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A sequential test procedure for the choice of the number of regimes in multivariate nonlinear models

Andrea Bucci

arXiv 4 Jun 2024 · Econometrics

arXiv:2406.02152 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper proposes a sequential test procedure for determining the number of regimes in nonlinear multivariate autoregressive models. The procedure relies on linearity and no additional nonlinearity tests for both multivariate smooth transition and threshold autoregressive models. We conduct a simulation study to evaluate the finite-sample properties of the proposed test in small samples. Our findings indicate that the test exhibits satisfactory size properties, with the rescaled version of the Lagrange Multiplier test statistics demonstrating the best performance in most simulation settings. The sequential procedure is also applied to two empirical cases, the US monthly interest rates and Icelandic river flows. In both cases, the detected number of regimes aligns well with the existing literature.

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Tsay, R. S (1998) Testing and Modeling Multivariate Threshold Models1.00093100%
2Strikholm, B. and Teräsvirta, T (2006) A sequential procedure for determining the number of regimes in a threshold autoregressive model1.00084100%
3Camacho, M (2004) Vector smooth transition regression models for US GDP and the composite index of leading indicators1.00084100%
4Luukkonen, R., Saikkonen, P., and Teräsvirta, T (1988) Testing Linearity Against Smooth Transition Autoregressive Models1.00064100%
5Kheifets, I. L. and Saikkonen, P. J (2020) Stationarity and ergodicity of vector STAR models1.00055100%
6Teräsvirta, T. and Yang, Y (2014) Specification, estimation and evaluation of vector smooth transition autoregressive models with applications1.00053100%
7Teräsvirta, T. and Yang, Y (2014) Linearity and Misspecification Tests for Vector Smooth Transition Regression Models0.8947671%
8Eitrheim, . and Teräsvirta, T (1996) Testing the adequacy of smooth transition autoregressive models0.7373367%
9He, C., Teräsvirta, T., and González, A (2008) Testing Parameter Constancy in Stationary Vector Autoregressive Models Against Continuous Change0.73732100%
10Davies, R. B (1987) Hypothesis Testing when a Nuisance Parameter is Present Only Under the Alternatives0.64422100%

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