A review of the general state-space modeling framework. The discussion focuses heavily on the three prediction problems of forecasting, filtering, and smoothing within the state- space context. Numerous examples are provided detailing special cases of the state-space model and its use in solving...

We study the autocovariance structure of a general Markov switching second-order stationary VARMA model. Then we give stable finite order VARMA(p*, q*) representations for those M-state Markov switching VARMA(p, q) processes where the observables are uncorrelated with the regime variables. This...

In this paper, we show how to estimate the parameters of stochastic volatility models using Bayesian estimation and Markov chain Monte Carlo (MCMC) simulations through the approximation of the a-posteriori distribution of parameters. Simulated independent draws are made possible by using...

We show how to compute patterns of variation over time, both among and within countries, that determine the international term structure of interest rates, using maximum likelihood within a linear Gaussian state-space framework. The simultaneous estimation of common factors (shared by all...

Recent literature has argued that exchange rate pass-through (ERPT) into inflation has been declining following a dramatic change in inflation environment during the 1990s. We formally check this hypothesis for a sample of 12 emerging and developed economies, by making use of a state-space model...

We develop a dynamic factor model for panel data with a short time dimension (i.e. n15). Unlike most of the work in the DFM literature where one common factor is estimated for a group of cross sectional units, our interest lies in the estimation of a latent variable for each cross sectional unit...

Traditional unobserved component models assume that the trend, cycle and seasonal components of an individual time series evolve separately over time. Although this assumption has been relaxed in recent papers that focus on trend-cycle interactions, it remains at the core of all seasonal...