Bootstrap non-parametric significance test

Gu, Jingping; Li, Dingding; Liu, Dandan

doi:10.1080/10485250701734497

Cited by 18 publications

(34 citation statements)

References 27 publications

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“…N (0; 1) in probability. The …rst part is similar to that in Theorem 6 under H 1 ; see Gu et al (2007) for a similar analysis in testing omitted variables. But there is some di¤erence to show the second part.…”

Section: Proof Of Theoremsupporting

confidence: 49%

Threshold Regression with Endogeneity

Phillips

2014

SSRN Journal

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This paper studies estimation and speci…cation testing in threshold regression with endogeneity. For estimation, there are three results that are di¤erent from those in regular models. First, the threshold point and the parameters of threshold e¤ects can be identi…ed without instruments. Second, in partially linear models, extra randomness in the parametric regressors above the nonparametric regressors is not needed to identify the parametric coe¢ cients. Third, besides identi…cation, instruments can play di¤erent roles to di¤erent parameters. For speci…cation testing, we test whether endogeneity exists and whether there are threshold e¤ects when instruments are absent. Especially, the second test extends the usual parametric structural change tests to the nonparametric case. Furthermore, a wild bootstrap procedure is suggested to obtain critical values for both tests in …nite samples.

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Section: Proof Of Theoremsupporting

confidence: 49%

Threshold Regression with Endogeneity

Phillips

2014

SSRN Journal

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“…For a given variable, * indicates that the bandwidth is higher than twice its sample standard deviation (see Table ). Significance test statistics—Racine, Hart, and Li () for discrete variables and Gu, Li, and Liu () for continuous regressors—and their corresponding bootstrap P ‐values (399 replications) in brackets and parentheses, respectively.…”

Section: Resultsmentioning

confidence: 99%

Regional Growth, Innovation, and Latent Nonlinear Effects

Sanso‐Navarro

Vera-Cabello

2016

Journal of Regional Science

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This paper studies the link between knowledge, innovation, and growth in European regions using nonparametric methods. Our findings suggest that knowledge inputs and the share of innovative firms have a heterogeneous and nonlinear relationship with growth. This evidence has been exploited to examine the consequences of alternative policies using a counterfactual estimation setup, the results of which imply that increasing the formal knowledge base may be optimal in most regions. Less knowledge and innovation intensive regions will also benefit from a higher innovation potential and from a trustworthy and entrepreneurial economic environment.

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“…Under the null hypothesis, the test statistic is asymptotically distributed as a standard normal variate under certain regularity conditions. A number of studies, such as Li and Wang (1998), Li (2005), Li and Racine (2007, Chapter 12), and Gu, Li, and Liu (2007), show that the test of Fan and Li (1996) and Zheng (1996) has good finite sample performance when used in combination with the bootstrap method in various applications. Some of the additional advantages of the bootstrap method are that it allows for heteroskedasticity, it works well with serially correlated data, and the result is insensitive to the choice of the bandwidth.…”

Section: Nonparametric Testsmentioning

confidence: 98%

Diagnosing affine models of options pricing: Evidence from VIX

Zhang

2013

Journal of Financial Economics

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a b s t r a c tAffine jump-diffusion models have been the mainstream in options pricing because of their analytical tractability. Popular affine jump-diffusion models, however, are still unsatisfactory in describing the options data and the problem is often attributed to the diffusion term of the unobserved state variables. Using prices of variance-swaps (i.e., squared VIX) implied from options prices, we provide fresh evidence regarding the misspecification of affine jump-diffusion models, as variance-swap prices are affine functions of the state variables in a broader class of models that do not restrict the diffusion term of the state variables. We apply the nonparametric methodology used by Aït-Sahalia (1996b), supplemented with bootstrap tests and other parametric tests, to the S&P 500 index options data from January 1996 to September 2008. We find that, while the affine diffusion term of the state variables may contribute to the misspecification as the literature has suggested, the affine drift of the state variables, jump intensities, and risk premiums are also sources of misspecification.

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Bootstrap non-parametric significance test

Cited by 18 publications

References 27 publications

Threshold Regression with Endogeneity

Threshold Regression with Endogeneity

Regional Growth, Innovation, and Latent Nonlinear Effects

Diagnosing affine models of options pricing: Evidence from VIX

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