We introduce a nonlinear semi-parametric model that allows for the robust filtering of a common stochastic trend in a multivariate system of cointegrated time series. The observation-driven stochastic trend can be specified using flexible updating mechanisms. The model provides a general...

This paper estimates Taylor rules featuring instabilities in policy parameters, switches in policy shocks' volatility, and time-varying trend inflation using post-WWII U.S. data. The model embedding the stochastic target performs better in terms of data-fit and identification of the changes in...

There is growing literature in macroeconomics, especially on business cycle synchronization, employing different methods of time series clustering. However, even as an unsupervised learning method, this technique requires making choices that are nontrivially influenced by the nature of the data...

The major objective of this paper is to demonstrate, theoretically and empirically, the test of a single structural break/change. Failure to address a structural break can lead to forecasting errors and the general unreliability of a model. Three approaches of testing for structural change are...

Many complex systems display fluctuations between alternative states in correspondence to tipping points. These critical shifts are usually associated with generic empirical phenomena such as strengthening correlations between entities composing the system. In finance, for instance, market...

This paper analyzes how the OECD revises potential output (PO) estimates after recessions. We show that downward revisions are substantial and mostly driven by supply shocks while PO estimates do not significantly react to demand shocks. In addition, revisions are partly caused by avoidable...

The data mining technique of time series clustering is well established in many fields. However, as an unsupervised learning method, it requires making choices that are nontrivially influenced by the nature of the data involved. The aim of this paper is to verify usefulness of the time series...