This paper provides a novel mechanism for identifying and estimating latent group structures in panel data using penalized techniques. We consider both linear and nonlinear models where the regression coefficients are heterogeneous across groups but homogeneous within a group and the group membership is unknown. Two approaches are considered -penalized profile likelihood (PPL) estimation for the general nonlinear models without endogenous regressors, and penalized GMM (PGMM) estimation for linear models with endogeneity. In both cases we develop a new variant of Lasso called classifier-Lasso (C-Lasso) that serves to shrink individual coefficients to the unknown group-specific coefficients. CLasso achieves simultaneous classification and consistent estimation in a single step and the classification exhibits the desirable property of uniform consistency. For PPL estimation C-Lasso also achieves the oracle property so that group-specific parameter estimators are asymptotically equivalent to infeasible estimators that use individual group identity information. For PGMM estimation the oracle property of C-Lasso is preserved in some special cases. Simulations demonstrate good finite-sample performance of the approach both in classification and estimation. Empirical applications to both linear and nonlinear models are presented.JEL Classification: C33, C36, C38, C51

Y is conditionally independent of Z given X if Prff ðyjX ; ZÞ ¼ f ðyjX Þg ¼ 1 for all y on its support, where f ðÁjÁÞ denotes the conditional density of Y given ðX ; ZÞ or X : This paper proposes a nonparametric test of conditional independence based on the notion that two conditional distributions are equal if and only if the corresponding conditional characteristic functions are equal. We extend the test of Su and White (2005. A Hellinger-metric nonparametric test for conditional independence. Discussion Paper, Department of Economics, UCSD) in two directions:(1) our test is less sensitive to the choice of bandwidth sequences; (2) our test has power against deviations on the full support of the density of (X ; Y ; Z). We establish asymptotic normality for our test statistic under weak data dependence conditions. Simulation results suggest that the test is well behaved in finite samples. Applications to stock market data indicate that our test can reveal some interesting nonlinear dependence that a traditional linear Granger causality test fails to detect. r

In this paper we consider the problem of frequentist model averaging for quantile regression (QR) when all the  models under investigation are potentially misspecified and the number of parameters in some or all models is diverging with the sample size  To allow for the dependence between the error terms and the regressors in the QR models, we propose a jackknife model averaging (JMA) estimator which selects the weights by minimizing a leave-one-out cross-validation criterion function and demonstrate that the jackknife selected weight vector is asymptotically optimal in terms of minimizing the out-of-sample final prediction error among the given set of weight vectors.We conduct Monte Carlo simulations to demonstrate the finite-sample performance of the proposed JMA QR estimator and compare it with other model selection and averaging methods. We find that the JMA QR estimator can achieve significant efficiency gains over the other methods, especially for extreme quantiles. We apply our JMA method to forecast quantiles of excess stock returns and wages.JEL Classification: C51, C52

We define a new procedure for consistent estimation of nonparametric simultaneous equations models under the conditional mean independence restriction of Newey et al. [1999. Nonparametric estimation of triangular simultaneous equation models. Econometrica 67, 565-603]. It is based upon local polynomial regression and marginal integration techniques. We establish the asymptotic distribution of our estimator under weak data dependence conditions. Simulation evidence suggests that our estimator may significantly outperform the estimators of Pinkse [2000. Nonparametric two-step regression estimation when regressors and errors are dependent. Canadian Journal of Statistics 28, 289-300] and Newey and Powell [2003. Instrumental variable estimation of nonparametric models. Econometrica 71, 1565-1578].

We propose a nonparametric test of conditional independence based on the weighted Hellinger distance between the two conditional densities, f(y|x,z) and f(y|x), which is identically zero under the null. We use the functional delta method to expand the test statistic around the population value and establish asymptotic normality under β-mixing conditions. We show that the test is consistent and has power against alternatives at distance n−1/2h−d/4. The cases for which not all random variables of interest are continuously valued or observable are also discussed. Monte Carlo simulation results indicate that the test behaves reasonably well in finite samples and significantly outperforms some earlier tests for a variety of data generating processes. We apply our procedure to test for Granger noncausality in exchange rates.

a b s t r a c tIn this paper we consider the problem of estimating semiparametric panel data models with cross section dependence, where the individual-specific regressors enter the model nonparametrically whereas the common factors enter the model linearly. We consider both heterogeneous and homogeneous regression relationships when both the time and cross-section dimensions are large. We propose sieve estimators for the nonparametric regression functions by extending Pesaran's (2006) common correlated effect (CCE) estimator to our semiparametric framework. Asymptotic normal distributions for the proposed estimators are derived and asymptotic variance estimators are provided. Monte Carlo simulations indicate that our estimators perform well in finite samples.

Conventional factor models assume that factor loadings are fixed over a long horizon of time, which appears overly restrictive and unrealistic in applications. In this paper, we introduce a time-varying factor model where factor loadings are allowed to change smoothly over time. We propose a local version of the principal component method to estimate the latent factors and time-varying factor loadings simultaneously. We establish the limiting distributions and uniform convergence of the estimated factors and factor loadings in the standard large  and large  framework. We also propose a BIC-type information criterion to determine the number of factors, which can be used in models with either time-varying or time-invariant factor models. Based on the comparison between the estimates of the common components under the null hypothesis of no structural changes and those under the alternative, we propose a consistent test for structural changes in factor loadings. We establish the null distribution, the asymptotic local power property, and the consistency of our test. Simulations are conducted to evaluate both our nonparametric estimates and test statistic. We also apply our test to investigate Stock and Watson's (2009) U.S. macroeconomic data set and find strong evidence of structural changes in the factor loadings. * The authors thank the co-editor Oliver Linton, an associate editor, and three anonymous referees for their constructive comments and suggestions. They also express their sincere appreciation to Serena Ng for discussions on the subject matter.

No abstract