In this paper we propose a test for a set of linear restrictions in a Vector Autoregressive Moving Average (VARMA) model. This test is based on the autoregressive metric, a notion of distance between two univariate ARMA models, M 0 and M 1 , introduced by Piccolo in 1990. In particular, we show that this set of linear restrictions is equivalent to a null distance d(M 0 , M 1 ) between two given ARMA models. This result provides the logical basis for using d(M 0 , M 1 ) = 0 as a null hypothesis in our test. Some Monte Carlo evidence about the finite sample behavior of our testing procedure is provided and two empirical examples are presented.
Testing for a set of linear restrictions in varma models using autoregressive metric: An application to granger causality test / DI IORIO, Francesca; U., Triacca. - In: ECONOMETRICS. - ISSN 2225-1146. - 2:4(2014), pp. 203-216. [10.3390/econometrics2040203]
Testing for a set of linear restrictions in varma models using autoregressive metric: An application to granger causality test
DI IORIO, FRANCESCA;
2014
Abstract
In this paper we propose a test for a set of linear restrictions in a Vector Autoregressive Moving Average (VARMA) model. This test is based on the autoregressive metric, a notion of distance between two univariate ARMA models, M 0 and M 1 , introduced by Piccolo in 1990. In particular, we show that this set of linear restrictions is equivalent to a null distance d(M 0 , M 1 ) between two given ARMA models. This result provides the logical basis for using d(M 0 , M 1 ) = 0 as a null hypothesis in our test. Some Monte Carlo evidence about the finite sample behavior of our testing procedure is provided and two empirical examples are presented.File | Dimensione | Formato | |
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