30,208 research outputs found
Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure
This paper presents a new approach to estimation and inference in panel data models with a multifactor error structure where the unobserved common factors are (possibly) correlated with exogenously given individual-specific regressors, and the factor loadings differ over the cross section units. The basic idea behind the proposed estimation procedure is to filter the individual-specific regressors by means of (weighted) cross-section aggregates such that asymptotically as the cross-section dimension (N) tends to infinity the differential effects of unobserved common factors are eliminated. The estimation procedure has the advantage that it can be computed by OLS applied to an auxiliary regression where the observed regressors are augmented by (weighted) cross sectional averages of the dependent variable and the individual specific regressors. Two different but related problems are addressed: one that concerns the coefficients of the individual-specific regressors, and the other that focusses on the mean of the individual coefficients assumed random. In both cases appropriate estimators, referred to as common correlated effects (CCE) estimators, are proposed and their asymptotic distribution as N ¨ âĄ, with T (the time-series dimension) fixed or as N and T¨ ⥠(jointly) are derived under different regularity conditions. One important feature of the proposed CCE mean group (CCEMG) estimator is its invariance to the (unknown but fixed) number of unobserved common factors as N and T¨ ⥠(jointly). The small sample properties of the various pooled estimators are investigated by Monte Carlo experiments that confirm the theoretical derivations and show that the pooled estimators have generally satisfactory small sample properties even for relatively small values of N and T.cross section dependence, large panels, common correlated effects, heterogeneity, estimation and inference
Rethinking the Effective Sample Size
The effective sample size (ESS) is widely used in sample-based simulation
methods for assessing the quality of a Monte Carlo approximation of a given
distribution and of related integrals. In this paper, we revisit and complete
the approximation of the ESS in the specific context of importance sampling
(IS). The derivation of this approximation, that we will denote as
, is only partially available in Kong [1992]. This
approximation has been widely used in the last 25 years due to its simplicity
as a practical rule of thumb in a wide variety of importance sampling methods.
However, we show that the multiple assumptions and approximations in the
derivation of , makes it difficult to be considered even
as a reasonable approximation of the ESS. We extend the discussion of the ESS
in the multiple importance sampling (MIS) setting, and we display numerical
examples. This paper does not cover the use of ESS for MCMC algorithms
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