Erratum: The likelihood ratio test under nonstandard conditions: Testing the Markov switching model of GNP

Hansen, Bruce E.

doi:10.1002/(sici)1099-1255(199603)11:2<195::aid-jae375>3.0.co;2-2

Cited by 294 publications

(137 citation statements)

References 19 publications

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“…These are the primary sources of intensive computational requirements. In this framework, as pointed out by Hansen (1992Hansen ( , 1996, to perform a Monte Carlo study is not feasible because the computational requirement is enormous. Therefore, to compare the bootstrap and Carrasco et al tests with the Hansen test we use a simpler class of DGP s, which are employed by Hansen (1992Hansen ( , 1996 in his Monte Carlo study.…”

Section: Experimental Designmentioning

confidence: 99%

“…We report the empirical size and power obtained with DGP H 1 and DGP H 3 in Table 3. The size and power obtained by Hansen (1992Hansen ( , 1996 for the corresponding DGP s are reported in Table 4. As we pointed out in the previous section, the Hansen test may be conservative, in fact, we see that this test under-rejects with an empirical size ranging from 0 to 2.…”

Section: Experimental Designmentioning

confidence: 99%

“…Hence, in this case, conventional statistics do not have an asymptotic standard χ 2 -distribution under the null hypothesis. Hansen (1992Hansen ( , 1996 proposes a theory of hypothesis testing which avoids these problems, but his procedure only gives bounds for the LR statistic and requires extensive computation, which involves large scale simulation and optimization over a three-dimensional grid. A less computationally demanding test procedure is discussed in Garcia (1998), but his method is theoretically little attractive since it overlooks the problem of the singular information matrix.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, Carrasco et al (2004) propose a new test for the stability of parameters in MS models which only requires the estimation of the model under the null. This is a great advantage over the tests of Hansen (1992Hansen ( , 1996 and Garcia (1998), since it is well-known that the estimation of MS models is particularly burdensome. In this paper we propose an alternative way to perform a test of linearity in this framework.…”

Section: Introductionmentioning

confidence: 99%

“…We analyze the finite sample properties of the bootstrap test by means of Monte Carlo simulations. More precisely, we compare the size and power obtained with the bootstrap procedure with those obtained using Hansen (1992Hansen ( , 1996 and Carrasco et al (2004) tests. The aim is to provide to applied researchers a guide about the best method to test for linearity.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Testing for linearity in Markov switching models: a bootstrap approach

Sanzo

2007

Stat Methods Appl

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Testing for linearity in the context of Markov switching models is complicated because standard regularity conditions for likelihood based inference are violated. This is due to the fact that, under the null hypothesis of linearity, some parameters are not identified and scores are identically zero. Thus the asymptotic distribution of the test statistic of interest does not possess the standard χ 2 -distribution. In this paper we propose a bootstrap resampling scheme to approximate the distribution of the test statistic of interest under the null of linearity. The procedure is relatively easy to program and the computation requirements are reasonable. We investigate the performance of the bootstrap-based test using Monte Carlo simulations. We find that the test works well and outperforms the Hansen test and the Carrasco et al. test. The use of the various methods is also illustrated by means of empirical examples.

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Section: Experimental Designmentioning

confidence: 99%

Section: Experimental Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Testing for linearity in Markov switching models: a bootstrap approach

Sanzo

2007

Stat Methods Appl

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Switching between chartists and fundamentalists: a Markov regime-switching approach

Vigfusson

1997

Int. J. Fin. Econ.

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Stock market volatility and the business cycle

1996

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This paper investigates the joint time series behavior of monthly stock returns and growth in industrial production. We find that stock returns are well characterized by year‐long episodes of high volatility, separated by longer quiet periods. Real output growth, on the other hand, is subject to abrupt changes in the mean associated with economic recessions. We study a bivariate model in which these two changes are driven by related unobserved variables, and conclude that economic recessions are the primary factor that drives fluctuations in the volatility of stock returns. This framework proves useful both for forecasting stock volatility and for identifying and forecasting economic turning points.

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Erratum: The likelihood ratio test under nonstandard conditions: Testing the Markov switching model of GNP

Cited by 294 publications

References 19 publications

Testing for linearity in Markov switching models: a bootstrap approach

Testing for linearity in Markov switching models: a bootstrap approach

Switching between chartists and fundamentalists: a Markov regime-switching approach

Stock market volatility and the business cycle

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