Bootstrap bias-adjusted GMM estimators

The ability of six alternative bootstrap methods to reduce the bias of GMM parameter estimates is examined in an instrumental variable framework using Monte Carlo analysis. Promising results were found for the two bootstrap estimators suggested in the paper

Bias-corrected Moment-based Estimators for Parametric Models under Endogenous Stratified Sampling

This paper provides an integrated approach for estimating parametric models from endogenous stratified samples. We discuss several alternative ways of removing the bias of the moment indicators usually employed under random sampling for estimating the parameters of the structural model and the proportion of the strata in the population. Those alternatives give rise to a bunch of moment-based estimators which are appropriate for both cases where the marginal strata probabilities are known and unknown. The derivation of our estimators is very simple and intuitive and incorporates as particular cases most of the likelihood-based estimators existing in the literature

Alternative estimating and testing empirical strategies for fractional regression models

In many economic settings, the variable of interest is often a fraction or a proportion, being defined only on the unit interval. The bounded nature of such variables and, in some cases, the possibility of nontrivial probability mass accumulating at one or both boundaries raise some interesting estimation and inference issues. In this paper we: (i) provide a comprehensive survey of the main alternative models and estimation methods suitable to deal with fractional response variables; (ii) propose a full testing methodology to assess the validity of the assumptions required by each alternative estimator; and (iii) examine the finite sample properties of most of the estimators and tests discussed through an extensive Monte Carlo study. An application concerning corporate capital structure choices is also provided.Fractional regression models; Conditional mean tests; Non-nested hypotheses; Zero outcomes; Two-part models.

Fractional regression models for second stage DEA efficiency analyses

Data envelopment analysis (DEA) is commonly used to measure the relative efficiency of decision-making units. Often, in a second stage, a regression model is estimated to relate DEA efficiency scores to exogenous factors. In this paper, we argue that the traditional linear or tobit approaches to second-stage DEA analysis do not constitute a reasonable data-generating process for DEA scores. Under the assumption that DEA scores can be treated as descriptive measures of the relative performance of units in the sample, we show that using fractional regression models are the most natural way of modeling bounded, proportional response variables such as DEA scores. We also propose generalizations of these models and, given that DEA scores take frequently the value of unity, examine the use of two-part models in this framework. Several tests suitable for assessing the specification of each alternative model are also discussed.Second-stage DEA; Fractional data; Specification tests; One outcomes; Two-part models.

Two-step Empirical Likelihood Estimation under Stratified Sampling when Aggregate Information is Available

Empirical likelihood (EL) is appropriate to estimate moment condition models when a random sample from the target population is available. However, many economic surveys are subject to some form of stratification, in which case direct application of EL will produce inconsistent estimators. In this paper we propose a two-step EL (TSEL) estimator to deal with stratified samples in models defined by unconditional moment restrictions in presence of some aggregate information, which may consist, for example, of the mean and the variance of the variable of interest and/or the explanatory variables. A Monte Carlo simulation study reveals promising results for many versions of the TSEL estimator

Is neglected heterogeneity really an issue in binary and fractional regression models? : A simulation exercise for logit, probit and loglog models

Theoretical and simulation analysis is performed to examine whether unobserved heterogeneity independent of the included regressors is really an issue in logit, probit and loglog models with both binary and fractional data. It is found that unobserved heterogeneity has the following effects. First, it produces an attenuation bias in the estimation of regression coefficients. Second, although it is innocuous for logit estimation of average sample partial effects, it may generate biased estimation of those effects in the probit and loglog models. Third, it has much more deleterious effects on the estimation of population partial effects. Fourth, it is only for logit models that it does not substantially affect the prediction of outcomes. Fifth, it is innocuous for the size of Wald tests for the significance of observed regressors but, in small samples, it substantially reduces their power.info:eu-repo/semantics/publishedVersio

A symptotic Bias for GMM and GEL Estimators with Estimated Nuisance Parameter

This papers studies and compares the asymptotic bias of GMM and generalized empirical likelihood (GEL) estimators in the presence of estimated nuisance parameters. We consider cases in which the nuisance parameter is estimated from independent and identical samples. A simulation experiment is conducted for covariance structure models. Empirical likelihood offers much reduced mean and median bias, root mean squared error and mean absolute error, as compared with two-step GMM and other GEL methods. Both analytical and bootstrap bias-adjusted two-step GMM estima-tors are compared. Analytical bias-adjustment appears to be a serious competitor to bootstrap methods in terms of finite sample bias, root mean squared error and mean absolute error. Finite sample variance seems to be little affected

Goodness of Fit Tests for Moment Condition Models

This paper proposes novel methods for the construction of tests for models specified by unconditional moment restrictions. It exploits the classical-like nature of generalized empirical likelihood (GEL) to define Pearson-type statistics for over-identifying moment conditions and parametric constraints based on constrasts of GEL implied probabilities which are natural by-products of GEL estimation. As is increasingly recognized, GEL can possess both theoretical and empirical advantages over the more standard generalized method of moments (GMM). Monte Carlo evidence comparing GMM, GEL and Pearsontype statistics for over-identifying moment conditions indicates that the size properties of a particular Pearson-type statistic is competitive in most and an improvement over other statistics in many circumstances

A two-part fractional regression model for the financial leverage decisions of micro, small, medium and large firms

In this paper we examine the following two hypotheses which traditional theories of capital structure are relatively silent about: (i) the determinants of financial leverage decisions are different for micro, small, medium and large firms; and (ii) the factors that determine whether or not a firm issues debt are different from those that determine how much debt it issues. Using a binary choice model to explain the probability of a firm raising debt and a fractional regression model to explain the relative amount of debt issued, we find strong support for both hypotheses. Confirming recent empirical evidence, we find also that, although larger firms are more likely to use debt, conditional on having some debt firm size is negatively related to the proportion of debt used by firms