This paper considers inference in log-linearized dynamic stochastic general equilibrium (DSGE) models with weakly (including un-) identified parameters. The framework allows for analysis using only part of the spectrum, say at the business cycle frequencies. First, we characterize weak...

The inference for hidden Markov chain models in which the structure is a multiple-equation macroeconomic model raises a number of difficulties that are not as likely to appear in smaller models. One is likely to want to allow for many states in the Markov chain without allowing the number of...

The inference for hidden Markov chain models in which the structure is a multiple-equation macroeconomic model raises a number of difficulties that are not as likely to appear in smaller models. One is likely to want to allow for many states in the Markov chain without allowing the number of...

This paper investigates multivariate Beveridge-Nelson decomposition of key macro aggregate data. We find (a) inflation seems to be dominated by its trend component, and, perhaps as a result of this, the short-term interest rate is also trend dominated; and (b) consumption also seems to be...

Markov models introduce persistence in the mixture distribution. In time series analysis, the mixture components relate to different persistent states characterizing the state-specific time series process. Model specification is discussed in a general form. Emphasis is put on the functional form...

In this paper, we investigate the dynamic response of stock market volatility to changes in monetary policy. Using a vector autoregressive model, our findings reveal a significant and asymmetric response of stock returns and volatility to monetary policy shocks. Although the increase in the...

This paper proposes a parsimoniously time varying parameter vector autoregressive model (with exogenous variables, VARX) and studies the properties of the Lasso and adaptive Lasso as estimators of this model. The parameters of the model are assumed to follow parsimonious random walks, where...

We propose a methodology for producing forecast densities for economic aggregates based on disaggregate evidence. Our ensemble predictive methodology utilizes a linear mixture of experts framework to combine the forecast densities from potentially many component models. Each component represents...

This paper considers the problem of jointly decomposing a set of time series variablesinto cyclical and trend components, subject to sets of stochastic linear restrictionsamong these cyclical and trend components. We derive a closed form solution to anordinary problem featuring homogeneous...

This paper investigates how the ordering of variables affects properties of the time-varying covariance matrix in the Cholesky multivariate stochastic volatility model. It establishes that systematically different dynamic restrictions are imposed when the ratio of volatilities is time-varying....