Modelling cyclical unemployment using machine learning and traditional time series forecasting techniques in the Visegrád group countries
Анотація
This study aims to compare the predicting accuracy of LASSO and fixed effect OLS regression models using in-sample forecasts for inflation and unemployment rates in Visegrád Group countries between 2005 Q1 - 2024 Q2. In light of the failures of Okun’s law and the Phillips Curve, commonly found in the toolkits of central banks, in predicting unemployment during the post-GFC economic recovery, as well as the recent COVID-19 crisis, the authors of this paper advocate for the application of novel time series decomposition tools to estimate cyclical unemployment and its effects on inflation.
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