In this paper, we estimate the time-varying COVID-19 contact rate of a Susceptible-Infected-Recovered (SIR) model. Our measurement of the contact rate is constructed using data on actively infected, recovered and deceased cases. We propose a new trend filtering method that is a variant of the...

This note gives a fairly complete statistical description of the Hodrick-Prescott Filter (1997) which has been proposed in the context of my seasonal adjustment method (Schlicht 1981, 1984). A statistics estimator for the smoothing parameter is proposed that is asymptotically equivalent to the...

I present empirical results on the contribution of three key drivers of inflation in Denmark: an inflation trend anchored by inflation expectations; the Danish business cycle; and an energy price cycle. All three drivers contribute significantly to the development of inflation and explain most of...

Many seasonal macroeconomic time series are subject to changes in their means and variances over a long time horizon. In this paper we propose a general treatment for the modelling of time-varying features in economic time series. We show that time series models with mean and variance functions...

This article provides new insights into the cyclical behavior of consumer and producer real wages in the USA and Germany. We apply two methods for the estimation of the cyclical components from the data: the approach based on the structural time series models and the ARIMA-model-based approach...

We propose the method of eigenvalue filtering as a new tool to extract time series subcomponents (such as business-cycle or irregular) defined by properties of the underlying eigenvalues. We logically extend the Beveridge-Nelson decomposition of the VAR time-series models focusing on the...

Trend extraction from time series is often performed by using the filter proposed by Leser (1961), also known as the Hodrick-Prescott filter. Practical problems arise, however, if the time series contains structural breaks (as produced by German unification for German time series, for instance),...

We propose multivariate classification as a statistical tool to describe business cycles. These cycles are often analyzed as a univariate phenomenon in terms of GNP or industrial net production ignoring additional information in other economic variables. Multivariate classification overcomes...

We build an equilibrium business cycle model in which agents cannot perfectly distinguish between the permanent and transitory components of TFP shocks and learn about those components using the Kalman filter. Calibrated to Mexico, the model predicts a higher variability of consumption relative...