Measuring business cycles: Empirical evidence based on an unobserved component approach

Abstract

We adopt an unobserved components time series model to track the business cycles in the G7 countries using the Industrial production index over the period from 1:1961 to 8:2017. The advantage of adopting the industrial production series frequency is that the business cycle can be investigated in terms of a higher frequency than once per quarter. The aim here is to extract the classical cycle by dating the peaks and troughs and investigating the characteristics of the business cycle through the unobserved component model, which has the capacity to model fat tails data using a driven parameter through the Kalman filter. We find that the industrial production index has medium-term cycles which have a few statistical properties in common. We show that the length and amplitude of the business cycles vary over time and across countries.

Keywords

• unobserved component time series model
• maximum likelihood estimation
• classical cycle
• industrial production index
• medium-term cycles