Unobserved Heterogeneity in the Binary Logit Model with Cross-Sectional Data and Short Panels: A Finite Mixture Approach

This paper proposes a new approach to dealing with unobserved heterogeneity in applied research using the binary logit model with cross-sectional data and short panels. Unobserved heterogeneity is particularly important in non-linear regression models such as the binary logit model because, unlike in linear regression models, estimates of the effects of observed independent variables are biased even when omitted independent variables are uncorrelated with the observed independent variables. We propose an extension of the binary logit model based on a finite mixture approach in which we conceptualize the unobserved heterogeneity via latent classes. Simulation results show that our approach leads to considerably less bias in the estimated effects of the independent variables than the standard logit model. Furthermore, because identification of the unobserved heterogeneity is weak when the researcher has cross-sectional rather than panel data, we propose a simple approach that fixes latent class weights and improves identification and estimation. Finally, we illustrate the applicability of our new approach using Canadian survey data on public support for redistribution.binary logit model; unobserved heterogeneity; latent classes; simulation

Adequacy of multinomial logit model with nominal responses over binary logit model.

The aim of this study was to fit a multinomial logit model and check whether any gain achieved by this complicated model over binary logit model. It is quite common in practice, the categorical response have more than two levels. Multinomial logit model is a straightforward extension of binary logit model. When response variable is nominal with more than two levels and the explanatory variables are mixed of interval and nominal scale, multinomial logit analysis is appropriate than binary logit model. The maximum likelihood method of estimation is employed to obtain the estimates and consequently Wald test and likelihood ratio test have been used. The findings suggest that parameter estimates under two logits were similar since neither Wald statistic was significant. Thus, it can be concluded that complicated multinomial logit model was no better than the simpler binary logit model. In case of response variable having more than two levels in categorical data analysis, it is strongly recommended that the adequacy of the multinomial logit model over binary logit model should be justified in its fitting process

Model-building of multiple binary logit using model averaging

Many researchers had been carried out on the study of statistical modelling, making it easier for new researchers in many sectors (social sciences, economics, medical, and etc.) to obtain knowledge in order to ease their research study. Nevertheless, there is still no agreed guidelines in obtaining the best model for multiple binary logit (MBL) using model averaging (MA). This research will demonstrate the proper guidelines to obtain best MBL model by using MA. Upper Gastrointestinal Bleed (UGIB) data were studied to illustrate the process of model-building using the proposed guidelines. This study will pinpoint the factors with high possibility leading to mortality of UGIB patients using obtained best model. Corrected Akaike Information Criteria (AICc) and Bayesian Information Criteria (BIC) were used to compute the weights in model averaging method. The performance of the models was computed by using Root mean square error (RMSE) and mean absolute error (MAE). Model obtained by using BIC weights showed a better performance since the RMSE and MAE values are lower compared to model obtained using AICc weights. The factors that affects the survivability of UGIB patients are shock score, comorbidity and rebleed. In conclusion, model-building of multiple binary logit using model averaging showed a better performance when using BIC

A Binary Logit Analysis of Factors Impacting Adoption of Genetically Modified Cotton

Agricultural Resource Management Survey (ARMS) data for 2003 were used to estimate two binary logit models for two definitions of genetically modified (GM) cottonseed adoption. Results indicate conservation tillage did not positively affect adoption of GM cotton with either of these definitions, while adoption of GM cotton in the previous year did. Refuge cotton also did not affect these adoption decisions for the study year.Agricultural Resource Management Survey (ARMS), binary logit model, conservation tillage, cotton, genetically modified seed, herbicide-resistant cotton, jackknife procedure, refuge cotton, stacked-gene cotton, technology adoption, Crop Production/Industries,

Local Nonlinear Least Squares Estimation: Using Parametric Information Nonparametrically

We introduce a new kernel smoother for nonparametric regression that uses prior information on regression shape in the form of a parametric model. In effect, we nonparametrically encompass the parametric model. We derive pointwise and uniform consistency and the asymptotic distribution of our procedure. It has superior performance to the usual kernel estimators at or near the parametric model. It is particularly well motivated for binary data using the probit or logit parametric model as a base. We include an application to the Horowitz (1993) transport choice dataset.Kernel, nonparametric regression, parametric regression, binary choice

Average elasticity in the framework of the fixed effects logit model

This note proposes the average elasticity of the logit probabilities with respect to the exponential functions of explanatory variables in the framework of the fixed effects logit model. The average elasticity is able to be calculated using the consistent estimators of parameters of interest and the average of binary dependent variables, regardless of the fixed effects.average elasticity; fixed effects logit model

Clarify: Software for Interpreting and Presenting Statistical Results

Clarify is a program that uses Monte Carlo simulation to convert the raw output of statistical procedures into results that are of direct interest to researchers, without changing statistical assumptions or requiring new statistical models. The program, designed for use with the Stata statistics package, offers a convenient way to implement the techniques described in: Gary King, Michael Tomz, and Jason Wittenberg (2000). "Making the Most of Statistical Analyses: Improving Interpretation and Presentation." American Journal of Political Science 44, no. 2 (April 2000): 347-61. We recommend that you read this article before using the software. Clarify simulates quantities of interest for the most commonly used statistical models, including linear regression, binary logit, binary probit, ordered logit, ordered probit, multinomial logit, Poisson regression, negative binomial regression, weibull regression, seemingly unrelated regression equations, and the additive logistic normal model for compositional data. Clarify Version 2.1 is forthcoming (2003) in Journal of Statistical Software.

Conditional Logit with one Binary Covariate: Link between the Static and Dynamic Cases

Disentangling state dependence from unobserved heterogeneity is a common issue in economics. It arises for instance when studying transitions between different states on the labor market. When the outcome variable is binary, one of the usual strategies consists in using a conditional logit model with an appropriate conditioning suitable for a dynamic framework. Although static conditional logit procedures are widely available, these procedures cannot be used directly in a dynamic framework. Indeed, it is inappropriate to use them with a lag dependent variable in the list of regressors. Moreover, reprogramming this kind of procedures in a dynamic framework can prove quite cumbersome because the likelihood can have a very high number of terms when the number of periods increases. Here, we consider the case of a conditional logit model with one binary regressor which can be either exogenous or the lagged dependent variable itself. We provide closed forms for the conditional likelihoods in both cases and show the link between them. These results show that in order to evaluate a conditional logit model with one lag of state dependence and no other covariate, it is possible to simply generate a two variable dataset and use standard procedures originally intended for models without state dependence. Moreover, the closed forms help reduce the computational burden even in the static case in which preimplemented procedures usually exist.conditional logit, state dependence, binary model, incidental parameter

A LOGIT ANALYSIS OF PARTICIPATION IN TENNESSEE'S FOREST STEWARDSHIP PROGRAM

This study determines the likely effect of cost-share incentives on participation in the Tennessee Forest Stewardship Program and identifies other factors that may contribute to participation. A random utility model is used to determine the probability that a landowner will choose to participate in the program. A binary choice model is specified to represent the dichotomous decision and a logit procedure is used to fit the model. Data are obtained from mail surveys of 4,000 randomly selected landowners. Results indicate that attitudes and knowledge of forestry programs may be more influential in a landowner's decision to participate than monetary incentives.Cost-share incentive, Stewardship Incentive Program, Logit, Nonindustrial private forest, NIPF, Participation, Forestry, Trees, Resource /Energy Economics and Policy,