Improvement of Regression Forecasting Models

Abstract

In this paper authors propose the technique, which decreases average forecast error of regression based models. The main idea of the method is to use the weighted sum of several regression equations, which satisfy Ordinary Least Squares prerequisites and have independent residuals, instead of only one. It is shown that if all method requirements are met, it is possible to decrease Mean Squared Error almost by half, using just three equations. This technique allows deriving equations which contain more predictors than the number of observations. Additionally, this method proves to be more consistent in time than any of regressions, used in it, separately. It is also illustrated, that the proposed method outperforms the regression equation, computed with the same independent variables, and, thus, it gives more accurate estimators of regression coefficients. Empirical results are provided as well.

Energy System Structure in Russian Arctic: Coal Production Forecast

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“…As a result, the Sakhalin -Khabarovsk -Vladivostok gas pipeline was built, gas exports through which began in 2009, when Gazprom began selling LNG to Japan and Korea under the Sakhalin-II project». This helped Gazprom initially to enter the markets in the far East, and subsequently, relying on the Sakhalin-3 LNG project, together with China's Sinopec, to expand supplies to domestic and foreign consumers (Zubakin et al, 2015;Tryndina et al, 2020;Yumashev and Mikhaylov, 2020).…”

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