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Efficient estimation of factor models with time and cross-sectional dependenc...
This paper studies the efficient estimation of large-dimensional factor models with both time and cross-sectional dependence assuming (N,T) separability of the covariance... -
MM Algorithm for General Mixed Multinomial Logit Models (replication data)
This paper develops a new technique for estimating mixed logit models with a simple minorization-maximization (MM) algorithm. The algorithm requires minimal coding and is easy... -
Forecast Rationality Tests in the Presence of Instabilities, with Application...
This paper proposes a framework to implement regression-based tests of predictive ability in unstable environments, including, in particular, forecast unbiasedness and... -
A Social Interactions Model with Endogenous Friendship Formation and Selectiv...
This paper analyzes the endogeneity bias problem caused by associations of members within a network when the spatial autoregressive (SAR) model is used to study social... -
Bayesian Graphical Models for STructural Vector Autoregressive Processes (rep...
This paper proposes a Bayesian, graph-based approach to identification in vector autoregressive (VAR) models. In our Bayesian graphical VAR (BGVAR) model, the contemporaneous... -
Estimation of Dynamic Panel Data Models with Cross-Sectional Dependence: Usin...
This paper considers the estimation of dynamic panel data models when data are suspected to exhibit cross-sectional dependence. A new estimator is defined that uses... -
Replacing Sample Trimming with Boundary Correction in Nonparametric Estimatio...
Two-step nonparametric estimators have become standard in empirical auctions. A drawback concerns boundary effects which cause inconsistencies near the endpoints of the support... -
Simple Identification and Specification of Cointegrated Varma Models (replica...
We bring together some recent advances in the literature on vector autoregressive moving-average models, creating a simple specification and estimation strategy for the...
