Aggregate Consumer Behavior and Household Equivalence Scales

This article presents estimates of household equivalence scales, broken down by demographic characteristics, of U.S. households. Separate estimates are given by family size, age of head, region, race, and urban versus rural residence. Commodity-specific scales are presented for five separate commodity groups—energy, food, consumer goods, capital services, and other services. The estimates are obtained from an econometric model of aggregate consumer behavior. The parameters of this model are estimated by combining aggregate time series and individual cross-section data.     

Forecasting aggregate period-specific birth rates: the time series properties of a microdynamic neoclassical model of fertility

"This article demonstrates the value of microdata for understanding the effect of wages on life cycle fertility dynamics. Conventional estimates of neoclassical economic fertility models obtained from linear aggregate time series regressions are widely criticized for being nonrobust when adjusted for serial correlation. Moreover, the forecasting power of these aggregative neoclassical models has been shown to be inferior when compared with conventional time series models that assign no role to wages. This article demonstrates that, when neoclassical models of fertility are estimated on microdata using methods that incorporate key demographic restrictions and when they are properly aggregated, they have considerable forecasting power." Data are from the 1981 Swedish Fertility Survey.     

Demand Systems Estimation With Microdata: A Censored Regression Approach

Demand systems estimation increasingly makes use of household-level microdata, mainly to measure the effects of demographic variables. Data based on these household-expenditure surveys present a major estimation problem. For any given household, many of the goods have zero consumption, implying a censored dependent variable. Techniques which do not take this censored dependent variable into account will yield biased results. We utilize a censored regression approach that is computationally simple, consistent, and asymptotically efficient. The results are then presented and compared with those obtained using an uncensored technique.     

The case for small area microdata

Summary.  Census data are available in aggregate form for local areas and, through the samples of anonymized records (SARs), as samples of microdata for households and individuals. In 1991 there were two SAR files: a household file and an individual file. These have a high degree of detail on the census variables but little geographical detail, a situation that will be exacerbated for the 2001 SAR owing to the loss of district level geography on the individual SAR. The paper puts forward the case for an additional sample of microdata, also drawn from the census, that has much greater geographical detail. Small area microdata (SAM) are individual level records with local area identifiers and, to maintain confidentiality, reduced detail on the census variables. Population data from seven local authorities, including rural and urban areas, are used to define prototype samples of SAM. The rationale for SAM is given, with examples that demonstrate the role of local area information in the analysis of census data. Since there is a trade-off between the extent of local detail and the extent of detail on variables that can be made available, the confidentiality risk of SAM is assessed empirically. An indicative specification of the SAM is given, having taken into account the results of the confidentiality analysis.     

Testing the Validity of Aggregates

This article develops a new approach for testing aggregation restrictions in estimated production-function and cost-function models. Rather than using the well-known separability conditions for the existence of an aggregate, this approach focuses on testing whether a particular aggregate is valid and develops empirically testable necessary and sufficient conditions for the validity of some known aggregation scheme. An empirical section examines the power of this test in the context of a simple production-function model.     

中国居民消费行为研究

一、问题的提出进入 90年代以来我国居民消费率呈下降态势。消费占ＧＤＰ的比重 ,由 80年代的5 2 %降低到现在的 46 %。近年来 ,政府采取了包括降低银行存款利率 ,鼓励个人信贷消费、提高中低层居民收入等一系列举措 ,然而居民消费并没有摆脱低迷状态 ,1 998年全国消费品零售增长 6 8% ,比 1 990— 1 997的年均增长低 1 4 5个百分点。与居民低消费率相对应 ,居民储蓄持续增长成为我国最引人注目的经济现象之一。居民储蓄是指可支配收入减去消费和转移支付后的余额 ,包括实物性储蓄和金融性储蓄。在我国的居民储蓄中 ,金融性储蓄约占75 % ,…     

多层贝叶斯模型在消费者偏好分析中的应用研究——基于手机市场数据

基于手机市场的调研数据,构建了多层贝叶斯模型,并将其与传统哑变量回归模型进行比较,得出前者比后者具有更好的模型拟合能力和预测能力的结论。利用有人口特征变量的多层贝叶斯随机效应模型来完成对所有未知参数的估计,从个体内行为和个体间行为两个层面对消费者的偏好行为进行全面分析,结果发现该模型可以很好解释消费者的总体偏好及其偏好差异性。     

Seasonality in U.S. Consumer Demand

This study uses a new body of quarterly, not seasonally adjusted, data to test various hypotheses regarding consumer demand. The almost complete demand system (ACS) was used to test for the various restrictions of economic theory and for the presence of seasonality. Zero-degree homogeneity, Cournot aggregation, and negativity were, for the most part, accepted. Tests showed that seasonal effects can be modeled by quarterly intercepts and that slope coefficients are homogeneous over seasons. Also, it was demonstrated that although habit plays a significant role, it is not as prominent as past studies have shown.     

Source Data Perturbation and consistent sets of safe tables

When tables are generated from a data file, the release of those tables should not reveal too detailed information concerning individual respondents. The disclosure of individual respondents in the microdata file can be prevented by applying disclosure control methods at the table level (by cell suppression or cell perturbation), but this may create inconsistencies among other tables based on the same data file. Alternatively, disclosure control methods can be applied at the microdata level, but these methods may change the data permanently and do not account for specific table properties. These problems can be circumvented by assigning a (single and fixed) weight factor to each respondent/record in the microdata file. Normally this weight factor is equal to 1 for each record, and is not explicitly incorporated in the microdata file. Upon tabulation, each contribution of a respondent is weighted multiplicatively by the respondent's weight factor. This approach is called Source Data Perturbation (SDP) because the data is perturbed at the microdata level, not at the table level. It should be noted, however, that the data in the original microdata is not changed; only a weight variable is added. The weight factors can be chosen in accordance with the SDC paradigm, i.e. such that the tables generated from the microdata are safe, and the information loss is minimized. The paper indicates how this can be done. Moreover it is shown that the SDP approach is very suitable for use in data warehouses, as the weights can be conveniently put in the fact tables. The data can then still be accessed and sliced and diced up to a certain level of detail, and tables generated from the data warehouse are mutually consistent and safe.     

Spatial modelling of the two‐party preferred vote in Australian federal elections: 2001–2016

We examine the relationships between electoral socio‐demographic characteristics and two‐party preferences in the six Australian federal elections held between 2001 and 2016. Socio‐demographic information is derived from the Australian Census which occurs every 5 years. Since a census is not directly available for each election, an imputation method is employed to estimate census data for the electorates at the time of each election. This accounts for both spatial and temporal changes in electoral characteristics between censuses. To capture any spatial heterogeneity, a spatial error model is estimated for each election, which incorporates a spatially structured random effect vector. Over time, the impact of most socio‐demographic characteristics that affect electoral two‐party preference do not vary, with age distribution, industry of work, incomes, household mobility and relationships having strong effects in each of the six elections. Education and unemployment are among those that have varying effects. All data featured in this study have been contributed to the eechidna R package (available on CRAN).     

A Small Area Predictor under Area-Level Linear Mixed Models with Restrictions

A calibrated small area predictor based on an area-level linear mixed model with restrictions is proposed. It is showed that such restricted predictor, which guarantees the concordance between the small area estimates and a known estimate at the aggregate level, is the best linear unbiased predictor. The mean squared prediction error of the calibrated predictor is discussed. Further, a restricted predictor under a particular time-series and cross-sectional model is presented. Within a simulation study based on real data collected from a longitudinal survey conducted by a national statistical office, the proposed estimator is compared with other competitive restricted and non-restricted predictors.     

Disclosure Risk and Disclosure Avoidance for Microdata

Under given concrete exogenous conditions, the fraction of identifiable records in a microdata file without positive identifiers such as name and address is estimated. The effect of possible noise in the data, as well as the sample property of microdata files, is taken into account. Using real microdata files, it is shown that there is no risk of disclosure if the information content of characteristics known to the investigator (additional knowledge) is limited. Files with additional knowledge of large information content yield a high risk of disclosure. This can be eliminated only by massive modifications of the data records, which, however, involve large biases for complex statistical evaluations. In this case, the requirement for privacy protection and high-quality data perhaps may be fulfilled only if the linkage of such files with extensive additional knowledge is prevented by appropriate organizational and legal restrictions.     

Testing a Unit Root Based on Aggregate Time Series

Many time series encountered in practice are nonstationary, and instead are often generated from a process with a unit root. Because of the process of data collection or the practice of researchers, time series used in analysis and modeling are frequently obtained through temporal aggregation. As a result, the series used in testing for a unit root are often time series aggregates. In this paper, we study the effects of the use of aggregate time series on the Dickey–Fuller test for a unit root. We start by deriving a proper model for the aggregate series. Based on this model, we find the limiting distributions of the test statistics and illustrate how the tests are affected by the use of aggregate time series. The results show that those distributions shift to the right and that this effect increases with the order of aggregation, causing a strong impact both on the empirical significance level and on the power of the test. To correct this problem, we present tables of critical points appropriate for the tests based on aggregate time series and demonstrate their adequacy. Examples illustrate the conclusions of our analysis.     

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.     

Long-Run Identification in a Fractionally Integrated System

We propose an extension of structural fractionally integrated vector autoregressive models that avoids certain undesirable effects on the impulse responses that occur if long-run identification restrictions are imposed. We derive the model’s Granger representation and investigate the effects of long-run restrictions. Simulations illustrate that enforcing integer integration orders can have severe consequences for impulse responses. In a system of U.S. real output and aggregate prices, the effects of structural shocks strongly depend on the specification of the integration orders. In the statistically preferred fractional model, shocks that are typically interpreted as demand disturbances have a very brief influence on GDP. Supplementary materials for this article are available online.     

Testing the Stochastic Structure of Production: A Flexible Moment-Based Approach

Conventional production function specifications are shown to impose restrictions on the probability distribution of output that cannot be tested with the conventional models. These restrictions have important implications for firm behavior under uncertainty. A flexible representation of a firm's stochastic technology is developed based on the moments of the probability distribution of output. These moments are a unique representation of the technology and are functions of inputs. Large-sample estimators are developed for a linear moment model that is sufficiently flexible to test the restrictions implied by conventional production function specifications. The flexible moment-based approach is applied to milk production data. The first three moments of output are statistically significant functions of inputs. The cross-moment restrictions implied by conventional models are rejected.     

The Estimation of Short-Run Household Electricity Demand Using Pooled Aggregate Data

Short-run household electricity demand has been estimated with conditional demand models by a variety of authors using both aggregate data and disaggregate data. Disaggregate data are most desirable for estimating these models. However, in many cases, available disaggregate data may be inappropriate. Furthermore, disaggregate data may be unavailable altogether. In these cases, readily available aggregate data may be more appropriate. This article develops and evaluates an econometric technique to generate unbiased estimates of household electricity demand using such aggregate data.     

A Comparison of Nonparametric Tests of Weak Separability for Annual and Quarterly Data on Consumption,Leisure, and Money

Among the most fundamental assumptions made in economics are utility maximization and the separability of the arguments in the representative consumer's utility function. These assumptions are important for theoretical and empirical applications of economics. In this article, we present results from nonparametric tests of these assumptions of consumer behavior. We find that utility maximization generally obtains with either annual or quarterly per capita data on consumption goods, leisure, and relatively liquid monetary assets. Annual data on consumption goods, leisure, and all monetary assets are consistent with utility maximization. There is some evidence in support of using partial adjustment models when estimating quarterly data models of the demand for monetary assets. Further, annual data on consumption goods and leisure and on liquid monetary assets meet the necessary and sufficient conditions for weak separability. These results support the notion of a monetary aggregate more broadly based than currency plus demand deposits. Separability of monetary assets does not obtain for quarterly data.     

农村居民消费融资需求及影响因素分析——以陕西省渭南市为例

基于渭南市2 636户农户问卷调查数据,从农户分化与演变视觉分析农村居民消费融资需求状况。采用概率模型分析了影响农户融资消费的主要因素是收支的＂非均衡性＂、户主个性、消费环境、城乡融合程度和存贷款利率水平高低。建议调整国民收入分配结构,完善农村基本社会保障体系,推进农村居民生产生活集聚,构建以农户需求为导向的农村金融体系。     

非线性定价、家庭人口特征和收入再分配效应调整

本文以分时阶梯定价为例,在人口老龄化及生育政策逐渐放宽的背景下,通过建立结构计量模型,实证分析了引入家庭人口特征后,非线性定价对收入再分配效应这一政策目标的影响。通过构建引入家庭人口特征的二次近乎理想需求（QUAIDS）函数,基于相对等价补偿方法建立收入再分配效应调整的测度模型。估计了为保持相同效用增加家庭不同类型的人口需要增加的电费补偿率及其金额。研究结论表明,引入家庭人口特征后收入再分配效应得到了强化;家庭人口特征对消费者行为选择有显著影响;不同人口规模的家庭的电费补贴结构存在明显差异;动态分析显示,随着预算水平的提高,家庭人口特征对收入再分配效应的影响在减小。