Recently, considerable emphasis has been placed on the problems arising out of cross-sectional dependence in panel unit root tests. This paper adopts the factor based cross-sectional dependence paradigm of Bai and Ng (2004) but suggests alternative factor extraction methods. Some theoretical results for these methods are provided. Further, a detailed Monte Carlo study of these methods for multi...

In this paper we present a new approach to the specification of dynamic factor models. Our model has three advantages over existing work. Firstly, it is based on a minimal-dimension state-space representation giving some gain in computational efficiency over existing methods. Secondly, it easily accommodates hypothesis tests about the order of the factor-filter. Thirdly, by allowing the factor-...

We propose a dynamic factor model appropriate for panel datasets and develop an estimation algorithm which can handle datasets with large number of subjects and short temporal information. The algorithm uses a two cycle iterative approach for model estimation in such a large dataset. Each iteration consists of two distinct cycles, both following an EM algorithm approach. This iterative process ...

This paper presents a dynamic model of the industrial demands for structures, equipment, and blueand white-collar labor. Our approach is consistent with producers holding rational expectations and optimizing dynamically in the presence of adjustment costs, yet it permits generality of functional form regarding the technology. We represent the technology by a translog input requirement function ...

We discuss the development of dynamic factor models for multivariate financial time series, and the incorporation of stochastic volatility components for latent factor processes. Bayesian inference and computation is developed and explored in a study of the dynamic factor structure of daily spot exchange rates for a selection of international currencies. The models are direct generalizations of...

We propose a semi-parametric and dynamic rank factor model for topic modeling, capable of (i) discovering topic prevalence over time, and (ii) learning contemporary multi-scale dependence structures, providing topic and word correlations as a byproduct. The high-dimensional and time-evolving ordinal/rank observations (such as word counts), after an arbitrary monotone transformation, are well ac...

A simple equilibrium restriction identifies time-varying components of expected returns. Using data from the Fama and French threeand fivefactor models and momentum, we use this restriction to predict factor returns out of sample. We derive a policy for timing exposures to these components and find the modified portfolio generates returns with higher out-of-sample Sharpe ratios than the underly...

I introduce posterior simulation methods for a dynamic latent factor model featuring both mean and variance factors. The cross-sectional dimension may be large, so the methods are applicable to data-rich environments. I apply the new methods in two empirical applications. The first involves a panel of 10 currencies, with daily log returns observed over a decade; the second, a panel of 134 real ...

This paper concerns the modelling of stochastic processes by means of dynamic factor models. In such models the observed process is decomposed into a structured part called the latent process, and a remainder that is called noise. The observed variables are treated in a symmetric way, so that no distinction between inputs and outputs is required. This motivates the condition that also the prior...

Based on the theory of multiple statistical hypotheses testing, we elaborate likelihood-based simultaneous statistical inference methods in dynamic factor models (DFMs). To this end, we work up and extend the methodology of Geweke and Singleton (1981) by proving a multivariate central limit theorem for empirical Fourier transforms of the observable time series. In an asymptotic regime with obse...