The Cointegrated VAR Model with Deterministic Structural Breaks

• Autorzy: Emilia Gosińska , Aleksander Welfe
• Języki publikacji: EN

Abstrakty

EN

The presence of a binary variable in the cointegrated VAR (CVAR) model is most often interpreted as the structural break affecting the data generating process. It is proved in the paper that to enjoy this interpretation the binary variable must appear simultaneously inside and outside the cointegration space. In order to test for the break we advocate to employ the Wald statistic, however, its critical values and the power had to be simulated separately for the possible change of the constant, the trend, and both. The experiments were designed for different sizes of the cointegrating space, number of variables, the span of the break, normally and t-distributed errors. It is shown that the power of the test depends mostly on the magnitude of the break and the sample size while other factors are of secondary importance. In order to test for the break at unknown period the supWald statistic was proposed. (original abstract)

Słowa kluczowe

EN

Vector Autoregression Model (VAR); Hypothesis testing

PL

Model wektorowej autoregresji; Testowanie hipotez

• Czasopismo: Central European Journal of Economic Modelling and Econometrics (CEJEME)
• Rocznik: 2022
• Tom: 14
• Numer: nr 3
• Strony: 335--350

Twórcy

autor

Emilia Gosińska

• University of Lodz, Poland

autor

Aleksander Welfe

• University of Lodz, Poland

Bibliografia

• [1] Andrews D. W. K., (1993), Tests for parameter instability and structural change with unknown change point, Econometrica 61(4), 821-856, DOI: 10.2307/2951764.
• [2] Gosińska E., (2015), Modelowanie procesów ekonomicznych generowanych przez niestacjonarne procesy stochastyczne ze zmianą strukturalną (Modelling of economic processes generated by nonstationary stochastic processes with structural break), unpublished Phd thesis, Biblioteka Uniwersytetu Łódzkiego.
• [3] Johansen S., Nielsen M., (2018), The cointegrated vector autoregressive model with general deterministic terms, Journal of Econometrics 202, 214-229, DOI: 10.1016/j.jeconom.2017.10.003.
• [4] Johansen S., (1995), Likelihood-Based Inference in Cointegrated Vector Autoregressive Models, Oxford University Press.
• [5] Juselius K., (2006), The Cointegrated VAR Model: Methodology and Applications, Oxford University Press.
• [6] Lütkepohl H., (2005), New Introduction to Multiple Time Series Analysis, Springer, Berlin.
• [7] Nielsen H. B., (2004), Cointegration analysis in the presence of outliers, Econometrics Journal 7, 249-271.
• [8] Perron P., Yabu T., (2009), Testing for shifts in trend with an integrated or stationary noise component, Journal of Business and Economic Statistics 27(3), 369-396. DOI: 10.1198/jbes.2009.07268.
• [9] Saikkonen P., Lütkepohl H., (2000), Testing for the cointegrating rank of a VAR process with structural shifts, Journal of Business and Economic Statistics 18, 451-464. DOI: 10.2307/1392226.
• [10] Sobreira N., Nunes L.C., (2012), Testing for Broken Trends in Multivariate Time Series, Universidade Nova de Lisboa Working Paper, available at: https://www.academia.edu/15294230/Testing_for_Broken_Trends_in_ Multivariate_Time_Series.
• [11] Toda H. Y., (1994), Finite sample properties of likelihood ratio tests for cointegrating ranks when linear trends are present, The Review of Economics and Statistics 76(1), 66-79. DOI: 10.2307/2109827.
• [12] Trenkler C., Saikkonen P., Lütkepohl H., (2006), Testing for the Cointegrating Rank of a VAR Process with Level Shift and Trend Break, EUI Working Papers, ECO No. 2006/29, available at: https://cadmus.eui.eu//handle/1814/6306.

Identyfikatory

DOI

10.24425/cejeme.2022.142713