Structural vector autoregressive analysis aims to trace the contemporaneous linkages among (macroeconomic) variables back to underlying orthogonal structural shocks. In homoskedastic Gaussian models the identification of these linkages deserves external and typically notdata-based information....

We introduce the technique of band spectral panel regression (BSPR) to analyze global linkages across sectors and frequency bands. It relies on decomposing time series —allowably measured in mixed observation frequency— into “deviation cycle” dynamics by frequency band. We use it to...

The Hodrick-Prescott filter is a convenient and therefore widely and routinely applied detrending method in macroeconomics working with empirical data. However, James Hamilton has recently gained attention with his vigorous advice against it and a proposal of a better alternative. Before...

We introduce a structural quantile vector autoregressive (VAR) model. Unlike standard VAR which models only the average interaction of the endogenous variables, quantile VAR models their interaction at any quantile. We show how to estimate and forecast multivariate quantiles within a recursive...

The paper assesses the impact of adding information on financial cycles on the output gap estimates for eight advanced economies using two unobserved components models: a reduced form extended Hodrick-Prescott filter, and a standard semi-structural unobserved components model. To complement...

Structural vector autoregressive analysis aims to trace the contemporaneous linkages among (macroeconomic) variables back to underlying orthogonal structural shocks. In homoskedastic Gaussian models the identification of these linkages deserves external and typically notdata-based information....

The authors use first differenced logged quarterly series for the GDP of 29 countries and the euro area to assess the need to use nonlinear models to describe business cycle dynamic behaviour. Their approach is model (estimation)-free, based on testing only. The authors aim to maximize power to...

Hamilton (2017) criticises the Hodrick and Prescott (1981, 1997) filter (HP filter) because of three drawbacks (i. spurious cycles, ii. end-of-sample bias, iii. ad hoc assumptions regarding the smoothing parameter) and proposes a regression filter as an alternative. I demonstrate that Hamilton's...

Hamilton (2018) proposes a regression filter (Hamilton filter) as an alternative to the Hodrick-Prescott filter (HP filter). Using frequency domain analysis, among others, I show that the Hamilton filter improves on the HP filter, because it does not induce spurious cycles and it has a smaller...

Business-cycle adjustment is mostly determined via filter methods, especially the HP filter, or, e.g. within the EU fiscal rules, by a production function approach. James Hamilton put big doubt on the quality of the HP filter estimates, and proposed an alternative regression approach to...