Almost Periodically Correlated Time Series in Business Fluctuations Analysis

TL;DR

This paper introduces a non-parametric subsampling method for statistical inference on business cycles, modeling them via Almost Periodically Correlated time series spectrum, and applies it to Polish industrial production data.

Contribution

It presents a novel subsampling procedure for analyzing business cycles using APC time series spectral characteristics, enabling non-parametric filtering of economic fluctuations.

Findings

Successful extraction of business cycle features from Polish industrial data

Demonstration of APC spectral modeling for economic fluctuations

Validation of the filtering approach for business cycle analysis

Abstract

We propose a non-standard subsampling procedure to make formal statistical inference about the business cycle, one of the most important unobserved feature characterising fluctuations of economic growth. We show that some characteristics of business cycle can be modelled in a non-parametric way by discrete spectrum of the Almost Periodically Correlated (APC) time series. On the basis of estimated characteristics of this spectrum business cycle is extracted by filtering. As an illustration we characterise the man properties of business cycles in industrial production index for Polish economy.