Within a New Zealand business cycle context, we assess whether Hamilton's (H84) OLS regression methodology produces stylised business cycle facts which are materially different from HP1600 measures, and whether using the H84 predictor and other forecast extensions improves the HP filter's...

Seasonality in macroeconomic time series can obscure movements of other components in a series that are operationally more important for economic and econometric analyses. Indeed, in practice one often prefers to work with seasonally adjusted data to assess the current state of the economy and...

We study how financial factors shape and interact with the U.S. business cycle through a unified empirical approach where we jointly estimate financial and business cycles as well as identify their underlying drivers using a medium-scale Bayesian Vector Autoregression. First, we show, both in...

We demonstrate how Bayesian shrinkage can address problems with utilizing large information sets to calculate trend and cycle via a multivariate Beveridge-Nelson (BN) decomposition. We illustrate our approach by estimating the U.S. output gap with large Bayesian vector autoregressions that...

Recent studies attempt to quantify the empirical importance of news shocks (ie., anticipated future shocks) in business cycle fluctuations. This paper identifies news shocks in a dynamic stochastic general equilibrium model estimated with not only actual data but also forecast data. The...

This paper reconciles two widely used trend-cycle decompositions of GDP that give markedly different estimates: the correlated unobserved components model yields output gaps that are small in amplitude, whereas the Hodrick-Prescott (HP) filter generates large and persistent cycles. By embedding...

Multivariate analysis can help to focus on economic phenomena, including trend and cyclical movements. To allow for potential correlation with seasonality, the present paper studies a three component multivariate unobserved component model, focusing on the case of quarterly data and showing that...

We compare a number of widely used trend-cycle decompositions of output in a formal Bayesian model comparison exercise. This is motivated by the often markedly different results from these decompositions — different decompositions have broad implications for the relative importance of real...