Alternative versions of the RESET test for binary response index models: a comparative study

Binary response index models may be affected by several forms of misspecification, which range from pure functional form problems (e.g. incorrect specification of the link function, neglected heterogeneity, heteroskedasticity) to various types of sampling issues (e.g. covariate measurement error, response misclassification, endogenous stratification, missing data). In this paper we examine the ability of several versions of the RESET test to detect such misspecifications in an extensive Monte Carlo simulation study. We find that: (i) the best variants of the RESET test are clearly those based on one or two fitted powers of the response index; and (ii) the loss of power resulting from using the RESET instead of a test directed against a specific type of misspecification is very small in many cases.Binary models; RESET; Misspecification.

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

In this paper we examine theoretically and by simulation whether or not unobserved heterogeneity independent of the included regressors is really an issue in logit, probit and loglog models with both binary and fractional data. We found that unobserved heterogeneity: (i) produces an attenuation bias in the estimation of regression coefficients; (ii) is innocuous for logit estimation of average sample partial effects, while in the probit and loglog cases there may be important biases in the estimation of those quantities; (iii) has much more destructive effects over the estimation of population partial effects; (iv) only for logit models does not affect substantially the prediction of outcomes; and (v) is innocuous for the size and consistency of Wald tests for the significance of observed regressors but, in small samples, reduces their power substantially.Binary models; fractional models; neglected heterogeneity; partial effects; prediction; wald tests.

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.

Covariate measurement error : bias reduction under response-based sampling

In this paper we propose a general framework to deal with the presence of covariate measurement error (CME) in response-based (RB) samples. Using Chesher’s (1991) methodology, we obtain a small error variance approximation for the contaminated sampling distributions that characterise RB samples with CME. Then, following Chesher (2000), we develop generalised method of moments (GMM) estimators that reduce the bias of the most well known likelihood-based estimators for RB samples which ignore the existence of CME and derive a score test to detect the presence of this type of measurement error. Our approach only requires the specification of the conditional distribution of the response variable given the latent covariates and the classical additive measurement error model assumption, the availability of information on both the marginal probability of the strata in the population and the variance of the measurement error not being essential. Monte Carlo evidence is presented which suggests that, in RB samples of moderate sizes, the bias-reduced GMM estimators perform wellinfo:eu-repo/semantics/publishedVersio

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

Moment-based estimation of nonlinear regression models with boundary outcomes and endogeneity, with applications to nonnegative and fractional responses

In this article, we suggest simple moment-based estimators to deal with unobserved heterogeneity in a special class of nonlinear regression models that includes as main particular cases exponential models for nonnegative responses and logit and complementary loglog models for fractional responses. The proposed estimators: (i) treat observed and omitted covariates in a similar manner; (ii) can deal with boundary outcomes; (iii) accommodate endogenous explanatory variables without requiring knowledge on the reduced form model, although such information may be easily incorporated in the estimation process; (iv) do not require distributional assumptions on the unobservables, a conditional mean assumption being enough forconsistentestimationofthestructuralparameters;and(v)undertheadditionalassumption that the dependence between observables and unobservables is restricted to the conditional mean, produce consistent estimators of partial effects conditional only on observables.info:eu-repo/semantics/publishedVersio

Combining micro and macro data in hedonic price indexes

This paper proposes arithmetic and geometric Paasche quality-adjusted price indexes that combine micro data from the base period with macro data on the averages of asset prices and characteristics at the index period. The suggested indexes have two types of advantages relative to traditional Paasche indexes: (i) simplification and cost reduction of data acquisition and manipulation; and (ii) potentially greater efficiency and robustness to sampling problems. A Monte Carlo simulation study and an empirical application concerning the housing market illustrate some of those advantages.info:eu-repo/semantics/publishedVersio

The effect of income on the energy mix: Are democracies more sustainable?

This paper shows that the effect of income on the energy mix depends on the democracy level. We find that more democratic countries tend to depart from the hydroelectric power, oil, and geothermal sources of energy to rely on coal, natural gas, and modern renewable sources (nuclear, biomass, wind, and others). Less democratic countries tend to become more dependent on oil and natural gas with their own development. Moreover, the energy ladder transition from hydroelectric sources to natural gas appears to be escalated more quickly for less democratic countries. These transitions are thus more environmental friendly for more democratic countries than for less democratic ones. An extended multinomial fractional regression model is proposed to test and deal with the endogeneity of income.info:eu-repo/semantics/publishedVersio