cquad: An R and Stata Package for Conditional Maximum Likelihood Estimation of Dynamic Binary Panel Data Models

We illustrate the R package cquad for conditional maximum likelihood estimation of the quadratic exponential (QE) model proposed by Bartolucci and Nigro (2010) for the analysis of binary panel data. The package also allows us to estimate certain modified versions of the QE model, which are based on alternative parametrizations, and it includes a function for the pseudo-conditional likelihood estimation of the dynamic logit model, as proposed by Bartolucci and Nigro (2012). We also illustrate a reduced version of this package that is available in Stata. The use of the main functions of this package is based on examples using labor market data

Enrollment costs, university quality and higher education choices in Italy

In this paper, we analyze the higher education choices of Italian secondary school leavers by addressing the roles of university quality, costs and geographical distance to the institution as well as the relationship between students’ choices and their personal and household’s attributes, such as individual secondary school background and the socio-economic condition of the family of origin. Grounding such decision process on the framework of the Random Utility Model (RUM), we provide empirical evidence on the determinants of students’ choices by estimating a nested logit model on the ISTAT survey of secondary school graduates. Results show that the effects of increasing costs of enrollments and university standards are strongly differentiated across sub-groups of individuals. In particular, the choice probability of weaker students, in the sense of secondary school background and household’s socio–economic condition, is more sensitive to changes in university costs and quality

Enrollment costs, university quality and higher education choices in Italy

In this paper, we analyze the higher education choices of Italian secondary school leavers by addressing the roles of university quality, costs and geographical distance to the institution as well as the relationship between students’ choices and their personal and household’s attributes, such as individual secondary school background and the socio-economic condition of the family of origin. Grounding such decision process on the framework of the Random Utility Model (RUM), we provide empirical evidence on the determinants of students’ choices by estimating a nested logit model on the ISTAT survey of secondary school graduates. Results show that the effects of increasing costs of enrollments and university standards are strongly differentiated across sub-groups of individuals. In particular, the choice probability of weaker students, in the sense of secondary school background and household’s socio–economic condition, is more sensitive to changes in university costs and quality

cquad: An R and Stata Package for Conditional Maximum Likelihood Estimation of Dynamic Binary Panel Data Models

We illustrate R package cquad for conditional maximum likelihood estimation of the quadratic exponential (QE) model proposed by Bartolucci and Nigro (2010) for the analysis of binary panel data. The package also allows us to estimate certain modified versions of the QE model, which are based on alternative parametrizations, and it includes a function for the pseudo conditional likelihood estimation of the dynamic logit model, as proposed by Bartolucci and Nigro (2012). We also illustrate a reduced version of this package that is available in Stata. The use of the main functions of this package is based on examples using labor market data

Partial effects estimation for fixed-effects logit panel data models

We propose a multiple step procedure to estimate Average Partial Effects (APE) in fixed-effects panel logit models. Because the incidental parameters problem plagues the APEs via both the inconsistent estimates of the slope and individual parameters, we reduce the bias by evaluating the APEs at a fixed-T consistent estimator for the slope coefficients and at a bias corrected estimator for the unobserved heterogeneity. The proposed estimator has bias of order O(T −2 ) as n → ∞ and performs well in finite sample, even when n is much larger than T . We provide a real data application based on the labor supply of married women

MCMC Conditional Maximum Likelihood for the two-way fixed-effects logit

We propose a Markov chain Monte Carlo Conditional Maximum Likelihood (MCMC-CML) estimator for two-way fixed-effects logit models for dyadic data. The proposed MCMC approach, based on a Metropolis algorithm, allows us to overcome the computational issues of evaluating the probability of the outcome conditional on nodes in and out degrees, which are sufficient statistics for the incidental parameters. Under mild regularity conditions, the MCMC-CML estimator converges to the exact CML one and is asymptotically normal. Moreover, it is more efficient than the existing pairwise CML estimator. We study the finite sample properties of the proposed approach by means of a simulation study and three empirical applications, where we also show that the MCMC-CML estimator can be applied to binary logit models for panel data with both subject and time fixed effects. Results confirm the expected theoretical advantage of the proposed approach, especially with small and sparse networks or with rare events in panel data

A misspecification test for finite-mixture logistic models for clustered binary and ordered responses

An alternative to using normally distributed random effects in modeling clustered binary and ordered responses is based on using a finite-mixture. This approach gives rise to a flexible class of generalized linear mixed models for item responses, multilevel data, and longitudinal data. A test of misspecification for these finite-mixture models is proposed which is based on the comparison between the Marginal and the Conditional Maximum Likelihood estimates of the fixed effects as in the Hausman’s test. The asymptotic distribution of the test statistic is derived; it is of chi-squared type with a number of degrees of freedom equal to the number of covariates that vary within the cluster. It turns out that the test is simple to perform and may also be used to select the number of components of the finite-mixture, when this number is unknown. The approach is illustrated by a series of simulations and three empirical examples covering the main fields of application

A misspecification test for finite-mixture logistic models for clustered binary and ordered responses

An alternative to using normally distributed random effects in modeling clustered binary and ordered responses is based on using a finite-mixture. This approach gives rise to a flexible class of generalized linear mixed models for item responses, multilevel data, and longitudinal data. A test of misspecification for these finite-mixture models is proposed which is based on the comparison between the Marginal and the Conditional Maximum Likelihood estimates of the fixed effects as in the Hausman’s test. The asymptotic distribution of the test statistic is derived; it is of chi-squared type with a number of degrees of freedom equal to the number of covariates that vary within the cluster. It turns out that the test is simple to perform and may also be used to select the number of components of the finite-mixture, when this number is unknown. The approach is illustrated by a series of simulations and three empirical examples covering the main fields of application

Testing for state dependence in binary panel data with individual covariates

We propose a test for state dependence in binary panel data under the dynamic logit model with individual covariates. For this aim, we rely on a quadratic exponential model in which the association between the response variables is accounted for in a different way with respect to more standard formulations. The level of association is measured by a single parameter that may be estimated by a conditional maximum likelihood approach. Under the dynamic logit model, the conditional estimator of this parameter converges to zero when the hypothesis of absence of state dependence is true. This allows us to implement a Wald test for this hypothesis which may be very simply performed and attains the nominal significance level under any structure of the individual covariates. Through an extensive simulation study, we find that our test has good finite sample properties and it is more robust to the presence of (autocorrelated) covariates in the model specification in comparison with other existing testing procedures for state dependence. The test is illustrated by an application based on data coming from the Panel Study of Income Dynamics