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…     

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.     

Systematic Effects of Capital Service Price Definition on Perceptions of Input Substitution

Earlier attempts at reconciling disparate substitution elasticity estimates examined differences in separability hypotheses, data bases, and estimation techniques, as well as methods employed to construct capital service prices. Although these studies showed that differences in elasticity estimates between two or three studies may be attributable to the aforementioned features of the econometric models, they have been unable to demonstrate this link statistically and establish the existence of systematic relationships between features of the econometric models and the perception of production technologies generated by those models. Using sectoral data covering the entire production side of the U.S. economy, we estimate 34 production models for alternative definitions of the capital service price. We employ substitution elasticities calculated from these models as dependent variables in the statistical search for systematic relationships between features of the econometric models and perceptions of the sectoral technology as characterized by the elasticities. Statistically significant systematic effects are found between the monotonicity and concavity properties of the cost functions and service price–technical change specifications as well as between substitution elasticities.     

Estimating Engel curves: a new way to improve the SILC-HBS matching process using GLM methods

Microdata are required to evaluate the distributive impact of the taxation system as a whole (direct and indirect taxes) on individuals or households. However, in European Union countries this information is usually distributed into two separate surveys: the Household Budget Surveys (HBS), including total household expenditure and its composition, and EU Statistics on Income and Living Conditions (EU-SILC), including detailed information about households'' income and direct (but not indirect) taxes paid. We present a parametric statistical matching procedure to merge both surveys. For the first stage of matching, we propose estimating total household expenditure in HBS (Engel curves) using a GLM estimator, instead of the traditionally used OLS method. It is a better alternative, insofar as it can deal with the heteroskedasticity problem of the OLS estimates, while making it unnecessary to retransform the regressors estimated in logarithms. To evaluate these advantages of the GLM estimator, we conducted a computational Monte Carlo simulation. In addition, when an error term is added to the deterministic imputation of expenditure in the EU-SILC, we propose replacing the usual Normal distribution of the error with a Chi-square type, which allows a better approximation to the original expenditures variance in the HBS. An empirical analysis is provided using Spanish surveys for years 2012–2016. In addition, we extend the empirical analysis to the rest of the European Union countries, using the surveys provided by Eurostat (EU-SILC, 2011; HBS, 2010).     

Large and moderate deviations for modified Engel series

The modified Engel series of real numbers introduced by Rényi (1962 Rényi, A. (1962). A new approach to the theory of Engel’s series. Ann. Univ. Sci. Budapest. Eötvös Sect. Math. 5:25–32. [Google Scholar]) is a simple modification of Engel series, and they have the same classical limit theorems, such as the law of large numbers, central limit theorem, and law of the iterated logarithm. In this paper, we studied the large and moderate deviations for modified Engel series, which indicate that the large deviations for modified Engel series and Engel series are different.     

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.     

Generating Market Elasticity Estimates Using Cross-Sectional First-Choice and Second-Choice Data

This article develops a method for using first-choice/second-choice information to estimate the ratio of cross-elasticities to own-elasticities. I then use Bayesian techniques to couple this information with prior knowledge of the elasticities. This leads to elasticity estimates reflecting the decision maker's prior knowledge as well as existing data on customer first-choice/second-choice preferences.     

Formative Indicators and Effects of a Causal Model for Household Human Capital with Application

Dagum and Slottje (2000) estimated household human capital (HC) as a latent variable (LV) and proposed its monetary estimation by means of an actuarial approach. This paper introduces an improved method for the estimation of household HC as an LV by means of formative and reflective indicators in agreement with the accepted economic definition of HC. The monetary value of HC is used in a recursive causal model to obtain short- and long-term multipliers that measure the direct and total effects of the variables that determine household HC. The new method is applied to estimate US household HC for year 2004.     

Assessment of the Gould-Shih procedure for sample size re-estimation

The power of a clinical trial is partly dependent upon its sample size. With continuous data, the sample size needed to attain a desired power is a function of the within-group standard deviation. An estimate of this standard deviation can be obtained during the trial itself based upon interim data; the estimate is then used to re-estimate the sample size. Gould and Shih proposed a method, based on the EM algorithm, which they claim produces a maximum likelihood estimate of the within-group standard deviation while preserving the blind, and that the estimate is quite satisfactory. However, others have claimed that the method can produce non-unique and/or severe underestimates of the true within-group standard deviation. Here the method is thoroughly examined to resolve the conflicting claims and, via simulation, to assess its validity and the properties of its estimates. The results show that the apparent non-uniqueness of the method's estimate is due to an apparently innocuous alteration that Gould and Shih made to the EM algorithm. When this alteration is removed, the method is valid in that it produces the maximum likelihood estimate of the within-group standard deviation (and also of the within-group means). However, the estimate is negatively biased and has a large standard deviation. The simulations show that with a standardized difference of 1 or less, which is typical in most clinical trials, the standard deviation from the combined samples ignoring the groups is a better estimator, despite its obvious positive bias.     

Nonparametric Panel Estimation of Labor Supply

In this article, we estimate structural labor supply with piecewise-linear budgets and nonseparable endogenous unobserved heterogeneity. We propose a two-stage method to address the endogeneity issue that comes from the correlation between the covariates and unobserved heterogeneity. In the first stage, Evdokimov’s nonparametric de-convolution method serves to identify the conditional distribution of unobserved heterogeneity from the quasi-reduced model that uses panel data. In the second stage, the conditional distribution is plugged into the original structural model to estimate labor supply. We apply this methodology to estimate the labor supply of U.S. married men in 2004 and 2005. Our empirical work demonstrates that ignoring the correlation between the covariates and unobserved heterogeneity will bias the estimates of wage elasticities upward. The labor elasticity estimated from a fixed effects model is less than half of that obtained from a random effects model.     

A Dynamic Model of Investment in the U.S. Beef-Cattle Industry

Reproduction constraints lead to some a priori information that may prove valuable in econo- metrically modeling inventory dynamics within the U.S. cattle herd. The distributed-lag models estimated for animal inventories suggest an implicit incorporation of the age distribution of the herd that is crucial to understanding the retention/culling (investment) decisions. The estimated equations are validated using post-sample observations that were withheld prior to estimation. The final-form dynamic equations have complex roots and protracted price and investment effects. The parameter estimates are used to calculate intermediate and long-run elasticities at the means of relevant variables.     

Time-of-Day Electricity Consumption Response to Temperature and the Ownership of Air Conditioning Appliances

This article is concerned with the endogeneity of air conditioning appliance ownership in the consumption of electricity by time of day (TOD). Both TOD consumption and air conditioning appliance ownership depend on temperature level and on household responsiveness to temperature variation, as well as on other measured household characteristics. The article presents an appropriate econometric methodology and applies it to obtain estimates and perform tests based on data from Southern California Edison's Residential TOD Pricing Experiment. The authors emphasize the implications for estimated consumption price elasticities.     

Bayesian least squares estimates of univariate regression functions

The regression function R(?) to be estimated is assumed to have an expansion in terms of specified functions, orthogonalized vich respect to values of the explanatory variable. Relative precisions of OBSERVATION are assumed known. The estimate is the posterior linear mean of R(?) given the data. The investigator plots graphs of appropriate functions as an aid in eliciting his prior means and precisions for the coefficients in the expansion. The method is illustrated by an example using simulated data, an example in which effects of various dosages of Vitamin D are estimated, and an example in which a utility function is estimated.     

On the relationship between the reversed hazard rate and elasticity

Despite hazard and reversed hazard rates sharing a number of similar aspects, reversed hazard functions are far less frequently used. Understanding their meaning is not a simple task. The aim of this paper is to expand the usefulness of the reversed hazard function by relating it to other well-known concepts broadly used in economics: (linear or cumulative) rates of increase and elasticity. This will make it possible (i) to improve our understanding of the consequences of using a particular distribution and, in certain cases, (ii) to introduce our hypotheses and knowledge about the random process in a more meaningful and intuitive way, thus providing a means to achieving distributions that would otherwise be hardly imaginable or justifiable.     

中国城镇居民肉类需求的单位价值弹性估计偏差研究

当前,对中国居民肉类需求的价格弹性估计多是利用汇总数据和用单位价值替代市场价格进行估计,而产品质量变化会导致用单位价值估计的价格弹性有偏。鉴此,利用全国984个样本汇总数据,估计城镇居民对猪牛羊禽肉的各种弹性,并且对利用单位价值估计的单位价值弹性和真实的价格弹性之间的偏差进行估计。结论显示:通过单位价值估计的价格弹性(单位价值弹性)高估了真实的价格弹性;随着收入水平的提高城镇居民对肉类质量水平需求会不断提高,通过单位价值高估的价格弹性程度将更强,因此对单位价值弹性偏差的衡量越发重要。     

陕西省保险需求收入弹性分析

文章对保险需求的收入弹性做了理论分析,利用陕西省的数据对陕西省的保险总需求、财产保险需求、人寿保险需求的收入弹性系数分别做了计算和经济分析,并且与国际上的正常区间值进行了对比,其结论是:陕西省的三个弹性系数值基本都在正常范围内,只是收入对于财产保险和人寿保险的相对影响略有差异。     

Two-Stage Budgeting and Exact Aggregation

This paper presents an econometric model of demand for energy based on two-stage budgeting. The model provides own-price and cross-price elasticities of demand for energy and nonenergy commodities for the United States. These elasticities are estimated separately for households classified by family size, age of head, region, race, and urban versus rural residence. Price elasticities are presented conditional on total energy expenditure and total expenditure on all commodities. The model combines individual cross-section data with aggregate time series data and is based on exact aggregation over individual demand functions.     

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.     

Nonresponse Assessment of a Consumer Price Index

A household budget survey often suffers from a high nonresponse rate and a selective response. The bias that may be introduced in the estimation of budget shares because of this nonresponse can affect the estimate of a consumer price index, which is a weighted sum of partial price index numbers (weighted with the estimated budget shares). The bias is especially important when related to the standard error of the estimate. Because of the impossibility of subsampling nonrespondents to the budget survey, no exact information on the bias can be obtained. To evaluate the nonresponse bias, bounds for this bias are calculated using linear programming methods for several assumptions. The impact on a price index of a high nonresponse rate among people with a high income can also be assessed by using the elasticity with respect to total expenditure. Attention is also given to the possible nonresponse bias in a time series of price index numbers. The possible nonresponse bias is much larger than the standard error of the estimate.     

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.