Econometrics Journal最新文献

The spatial dynamic panel data (SDPD) model is a standard tool for analysing data with both spatial correlation and dynamic dependences among economic units. Conventional estimation methods rely on the key assumption that the spatial weight matrix is exogenous, which would likely be violated in some empirical applications where spatial weights are determined by economic factors. In this paper, we propose an SDPD model with individual fixed effects in a short time dimension, where the spatial weights can be endogenous and time-varying. We establish the consistency and asymptotic normality of the two-stage instrumental variable (2SIV) estimator and we investigate its finite sample properties using a Monte Carlo simulation. When applying this model to study government expenditures in China, we find strong evidence of spatial correlation and time dependence in making spending decisions among China's provincial governments.

This paper is concerned with applications of mixture models in econometrics. Focused attention is given to semiparametric and nonparametric models that incorporate mixture distributions, where important issues about model specifications arise. For example, there is a significant difference between a finite mixture and a continuous mixture in terms of model identifiability. Likewise, the dimension of the latent mixing variables is a critical issue, in particular when a continuous mixture is used. We present applications of mixture models to address various problems in econometrics, such as unobserved heterogeneity and multiple equilibria. New nonparametric identification results are developed for finite mixture models with testable exclusion restrictions without relying on an identification-at-infinity assumption on covariates. The results apply to mixtures with both continuous and discrete covariates, delivering point identification under weak conditions.

We consider identification and estimation in a nonparametric triangular system with a binary endogenous regressor and nonseparable errors. For identification, we take a control function approach utilizing the Dynkin system idea. We articulate various trade-offs, including continuity, monotonicity and differentiability. For estimation, we use the idea of local instruments under smoothness assumptions, but we do not assume additive separability in latent variables. Our estimator uses nonparametric kernel regression techniques and its statistical properties are derived using the functional delta method. We establish that it is -consistent and has a limiting normal distribution. We apply the method to estimate the returns on a college education. Unlike existing work, we find that returns on a college education are consistently positive. Moreover, the returns curves we estimate are inconsistent with the shape restrictions imposed in those papers.

This paper proposes two statistics for testing error cross-sectional independence in a static linear heterogeneous panel data model by virtue of pairwise augmented regressions. The tests based on the two statistics are extensions to the cross-sectional dependence test and the bias-adjusted Lagrange multiplier test. Unlike the two existing tests that are justified under sequential limits, the newly developed tests can be justified under simultaneous limits without any additional restriction imposed on the cross-sectional and time-series dimensions. Moreover, it is proved that the new tests can even be justified under high dimension, low sample size limits, provided that a homo-rank condition holds. Several simulation experiments are conducted to evaluate the performance of the newly introduced tests. The simulation results show that use of the tests can bring significant improvement, especially in cases of large cross-sectional dimension and small time-series dimension.

This paper characterizes the identified set for finite mixture models with one exclusion restriction on the data-generating process. It provides necessary and sufficient conditions on the observed data for point identification and for when the identified set has measure zero. The results are illustrated in a simulation study and with data from a randomized controlled trial on chemotherapy for colon cancer as well as with data from an observational study used to estimate returns to schooling.

In this paper, we consider forecast combination in a predictive regression. We construct the point forecast by combining predictions from all possible linear regression models, given a set of potentially relevant predictors. We derive the asymptotic risk of least-squares averaging estimators in a local asymptotic framework. We then develop a frequentist model averaging criterion, an asymptotically unbiased estimator of the asymptotic risk, to select forecast weights. Monte Carlo simulations show that our averaging estimator compares favourably with alternative methods, such as weighted AIC, weighted BIC, Mallows model averaging and jackknife model averaging. The proposed method is applied to stock return predictions.

We introduce a class of quantile regression estimators for short panels. Our framework covers static and dynamic autoregressive models, models with general predetermined regressors and models with multiple individual effects. We use quantile regression as a flexible tool to model the relationships between outcomes, covariates and heterogeneity. We develop an iterative simulation-based approach for estimation, which exploits the computational simplicity of ordinary quantile regression in each iteration step. Finally, an application to measure the effect of smoking during pregnancy on birthweight completes the paper.

The estimation of large covariance and precision matrices is fundamental in modern multivariate analysis. However, problems arise from the statistical analysis of large panel economic and financial data. The covariance matrix reveals marginal correlations between variables, while the precision matrix encodes conditional correlations between pairs of variables given the remaining variables. In this paper, we provide a selective review of several recent developments on the estimation of large covariance and precision matrices. We focus on two general approaches: a rank-based method and a factor-model-based method. Theories and applications of both approaches are presented. These methods are expected to be widely applicable to the analysis of economic and financial data.

We study the asymptotic refinements of nonparametric bootstrap for quasi-likelihood ratio type tests of nonlinear restrictions. The bootstrap method applies to extremum estimators, such as quasi-maximum likelihood and generalized method of moments estimators, among others. Unlike existing parametric bootstrap procedures for quasi-likelihood ratio type tests, this bootstrap approach does not require any specific parametric assumption on the data distribution, and constructs the bootstrap samples in a fully nonparametric way. We derive the higher-order improvements of the nonparametric bootstrap compared to procedures based on standard first-order asymptotic theory. We show that the magnitude of these improvements is the same as those of parametric bootstrap procedures currently proposed in the literature. Monte Carlo simulations confirm the reliability and accuracy of the nonparametric bootstrap.