Estimation of Censored Regression Model: A Simulation Study

We investigate the finite sample performance of several estimators proposed for the panel data Tobit regression model with individual effects, including Honoré estimator, Hansen’s best two-step GMM estimator, the continuously updating GMM estimator, and the empirical likelihood estimator (ELE). The latter three estimators are based on more conditional moment restrictions than the Honoré estimator, and consequently are more efficient in large samples. Although the latter three estimators are asymptotically equivalent, the last two have better finite sample performance. However, our simulation reveals that the continuously updating GMM estimator performs no better, and in most cases is worse than Honoré estimator in small samples. The reason for this finding is that the latter three estimators are based on more moment restrictions that require discarding observations. In our designs, about seventy percent of observations are discarded. The insufficiently few number of observations leads to an imprecise weighted matrix estimate, which in turn leads to unreliable estimates. This study calls for an alternative estimation method that does not rely on trimming for finite sample panel data censored regression model.     

On Calculation of the Extended Gini Coefficient

The conventional formula for estimating the extended Gini coefficient is a covariance formula provided by Lerman and Yitzhaki (1989). We suggest an alternative estimator, obtained by approximating the Lorenz curve by a series of linear segments. In a Monte Carlo experiment designed to assess the relative bias and efficiency of the two estimators, we find that, when using grouped data with 20 or fewer groups, our new estimator has less bias and lower mean squared error than the covariance estimator. When individual observations are used, or the number of groups is 30 or more, there is little or no difference in the performance of the two estimators.     

Estimation of Dynamic Panel Data Sample Selection Models

This paper considers the problem of identification and estimation in panel data sample selection models with a binary selection rule, when the latent equations contain strictly exogenous variables, lags of the dependent variables, and unobserved individual effects. We derive a set of conditional moment restrictions which are then exploited to construct two-step GMM-type estimators for the parameters of the main equation. In the first step, the unknown parameters of the selection equation are consistently estimated. In the second step, these estimates are used to construct kernel weights in a manner such that the weight that any two-period individual observation receives in the estimation varies inversely with the relative magnitude of the sample selection effect in the two periods. Under appropriate assumptions, these "kernel-weighted" GMM estimators are consistent and asymptotically normal. The finite sample properties of the proposed estimators are investigated in a small Monte-Carlo study.     

The maximum number of parameters for the Hausman test when the estimators are from different sets of equations

Hausman (1978) developed a widely-used model specification test that has passed the test of time. In this paper, we show that the asymptotic variance of the difference of the two estimators can be a singular matrix. Three illustrative examples are used, namely an exogeneity test for the linear regression model, a test for the Box–Cox transformation, and a test for sample selection bias.     

The Effect of Protest Votes on the Estimates of WTP for Use Values of Recreational Sites

Contingent valuation studies are often characterized by a considerable number of protest responses, which may cause selectivity bias on the final estimates for WTP. Sample selection models can detect and – if necessary – correct selectivity bias. In economic applications where the relevant dependent variable is continuous, sample selection models are generally estimated using Heckman's 2-step method rather than the FIML estimator. Either method has its own drawback: computational complexity for the FIML method, susceptibility to collinearity problems for the 2-step method. Using data on valuation of forest resources for recreational use, we analyse the performance of the two estimators. In this application, given the presence of some collinearity, the FIML is preferred to the 2-step method. A procedure is outlined to deal with selectivity problems in similar settings.     

On the estimation and forecasting of international migration: how relevant is heterogeneity across countries?

This paper performs a comparative analysis of estimation as well as of out-of-sample forecasting results of more than 20 estimators common in the panel data literature using the data on migration to Germany from 18 source countries in the period 1967–2001. Our results suggest that the choice of an estimation procedure has a substantial impact on the parameter estimates of the migration function. Out-of-sample forecasting results indicate the following: (1) the standard fixed effects estimators clearly outperforms the pooled OLS estimator, (2) both the fixed effects estimators and the hierarchical Bayes estimator exhibit the superior forecast performance, (3) the fixed effects estimators outperform GMM and other instrumental variables estimators, (4) forecasting performance of heterogenous estimators is mediocre in our data set.     

Endogeneity bias and growth regressions

The problem of regressor endogeneity stemming from reverse casuality is one that has plagued economists working in the field of empirical economic growth for some time. This paper attempts to address the relevant magnitude of this issue in the context of growth regressions based on the Solow growth model. The paper develops a method of running Monte Carlo simulations that allows us to generate simulated data that match the moments of observed real-world data typically used in such regressions while simultaneously allowing us to impose arbitrarily high correlations between the steady-state determinants of the Solow model and the unobserved residual term of the data-generating process. After running simulations that represent a wide sample of the mathematically-possible correlations, we conclude that a between estimator or a random effects estimator will deliever a lower average absolute bias across all coefficients than alternative estimators in almost all of our simulations. Conversely, estimators that use within-country variation will generate lower biases when looking solely at rates of convergence. Furthermore, we conclude that these results are robust when restricting our sample of simulations to several subsets of the assumed parameters and to changing our assumptions about country fixed-effects terms.     

The estimation of a hedonic asking and offer rent equation model: An EM algorithm approach

In this paper we claim that modeling housing markets, specifically the rental housing market, should be based on some sort of disequilibrium framework. We posit a model of asking and offer rents in the spirit of a sample selectivity model. This approach allows us not only to test whether landlords and tenants agree upon the marginal evaluations of unit attributes, but also to estimate the impact on rental demand of tenant specific attributes such as family income and race. Since estimators obtained from this procedure are consistent, but not efficient in this two equation context, we employ an EM algorithm in our estimation in order to obtain MLE equivalent estimators. We have found that our data support the use of this disequilibrium approach in that the coefficient estimates for the asking rent equation, under the assumptions of a sample selectivity model, are not coincident with those obtained from an uncorrected equation.     

Simulated Non-Parametric Estimation of Dynamic Models

This paper introduces a new class of parameter estimators for dynamic models, called simulated non-parametric estimators (SNEs). The SNE minimizes appropriate distances between non-parametric conditional (or joint) densities estimated from sample data and non-parametric conditional (or joint) densities estimated from data simulated out of the model of interest. Sample data and model-simulated data are smoothed with the same kernel, which considerably simplifies bandwidth selection for the purpose of implementing the estimator. Furthermore, the SNE displays the same asymptotic efficiency properties as the maximum-likelihood estimator as soon as the model is Markov in the observable variables. The methods introduced in this paper are fairly simple to implement, and possess finite sample properties that are well approximated by the asymptotic theory. We illustrate these features within typical estimation problems that arise in financial economics.     

Public sector transparency and countries’ environmental performance: A nonparametric analysis

Based on the theoretical background of the link between countries’ public sector transparency and environmental policy, our paper provides empirical evidence for a sample of 49 countries for the time period of 1995–2000. Assuming that countries’ environmental performance is a product of their environmental regulations and policies, we utilize a nonparametric estimator for investigating the link with public sector's transparency levels. Specifically, by applying conditional directional distance function models, we incorporate on the measurement of countries’ environmental performance the effect of their public sector transparency levels. In a second stage analysis applying local constant estimators, the results reveal an inverted ‘U’-shape relationship between countries’ public sector transparency levels and their environmental performance.     

Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects

This paper empirically assesses the wage effects of the Job Corps program, one of the largest federally funded job training programs in the U.S. Even with the aid of a randomized experiment, the impact of a training program on wages is difficult to study because of sample selection, a pervasive problem in applied microeconometric research. Wage rates are only observed for those who are employed, and employment status itself may be affected by the training program. This paper develops an intuitive trimming procedure for bounding average treatment effects in the presence of sample selection. In contrast to existing methods, the procedure requires neither exclusion restrictions nor a bounded support for the outcome of interest. Identification results, estimators, and their asymptotic distribution are presented. The bounds suggest that the program raised wages, consistent with the notion that the Job Corps raises earnings by increasing human capital, rather than solely through encouraging work. The estimator is generally applicable to typical treatment evaluation problems in which there is nonrandom sample selection/attrition.     

Small Sample Properties Of Existing And New Estimators Of The Autocorrelation Coefficient And Regression Coefficients In The Context Of Ar(1) Errors

This study systematically and comprehensively investigates the small sample properties of the existing and some new estimators of the autocorrelation coefficient and of the regression coefficients in a linear regression model when errors follow an autoregressive process of order one. The new estimators of autocorrelation coefficient proposed here are based on the jackknife procedure. The jackknife procedure is applied in two alternative ways: first to the regression itself, and second to the residuals of the regression model. Next, the performance of the existing and new estimators of autocorrelation coefficient (thirty-three in total) is investigated in terms of bias and the root mean squared errors. Finally, we have systematically compared all of the estimators of the regression coefficients (again thirty-three) in terms of efficiency and their performance in hypothesis testing. We observe that the performance of the autocorrelation coefficient estimators is dependent upon the degree of autocorrelation and whether the autocorrelation is positive or negative. We do not observe a direct link between the bias and efficiency of an estimator. The performance of the estimators of the regression coefficients also depends upon the degree of autocorrelation. If the efficiency of regression estimator is of concern, then the iterative Prais-Winsten estimator should be used since it is most efficient for the widest range of independent variables and values of the autocorrelation coefficient. If testing of the hypothesis is of concern, then the estimators based on jackknife technique are certainly superior and are highly recommended. However, for negative values of the autocorrelation coefficient, the estimators based on Quenouille procedure and iterative Prais-Winsten estimator are comparable. But, for computational ease iterative Prais-Winsten estimator is recommended.

     

Feasible bias-corrected OLS, within-groups, and first-differences estimators for typical micro and macro AR(1) panel data models

In this paper we suggest several alternative ways of constructing feasible bias-corrected (FBC) pooled least squares, within-groups, and first-differences estimators for AR(1) panel data models. In a Monte Carlo simulation study involving data with the qualities normally encountered by both microeconomists and macroeconomists we found that the estimators proposed seem to possess better finite sample properties than the GMM estimators usually employed in this setting: most FBC estimators are unbiased, even when the time series is highly persistent, display less variability, and are not affected by the relative magnitude of the variances for the individual effect and the idiosyncratic error.     

Do women react differently to incentives? Evidence from experimental data and payroll records

We consider the response to incentives as an explanation for observed productivity differences within a firm that paid its workers piece rates. We provide a framework within which the observed productivity differences can be decomposed into two parts: One due to differences in ability and the other due to differences in the response to incentives. We apply this decomposition to male and female workers from a tree-planting firm in British Columbia, Canada. Our evidence (both experimental and nonexperimental) suggests that there was no difference in the reaction to incentives between male and female planters. The productivity differential male planters enjoyed arose because of differences in ability, interpreted as strength in our application.     

The effect of uncertainty on UK investment authorisation: Homogenous vs. heterogeneous estimators

This paper compares pooled and non-pooled models of UK capital investment using the Confederation of British Industrys (CBI) Industrial Trends Survey, focusing on the impact of uncertainty. The uncertainty measure is based on the cross sectional dispersion of optimism about the future business conditions in the industry in which the firm operates. The panel data estimation shows that uncertainty has quantitatively important negative effects on investment. However, if we look at the estimation results at the industry level, we find a great diversity in both estimated elasticities and t-statistics, providing valuable information not available from the pooled model. Finally, we compare the forecast performances of the above models; this analysis confirms that pooled estimators are generally better than non-pooled estimators in terms of out-of-sample forecast performance, but the difference between the two is not very large.     

What explains the performance of students in a heterogeneous environment? Conditional efficiency estimation with continuous and discrete environmental variables

Efficiency estimations which do not account for the operational environment where production units are operating in may have only a limited value. This article presents a fully nonparametric framework to estimate relative performance of production units when accounting for continuous and discrete background variables. Using insights from recent developments in nonparametric econometrics, we show how conditional efficiency scores can be estimated using a tailored mixed kernel function with a data-driven bandwidth selection. The methodology is applied to the sample of Dutch pupils from the Organization for Economic Co-operation and Development's Programme for International Student Assessment (OECD PISA) data set. We estimate students' performance and the influence of its background characteristics. The results of our application show that several family- and student-specific characteristics have a statistically significant effect on the educational efficiency, while school-level variables do not have impact on performance.     

Rationality tests in the presence of instabilities in finite samples

The importance of expectations in modern macroeconomic models and in particular of policy makers expectations for forward looking policy rules has generated a lot of interest in time series of professional forecasts (including central bank staff forecasts). This has spawned a large literature on the evaluation of forecasts that are not model based or where the model is unknown. Although, the available time series of historical forecasts are typically short, this literature has so far mostly disregarded the small sample properties of the proposed tests and estimators. In this paper we fill this gap in the literature, focusing on a set of recently proposed rationality tests for unstable environments. Using a Monte Carlo study we demonstrate that the asymptotic tests are substantially oversized in finite samples including any sample size that is practically available. We provide finite sample adjusted critical values, that allow those tests to be properly applied to sample sizes of typically available forecasts such as the Survey of Professional Forecasters, the Federal Open Market Committee. The critical values we provide will help to avoid false rejections using those data.     

基于面板数据的西江流域经济发展成本差异分析

成本最小化选择是经济决策的基本规则之一,在区域发展布局上亦然。这就需要对不同类型区域的发展成本做比较分析。横贯我国西中东部、跨越南方四省(区)的西江流域在地理环境、发展成本及发展绩效上均呈现出较大的差异性,是进行此类分析的典型样域。基于面板数据的DEA和综合评价分析表明,西江流域不同域段单位产出的内部、外部和综合成本均与其地理梯度成正向关系,亦即流域发展成本大致呈上游>中游>下游的空间差异性和梯度性。此差异可能意味着需重新审视区域协调发展与均衡发展的关系、战略及政策。     

Long memory in volatilities of German stock returns

We show that there is strong evidence of long-range dependence in the volatilities of several German stock returns. This will be done by applying a method using the difference of the classical log-periodogram regression estimator for the memory parameter and of the tapered periodogram based estimator. Both estimators give similar values for the memory parameter for each series and this indicates long memory. To support our findings we apply also a methodology using the sample variance and a wavelet based estimator to the data. Also these two methods show clear evidence of long-range dependence in the volatilities of German stock returns.First version received: December 2001/Final version received: March 2003The computational assistance of Eleni Mitropoulou and Björn Stollenwerck as well as the helpful comments of two unknown referees are gratefully acknowledged. Research supported by Deutsche Forschungsgemeinschaft under SFB 475. Stock returns were obtained from Deutsche Finanzdatenbank (DFDB), Karlsruhe.     

Posterior Analysis of Environmental Damage Evaluation in Europe

In this paper we consider environmental damage evaluation using two types of econometric models to analyse tree damage due to acid deposits, using cross-country data for several European countries. First, we use a set of univariate Poisson models and second, a multinomial probit model with the difference that we do not observe class specific data. The damage function is parameterized in terms of a number of economic and environmental variables. Statistical inference is conducted using Bayesian methods relying on Markov Chain Monte Carlo simulation. Natural by-products of our method include an implied ranking of countries according to environmental damage, and (exact, finite sample) posterior distributions of average country-specific environmental damage.