Learn More
SVARs are widely used for policy analysis and to provide stylized facts for dynamic general equilibrium models. Yet there have been no workable rank conditions to ascertain whether an SVAR is globally identified. When identifying restrictions, such as long-run restrictions, are imposed on impulse responses, there have been no efficient algorithms for(More)
This paper considers estimation and inference in panel vector autoregressions (PVARs) where (i) the individual effects are either random or fixed, (ii) the time-series properties of the model variables are unknown a priori and may feature unit roots and cointegrating relations, and (iii) the time dimension of the panel is short and its cross-sectional(More)
This paper deals with a nonlinear errors-in-variables model where the distributions of the unobserved predictor variables and of the measurement errors are nonparametric. Using the instrumental variable approach, we propose method of moments estimators for the unknown parameters and simulation-based estimators to overcome the possible computational(More)
This paper provides a review of linear panel data models with slope heterogeneity, introduces various types of random coe¢ cients models and suggests a common framework for dealing with them. It considers the fundamental issues of statistical inference of a random coe¢ cients formulation using both the sampling and Bayesian approaches. The paper also(More)
We propose a simple to implement panel data method to evaluate the impacts of social policy. The basic idea is to exploit the dependence among cross-sectional units to construct the counterfactuals. The cross-sectional correlations are attributed to the presence of some (unobserved) common factors. However, instead of trying to estimate the unobserved(More)
This paper considers the problem of consistent model specification tests using series estimation methods. The null models we consider in this paper all contain some nonpara-metric components. A leading case we consider is to test for an additive partially linear model. The null distribution of the test statistic is derived using a central limit theorem for(More)
This paper considers the problem of testing for cross section independence in limited dependent variable panel data models. It derives a Lagrangian multiplier (LM) test and shows that in terms of generalized residuals of Gourieroux, Monfort, Renault and Trognon (1987) it reduces to the LM test of Breusch and Pagan (1980). Due to the tendency of the LM test(More)
Process variation has become a serious concern in nanometer technologies. Designs with competitive margins rely on well-characterized statistical models, which must predict the magnitude and scalability of variability accurately. In this paper, we propose a novel approach in creating the statistical models, which tracks the global variation correlation(More)
We study a partially linear varying coefficient model where the regressors are generated by the mul-tivariate unit root I(1) processes. The influence of the explanatory vectors on the response variable satisfies the semiparametric partially linear structure with the nonlinear component being functional coefficients. The profile likelihood estimation(More)