需,但扩大内需增加消费的同时,也不能忽略消费的负面影响,特别是通过产业间的带动作用对能源消耗及二氧化碳排放的影响。文章构建了碳排放预测的计量投入产出模型,预测了2011年分产品部门的居民消费支出对碳排放的拉动作用,并分析了其碳排放的成因。结果表明,减排重点部门为电力、热力的生产和供应业,而其碳排放主要通过消耗煤炭而产生的。     

中国能源需求影响因素的研究

基于中国1990—2007年的经济数据，通过分析影响能源需求的经济因素，利用通径分析法测算出各相关经济因素与能源需求的直接、间接关系及总影响程度，分析各因素对能源需求的影响路径，并根据测算结果提出相应的对策。     

农户正规贷款支付意愿水平、贷款可获性及其地区差异研究

研究不同地区农户正规贷款支付意愿水平及其贷款可获性,有助于理解农村金融市场供求非均衡的原因,为探寻供求均衡的实现机制提供农户层面的微观证据。基于江苏、河南与甘肃三省15个乡镇的调查数据,运用截取回归模型和排序数据模型对影响农户正规贷款支付意愿水平及其贷款可获性进行实证分析,并采用T统计量检验其地区差别。研究表明：农户正规贷款支付意愿水平受到个体特征、家庭支出与地区虚拟变量的影响;而农户正规贷款可获性则受到个体特征、家庭教育支出、还款能力、贷款申请规模以及地区虚拟变量的影响;东部地区农户正规贷款支付意愿水平及其贷款可获性均高于中西部地区,由此建议政府可以从扩大农村抵押担保物范围、创新抵押担保机制与金融产品等方面来增强农户家庭还款能力,提高正规贷款可获性,改善农村金融市场供求非均衡。     

经济开放与地方财政支出

 本文在经典的中位选民需求函数模型基础上,构建了地方财政支出决定的动态空间面板模型,分析了经济开放对地方财政支出的影响。研究发现,经济开放的地方财政支出效应是多维和复杂的,经济开放引起了地方政府支出规模的扩大,外商投资扩大使地方支农支出、社会福利支出增加,贸易开放使地方文化和公检法司支出增加,支持了经济开放的补偿假说;同时,投资开放引起行政管理支出和财政补贴支出减少,贸易开放度的提升促进了地方财政基础设施、科教等支出的增加,并减少社会福利支出,又支持效率假说。经济开放对地方财政支出结构扭曲的矫正和强化效应并存,我国应在坚持开放的前提下,积极创造使经济开放的积极效应最大化、消极效应最小化的制度条件。     

Bayesian hierarchical models for food frequency assessment

The aim of this study is to assess the biases of a Food Frequency Questionnaire (FFQ) by comparing total energy intake (TEI) with total energy expenditure (TEE) obtained from doubly labelled water(DLW) biomarker after adjusting measurement errors in DLW. We develop several Bayesian hierarchical measurement error models of DLW with different distributional assumptions on TEI to obtain precise bias estimates of TEI. Inference is carried out by using MCMC simulation techniques in a fully Bayesian framework, and model comparisons are done via the mean square predictive error. Our results showed that the joint model with random effects under the Gamma distribution is the best fit model in terms of the MSPE and residual diagnostics, in which bias in TEI is not significant based on the 95% credible interval. The Canadian Journal of Statistics 38: 506–516; 2010 © 2010 Statistical Society of Canada     

Non-stationary structural model with time-varying demand elasticities

The paper considers local linear regression of a time series model with non-stationary regressors and errors. Asymptotic property of the local linear estimator is derived under a new dependence measure of non-stationary time series. We apply the local linear regression method to estimate the “time-varying” coefficients of an economic-causal model for the industrial sector of the U.S. economy. Nonparametric bootstrap test on the time-varying coefficients strongly suggests that the price/income elasticities of the U.S. durable goods demand are time-varying.