U.S. historical initial jobless claims. Is it different with the coronavirus crisis? A fractional integration analysis.

Abstract

This research paper makes an empirical analysis based on long memory to understand the historical behavior of initial unemployment claims (ICSA) in the United States (U.S.) during all the recession periods and epidemic diseases such as Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS) and COVID-19 since 1967 applying statistical methods based on long range dependence and fractional differentiation. Using unit root/stationarity tests (ADF, PP and KPSS) we discover that the original time series is stationary I(0) and the subsamples are non-stationary I(1). Finally, to analyze the original time series as well as the several periods corresponding to the recessions that occurred in U.S. and the three epidemic diseases, we use AIC and BIC criterion to fit the best ARFIMA model. We conclude that the results display long memory with a degree of integration strictly below 1 (d < 1) for the COVID-19 episode and for the rest of the subsamples, except for the original time series and the 2nd subsample. Thus we can conclude that the impacts will be transient and with long lasting effects of shocks and expecting to disappear on their own in long term. Finally, we use a methodology proposed by Bai and Perron to estimate structural breaks not being necessary to know the time of the breaks in advance. The results are similar to those obtained previously.