ON THE INTERPRETATION OF COVARIATE ESTIMATES IN INDEPENDENT COMPETING-RISKS MODELS*

Competing-risks models are becoming increasingly pervasive in applied research to explain the factors determining both the time in a state (i.e. unemployment) and the exit route from the state (is. leaving unemployment for a job or non-participation). However, as in many limited dependent variable models, the interpretation of the covariate estimates requires care. Despite assertions to the contrary in many published papers, it is shown that the estimated qualitative effect of a covariate on the hazard for risk j typically conveys no information on its qualitative effect on either the likelihood of, or expected time until, exit via j. This is because such magnitudes depend on the qualitative and quantitative effects of the covariate on other risks which cannot be ignored. The point is demonstrated by using UK data on the unemployment to re-employment transitions of a sample of male job losers.     

Estimation and specification analysis of models of dividend behavior based on censored panel data

Dividends move in discrete jumps. Moreover, some companies pay dividends, others do not. Both these aspects necessitate the use of limited dependent variable models in the analysis of dividend behavior. Models of dividend behavior usually ignore these problems and treat dividends as a continuous variable. The present paper analyzes dividend behavior using panel data on 649 firms for 12 years (1976–1987). The model used is a censored regression model which allows for firm-specific and time effects. It is estimated using the maximum likelihood method under three different error covariance specifications. Based on specification tests, it is argued that it is important to allow for the zero observations, industry effects, and firm-specific and time effects in the estimation of models of dividend behavior.     

The degree of monopsony power in agricultural labour markets,and the impact of the agricultural minimum wage: an application to craft workers in England and Wales

This paper uses cross-section data to examine the determinants of wages for agricultural craft workers over the period 1991–1994. Using limited dependent variable models to take account of the censoring in the data resulting from the minimum wage, we have investigated the degree to which agricultural and broader labour markets are integrated, the impact of tied housing on wages, and the extent to which the minimum wage truncates the wage distribution.     

Estimation of disequilibrium and limited dependent variable models with serially dependent residuals

We present an approach to the estimation of disequilibrium models in the presence of serially dependent residuals that has several advantages and can be applied also to limited dependent variable and other models.     

Health expenditure models: A comparison using panel data

In this article, we focus on the estimation of outpatient expenditures with panel data. We model the logarithm of expenditures and consider five different models. The first two are two-part and sample selection cross-section models. Two-part panel data models turn out to be inappropriate for dealing with expenditures. We thus estimate sample selection models with panel data: one without a lagged dependent variable and two with a lagged dependent variable. These two latter models differ in their assumptions on the variance of the residuals. Modelling heteroscedasticity may indeed be important to avoid the bias due to the retransformation problem. We show that lagged dependent variables are important factors for heteroscedasticity. For the models with state dependence, we provide a new solution to the initial conditions problem by controlling for generalised residuals. We establish that panel data models highly improve the correlation explained by the model in the time-series dimension without damaging the fit in the cross-section dimension. For all indicators of fit, the model with state dependence and heteroscedasticity seems to dominate the others.     

Tobit or OLS? An empirical evaluation under different diary window lengths

Researchers analysing time-use data often estimate limited dependent variable models because time spent must be nonnegative and cannot be more than the total amount of time in a given observation period. While the traditional empirical technique applied to such cases is maximum likelihood estimation of a Tobit (censored regression) model, recent debate has questioned whether linear models estimated via Ordinary Least Squares (OLS) are preferable. On the one hand, Tobit models are deemed necessary to address the significant censoring (i.e. large numbers of zeroes) typically found in time-use data, in the face of which OLS estimators would be biased and inconsistent. Yet, optimization occurs over a longer period than that covered by the typical time diary (often a day), and thus some argue that reported zeroes represent a measurement problem rather than true nonparticipation in the activity, in which case OLS would be preferred. We provide direct empirical evidence on this question using the Australian Time Use Surveys, which record time-use information for two consecutive diary days, by estimating censored and linear versions of a parental child care model for both 24-hour and 48-hour windows of observation in order to determine the empirical consequences of estimation technique and diary length.     

Equivalence Scales in an Intertemporal Setting with an Application to the Former West Germany

Equivalence scales provide answers to questions like how much a household with two children needs to spend compared to a couple to attain the same welfare level. These are important questions for child allowances, social benefits and to assess the cost of children over the life-cycle for example. We discuss equivalence scales in an intertemporal setting with uncertainty. To estimate equivalence scales we use a panel from German households (GSOEP) containing subjective data on satisfaction with life and satisfaction with income to represent the welfare level. Because satisfaction is measured on a discrete scale we use limited dependent variable models for panel data in estimation. Using satisfaction with life data we find that larger households do not need any additional income to be as satisfied as a couple. Using satisfaction with income, however, yields equivalence scales that increase with household size.     

On estimating long-run effects in models with lagged dependent variables

A common procedure in economics is to estimate long-run effects from models with lagged dependent variables. For example, macro panel studies frequently are concerned with estimating the long-run impacts of fiscal policy, international aid, or foreign investment.Our analysis points out the hazards of this practice. We use Monte Carlo experiments to demonstrate that estimating long-run impacts from dynamic models produces unreliable results.Biases can be substantial, sample ranges very wide, and hypothesis tests can be rendered useless in realistic data environments. There are three reasons for this poor performance. First, OLS estimates of the coefficient of a lagged dependent variable are downwardly biased in finite samples. Second, small biases in the estimate of the lagged, dependent variable coefficient are magnified in the calculation of long-run effects. And third, and perhaps most importantly, the statistical distribution associated with estimates of the LRP is complicated, heavy-tailed, and difficult to use for hypothesis testing. While many of the underlying problems have been long-known in the literature, the continued widespread use of the associated empirical procedures suggests that researchers are unaware of the extent and severity of the estimation problems. This study aims to illustrate their practical importance for applied research.     

Efficient specification tests for limited dependent variable models

The procedure of Jarque and Bera (1980a, b), consisting of the application of the Lagrange Multiplier (LM) test to the Pearson Family of distributions, is used to derive efficient normality and/or homoscedasticity tests for limited dependent variable (LDV) models.     

The impact of software piracy on inclusive human development: evidence from Africa

The study examines the effect of software piracy on inclusive human development in 11 African countries for which software piracy data is available for the period 2000–2010. The empirical evidence is based on instrumental variable panel Fixed Effects (FE) and Tobit models in order to control for the unobserved heterogeneity and limited range in the dependent variable. The modeling exercise is based on the inequality adjusted human development (IHDI) and its constituents. The following main findings are established. First, from the FE regressions, software piracy consistently improves the IHDI and its constituents. Within this framework, the positive relationship between inclusive human development and software piracy is driven by all its constituents. Second, for Tobit regressions, the positive relationship between software piracy and inclusive human development is confirmed exclusively in IHDI and literacy specifications. Within the latter framework, the positive relationship between software piracy and inclusive human is driven fundamentally by the literacy rate. Policy implications are discussed.     

Stability under learning: The endogenous growth problem

A local dynamic analysis, in the neighborhood of the steady state, is developed for one and two-sector endogenous growth models. The problem differs from the conventionally assumed growth setups because one considers that expectations concerning the next period value of the control variable (consumption) are formed through adaptive learning. In such scenario, the found stability conditions reveal that convergence to the unique steady state point is feasible if a minimum requirement regarding the quality of learning in the long run equilibrium is fulfilled. Therefore, stability of growth under learning is dependent on the efficiency with which expectations are generated.     

PAIRWISE DIFFERENCE ESTIMATION WITH NONPARAMETRIC CONTROL VARIABLES*

This article extends the pairwise difference estimators for various semilinear limited dependent variable models proposed by Honoré and Powell (Identification and Inference in Econometric Models. Essays in Honor of Thomas Rothenberg Cambridge: Cambridge University Press, 2005) to permit the regressor appearing in the nonparametric component to itself depend upon a conditional expectation that is nonparametrically estimated. This permits the estimation approach to be applied to nonlinear models with sample selectivity and/or endogeneity, in which a “control variable” for selectivity or endogeneity is nonparametrically estimated. We develop the relevant asymptotic theory for the proposed estimators and we illustrate the theory to derive the asymptotic distribution of the estimator for the partially linear logit model.     

Repayment performance in group lending: Evidence from Jordan

Using data from a survey of 160 urban borrowing groups of the Microfund for Women in Jordan, we investigate the effect of screening, peer monitoring, group pressure, and social ties on borrowing groups' repayment behavior as an indirect test of different theoretical models. The dependent variable used captures the intensity of default measured by the total number of days of late repayment after each due date, allowing us to use count data models with cluster standard errors. As theory predicts, our empirical analysis suggests that peer monitoring, group pressure, and social ties reduce delinquency. The paper uncovers interesting evidence about the role of social ties and religion. Most notably, in an area where religion contributes to attitudes and beliefs of individuals, we find that religiosity improves repayment performance.     

Multivariate multiple regression modelling for technology analysis

Technology analysis is important for technology management areas such as research and development strategy and new product development. So many studies on technology analysis have been used across a diverse array of fields. Most of these were based on patent analysis, which analyses patent documents using text mining and statistics. The studies on conventional patent analyses constructed models consisting of various independent variables (technologies) and one dependent variable. But in reality, we have to consider a model that includes several dependent variables at the same time, because most technologies influence each other. In this paper, we propose a methodology for patent analysis that reflects the various response technologies simultaneously. We perform multivariate multiple regression modelling in order to efficiently conduct our technology analysis. To show how our modelling can be applied to realistic context, we carry out a case study using the patent documents related to three-dimensional printing technology.     

Modeling household fertility decisions: estimation and testing of censored regression models for count data

"This paper adds to the recent body of research on fertility by estimating and testing censored Poisson regression models and censored negative binomial regression models of household fertility decisions. A novel feature of this study is that in each case the censoring threshold varies from individual to individual. Also, a Lagrange multiplier or score test is used to investigate overdispersion. In these regression models the dependent variable is the number of children. In this situation, censored Poisson regression models and censored negative binomial regression models have statistical advantages over OLS, uncensored Poisson regression models, and uncensored negative binomial regression models. The censored models employed in this study are estimated using panel data collected from the Consumer Expenditure Survey compiled by the [U.S.] Bureau of Labor Statistics."     

地方政府卫生支出效率核算及影响因素实证研究——以中国31个省份面板数据为依据的DEA-Tobit分析

文章利用1997—2007年中国31个省份的面板数据,在DEA-Tobit两阶段分析框架下研究了中国地方政府的卫生支出效率。首先,通过数据包络分析方法(DEA)核算了各省份政府卫生支出的综合技术效率、纯技术效率和规模技术效率。在此基础上,利用受限因变量Tobit模型对效率得分与其影响因素之间的关系进行了实证研究。研究结果表明:中国地方政府的卫生支出效率存在显著的地区差异,而人口密度、居民受教育水平、人均GDP、财政分权和医疗体制改革等社会、经济和政策变量则是造成这种效率差异的重要原因。     

CHARTER SCHOOL COMPETITION AND ITS IMPACT ON EMPLOYMENT SPENDING IN MICHIGAN'S PUBLIC SCHOOLS

This paper seeks to determine how competition from charter schools affects a broad range of employees including instructors, administrators, and support personnel. Three empirical models are estimated utilizing a panel data from Michigan: a fixed effect model, a fixed effect model with lagged dependent variable, and an instrumental variable model. The key findings are that when a school district faces competition from charter schools they spend a larger percentage on instructors (most likely most of this is going toward teachers and not teacher aides), while spending a smaller percentage on employees that support instructors. The models seem to imply that the increased spending on teachers may not be reflected in a salary increase. (JEL H52, H75, I21, I22)     

我国经济增长与环境质量关系的再检验

建立了经济增长与环境质量的关系理论模型,利用1985—2008年全国时间序列数据,通过相关分析、协整分析分别建立了工业三废与经济增长之间的回归模型,对经济增长与环境质量之间的关系进行了实证分析。结果表明:我国环境质量与经济增长之间并不存在EKC"倒U"关系;经济增长、产业结构、技术要素和污染治理都是影响环境质量的重要因素。     

我国金融排除空间差异的影响要素分析

金融排除理论是一门新兴理论。文章介绍了金融排除的现状,并结合我国实际,尝试利用主成分分析、因子分析和聚类分析,建立排序选择模型,确定影响我国金融排除空间差异的影响要素,同时指出金融排除理论深化研究的思路和前景。     

Predicting UK Business Cycle Regimes

This paper uses logistic regression to construct a one‐quarter ahead prediction model for classical business cycle regimes in the UK. The binary dependent variable is obtained by applying simple mechanical rules to date turning points in quarterly real GDP data from 1963 to 1999. Using a range of real and financial leading indicators, several parsimonious one‐quarter‐ahead models are developed for the GDP regimes, with model selection based on the SIC criterion. A real M4 variable is consistently found to have predictive content. One model that performs well combines this with nominal UK and German short‐term interest rates. The role of the latter emphasises the open nature of the UK economy.