ANALYSIS AND DATA PROCESSING SYSTEMS

The problem of selection of most informative variables from a set of candidate predictors arises when constructing forecast models based on leading indicators. This problem can be solved by embedded methods, such as the LASSO regression, or filter methods, for example, using the correlation-based feature selection. The task of this paper is to compare the performance of these methods with alternative approaches to the time series analysis (ARIMA, the Holt-Winters model, and exponential smoothing). For this, an algorithm for constructing forecast models is proposed, including automatic selection of leading indicators. To conduct an empirical study, indicators suitable for regional labor market predicting were selected from official statistics. They describe indicators such as money supply, a balance sheet structure of credit institutions and a price index. A pseudo-out-of-sample forecasting of a number of indicators characterizing the situation in the registered labor market of the Novosibirsk Region for the period from 2015 to 2018 was carried out. Direct multi-step forecasts were computed for horizons of 6months. It turned out that a stable modification of the LASSO, the percentile-lasso, does not give any advantages in terms of average absolute forecast errors. In most cases, the best results were obtained using the LASSO regression with the choice of the regularization parameter according to the one standard error rule based on block cross-validation with 10 blocks selected at random. Due to the automatic selection of leading indicators, it was possible to reduce forecasting errors in comparison with alternative methods. Thus, the proposed algorithm is appropriate for regional labor market predicting.

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