Estimating dynamic panel data discrete choice models with fixed effects

This paper considers the estimation of dynamic binary choice panel data models with fixed effects. It is shown that the modified maximum likelihood estimator (MMLE) used in this paper reduces the order of the bias in the maximum likelihood estimator from O(T^-1) to O(T^-2), without increasing the asymptotic variance. No orthogonal reparametrization is needed. Monte Carlo simulations are used to evaluate its performance in finite samples where T is not large. In probit and logit models containing lags of the endogenous variable and exogenous variables, the estimator is found to have a small bias in a panel with eight periods. A distinctive advantage of the MMLE is its general applicability. Estimation and relevance of different policy parameters of interest in this kind of models are also addressed.     

Demand for labour inputs and adjustment costs: evidence from Spanish manufacturing firms

This paper studies the structure of the adjustment costs for heterogeneous labour inputs, allowing for asymmetries and interaction effects among them. To do this, I estimate Euler equations for the demands of permanent nonproduction (white collar) and production (blue collar) employees using a sample of Spanish manufacturing firms. These equations are estimated using the subsample of observations for which adjustment is done, controlling for endogenous sample selection. The main results confirm the heterogeneity of adjustment costs for these two labour inputs, and the existence of cross-adjustment effects between them. Weak evidence of asymmetry in permanent nonproduction labour is also found.     

Identification and estimation of nonlinear dynamic panel data models with unobserved covariates

This paper considers nonparametric identification of nonlinear dynamic models for panel data with unobserved covariates. Including such unobserved covariates may control for both the individual-specific unobserved heterogeneity and the endogeneity of the explanatory variables. Without specifying the distribution of the initial condition with the unobserved variables, we show that the models are nonparametrically identified from two periods of the dependent variable _Yit$Y_{it}$ and three periods of the covariate _Xit$X_{it}$. The main identifying assumptions include high-level injectivity restrictions and require that the evolution of the observed covariates depends on the unobserved covariates but not on the lagged dependent variable. We also propose a sieve maximum likelihood estimator (MLE) and focus on two classes of nonlinear dynamic panel data models, i.e., dynamic discrete choice models and dynamic censored models. We present the asymptotic properties of the sieve MLE and investigate the finite sample properties of these sieve-based estimators through a Monte Carlo study. An intertemporal female labor force participation model is estimated as an empirical illustration using a sample from the Panel Study of Income Dynamics (PSID).     

International propagation of shocks: A dynamic factor model using survey forecasts

This paper studies the pathways for the propagation of shocks across the G7 and major Asia-Pacific countries using multi-horizon forecasts of real GDP growth from 1995 to 2017. We show that if the forecasts are efficient in the long run, the results obtained using these forecasts are comparable to those obtained from the actual outturns. We measure global business cycle connectedness and study the impacts of both country-specific shocks and common international shocks using a panel factor structural VAR model. Our results suggest that there is a strong convergence of business cycles within the group of industrialized countries, as well as within the group of developing economies during non-recessionary periods. In particular, we find an increased decoupling between the industrialized and developing economies after the 2008 recession. However, the direction of shock spillovers during recessions and other crisis periods varies, depending on the nature and origin of the episode.