In this paper, we consider identification and estimation in panel data discrete choice models when the explanatory variable set includes strictly exogenous variables, lags of the endogenous dependent variable as well as unobservable individual-specific effects. For Ž . the binary logit model with the dependent variable lagged only once, Chamberlain 1993 gave conditions under which the model is not identified. We present a stronger set of conditions under which the parameters of the model are identified. The identification result suggests estimators of the model, and we show that these are consistent and asymptotically normal, although their rate of convergence is slower than the inverse of the square root of the sample size. We also consider identification in the semiparametric case where the logit assumption is relaxed. We propose an estimator in the spirit of the Ž Ž . . conditional maximum score estimator Manski 1987 , and we show that it is consistent. In addition, we discuss an extension of the identification result to multinomial discrete choice models, and to the case where the dependent variable is lagged twice. Finally, we present some Monte Carlo evidence on the small sample performance of the proposed estimators for the binary response model.

No abstract

We investigate the empirical significance of borrowing constraints in the market for consumer loans. We set up a theoretical model of consumer loan demand, which in the presence of credit rationing implies restrictions on the elasticities of loan demand with respect to the interest rate and the maturity of the loan. We estimate these elasticities and test the theoretical implications using micro data from the Consumer Expenditure Survey (1984-1995) on auto loan contracts. The econometric specification that we employ accounts for important features of the data: selection, censoring, and simultaneity. Our results suggest that credit constraints are binding for some groups in the population, in particular for young and low-income households.

No abstract

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.