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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... -
SEQUENTIAL MONTE CARLO SAMPLING FOR DSGE MODELS (replication data)
We develop a sequential Monte Carlo (SMC) algorithm for estimating Bayesian dynamic stochastic general equilibrium (DSGE) models; wherein a particle approximation to the... -
The case against JIVE (replication data)
We perform an extensive series of Monte Carlo experiments to compare the performance of two variants of the jackknife instrumental variables estimator, or JIVE, with that of the... -
Estimating dynamic equilibrium economies: linear versus nonlinear likelihood ...
This paper compares two methods for undertaking likelihood-based inference in dynamic equilibrium economies: a sequential Monte Carlo filter and the Kalman filter. The... -
A non-linear filtering approach to stochastic volatility models with an appli...
This paper develops a new model for the analysis of stochastic volatility (SV) models. Since volatility is a latent variable in SV models, it is difficult to evaluate the exact... -
A Monte Carlo study of the forecasting performance of empirical SETAR models ...
In this paper we investigate the multi-period forecast performance of a number of empirical self-exciting threshold autoregressive (SETAR) models that have been proposed in the...
