Template-type: ReDIF-Article 1.0
Author-Name: Urmat Dzhunkeev
Author-Email: dzhunkeev@gmail.com
Author-Workplace-Name: Lomonosov Moscow State University; RANEPA
Title: Forecasting Unemployment in Russia Using Machine Learning Methods
Abstract: In this paper, we forecast the dynamics of unemployment in Russia using several machine learning methods: random forest, gradient boosting, elastic net, and neural networks. The scientific contribution of this paper is threefold. First, along with feed-forward, fully connected neural networks, we use sequence-to-sequence model recurrent neural networks, which take the time-series structure of the sample dataset into account. Second, in addition to univariate long short-term memory models, we include additional macroeconomic indicators in order to estimate multivariate recurrent neural networks. Third, the model evaluation process considers revisions of statistical information in real-time datasets. In order to increase the model's predictive performance, we use additional unstructured indicators: search queries and news indices. Relative to the structural model of unemployment dynamics, the mean absolute forecast error for one month ahead is reduced by 65%, to 0.12 percentage points of the unemployment rate in the recurrent neural networks and long short-term memory models, and by 56%, to 0.14 percentage points in the modified gradient boosting algorithms. When accounting for revisions of statistical information, further reduction of the root-mean-square error by the models proposed is revealed, which highlights the importance of accounting for possible changes in the calculation of the values of macroeconomic indicators.
Classification-JEL: C53, E27, E37
Keywords: unemployment forecasting, machine learning, random forest, elastic net, neural networks, gradient boosting
Journal: Russian Journal of Money and Finance
Pages: 73-87
Volume: 81
Issue: 1
Year: 2022
Month: March
DOI: 10.31477/rjmf.202201.73
File-URL: https://rjmf.econs.online/upload/iblock/53a/Forecasting-Unemployment-in-Russia-Using-Machine-Learning-Methods.pdf 
Handle: RePEc:bkr:journl:v:81:y:2022:i:1:p:73-87