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An endogenously clustered factor approach to international business cycles (r...
Factor models have become useful tools for studying international business cycles. Block factor models can be especially useful as the zero restrictions on the loadings of some... -
Identifying relevant and irrelevant variables in sparse factor models (replic...
This paper considers factor estimation from heterogeneous data, where some of the variables-the relevant ones-are informative for estimating the factors, and others-the... -
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... -
Model selection with estimated factors and idiosyncratic components (replicat...
This paper provides consistent information criteria for the selection of forecasting models that use a subset of both the idiosyncratic and common factor components of a big... -
Dynamic spatial autoregressive models with autoregressive and heteroskedastic...
We propose a new class of models specifically tailored for spatiotemporal data analysis. To this end, we generalize the spatial autoregressive model with autoregressive and... -
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... -
On the Stability of the Excess Sensitivity of Aggregate Consumption Growth in...
This paper investigates whether there is time variation in the excess sensitivity of aggregate consumption growth to anticipated aggregate disposable income growth using... -
Tests of Predictive Ability for Vector Autoregressions Used for Conditional F...
Many forecasts are conditional in nature. For example, a number of central banks routinely report forecasts conditional on particular paths of policy instruments. Even though...
