Estimation of the Unemployment Rate in Moldova: A Comparison of ARIMA and Machine Learning Models Including COVID-19 Pandemic Periods

Denis Vîntu

MPRA Paper from University Library of Munich, Germany

Abstract: This study investigates the estimation of the unemployment rate in the Republic of Moldova, focusing on the impact of the COVID-19 pandemic. Two forecasting approaches are compared: the traditional ARIMA model and several machine learning models. The performance of these models is evaluated based on prediction accuracy metrics over pre-pandemic and pandemic periods. Results indicate that while ARIMA captures general trends effectively, machine learning models can better adapt to sudden shocks, such as those induced by the pandemic.

Keywords: Simultaneous equations model; Labor market equilibrium; Unemployment rate determination; Wage-setting equation; Price-setting equation; Beveridge curve; Job matching function; Phillips curve; Structural unemployment; Natural rate of unemployment; Labor supply and demand; Endogenous unemployment; Disequilibrium model; Employment dynamics; Wage-unemployment relationship; Aggregate labor market model; Multivariate system estimation; Identification problem; Reduced form equations; Equilibrium unemployment rate (search for similar items in EconPapers)
JEL-codes: C30 C31 C32 C33 C51 J64 J65 J68 (search for similar items in EconPapers)
Date: 2025-08, Revised 2025-08
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