We make four contributions in this paper. First, we provide a core of macroeconomic time series usable for systematic research on China. Second, we document, through various empirical methods, the robust findings about striking patterns of trend and cycle. Third, we build a theoretical model...

We make four contributions in this paper. First, we provide a core of macroeconomic time series usable for systematic research on China. Second, we document, through various empirical methods, the robust findings about striking patterns of trend and cycle. Third, we build a theoretical model...

Although the non-financial corporate sector accounts for the lion's share of the post-Global Financial Crisis. We make four contributions in this paper. First, we provide a core of macroeconomic time series usable for systematic research on China. Second, we document, through various empirical...

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...

In the existing literature, conditional forecasts in the vector autoregressive (VAR) framework have not been commonly presented with probability distributions or error bands. This paper develops Bayesian methods for computing such distributions or bands. It broadens the class of conditional...

If multivariate dynamic models are to be used to guide decision-making, it is important that it be possible to provide probability assessments of their results. Bayesian VAR models in the existing literature have not commonly (in fact, not at all as far as we know) been presented with error...