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INVESTIGATION OF ORDER SELECTION IN THE VECTOR AUTOREGRESSION MODEL
Abstract
The econometrical time series are under investigation in this paper. The mathematical models describing connections between two time series in the form of VAR(p), Vector Autoregression, models are investigated. Their connection is confirmed by means of cointegration tests. If connected time series are described with some models (here the ARIMA, Autoregressive integrated moving average, models) with certain lag orders it?s assumed that maximal orders of ARIMA models are the upper limits for the orders of the VAR model. This point is investigated in the computational experiment. Non-linear terms are included in the VAR model. Such models could describe connections between some time series better. The ordinary least squares method is used to evaluate coefficients in the VAR equations. This method allows non-linear terms to be added into equations. Also there are two ways of ARIMA orders selection. The first one uses values of the information criteria (Akaike and Bayes critera), the second one is based on the correlation functions plots analysis. The maxima locations indicate maximal value of orders of the ARIMA model. Thus there are two ways to select VAR model orders. Both of them are tested. There wage index and money income index time series of the Dynamic series of macroeconomic statistics of the Russian Federation are investigated are investigated in the computational experiment. Investigating non-linear models the VAR model with high coefficient of determination is obtained.
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Thomakos D. D., Guerard J. B. Na??ve, ARIMA, nonparametric, transfer function and VAR models: A comparison of forecasting performance. International Journal of Forecasting. 2004. Vol. 20 (1). pp. 53-67. DOI: 10.1016/S0169-2070(03)00010-4.
Hyndman R. J., Athanasopoulos G. Forecasting: principles and practice. OTexts, 2018. 382 p. ISBN-13: 978-0987507112.
Stock J. H., Watson M. W. Introduction to Econometrics. Pearson. 2011. 796 p.
Petrusevich D. A. Analysis of mathematical models used for econometrical time series forecasting. Rossiyskiy tekhnologicheskiy zhurnal (Russian Technological Journal). 2019. Vol. 7(2). pp. 61 – 73. (in Russ.). DOI: 10.32362/2500-316X-2019-7-2-61-73.
Petrusevich D. A. Time series forecasting using high order ARIMA functions. Proceedings of the International Multidisciplinary Scientific GeoConference: SGEM (Sofia, Bulgaria) 2019. Vol 19. Is 2.1. pp. 673-679. DOI: 10.5593/sgem2019/2.1/S07.088.
Box G., Jenkins G. Time series analysis: Forecasting and Control. John Wiley and Sons, 2008. 712 p. ISBN-13: 978-0470272848
Granger C. Testing for causality: A personal viewpoint. Journal of Economic Dynamics and Control. 1980. Vol. 2. pp.329-252 DOI: 10.1016/0165-1889(80)90069-X
Engle R., Granger C. Cointegration and error correction: representation, evaluation and testing Applied econometrics. 1987. Vol. 39. pp.107 – 135 DOI: 10.2307/1913236
Hanck C., Arnold M. E., Gerber A, Schmelzer M. Introduction to econometrics with R. University of Duisburg Essen. 2019. p. 392
Domit S., Monti F., Sokol A. Forecasting the UK economy with a medium-scale Bayesian VAR. International Journal of Forecasting. 2019. Vol. 35 (4). pp. 1669-1678. DOI: 10.1016/j.ijforecast.2018.11.004.
Chiu C. W., Mumtaz H. Pint?r G. Forecasting with VAR models: Fat tails and stochastic volatility. International Journal of Forecasting. 2017. Vol. 33 (4). pp. 1124-1143. DOI: 10.1016/j.ijforecast.2017.03.001.
Pesaran M. H., Schuermann T., Smith L. V. Forecasting economic and financial variables with global VARs. International Journal of Forecasting. 2009. Vol. 25 (4). pp. 642-675 DOI: 10.1016/j.ijforecast.2009.08.007.
Koop G. Forecasting with dimension switching VARs. International Journal of Forecasting. 2014. Vol. 30 (2). pp. 280 – 290 DOI: 10.1016/j.ijforecast.2013.09.005.
Dynamic series of macroeconomic statistics of the Russian Federation Retrieved from: http://sophist.hse.ru/hse/nindex.shtml (in Russ.)
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