Bootstrap Inference on the Boundary of the Parameter Space with Application to Conditional Volatility Models

Publikation: Working paperForskning

Standard

Bootstrap Inference on the Boundary of the Parameter Space with Application to Conditional Volatility Models. / Cavaliere, Giuseppe; Nielsen, Heino Bohn; Pedersen, Rasmus Søndergaard; Rahbek, Anders.

2018.

Publikation: Working paperForskning

Harvard

Cavaliere, G, Nielsen, HB, Pedersen, RS & Rahbek, A 2018 'Bootstrap Inference on the Boundary of the Parameter Space with Application to Conditional Volatility Models'. https://doi.org/10.2139/ssrn.3282935

APA

Cavaliere, G., Nielsen, H. B., Pedersen, R. S., & Rahbek, A. (2018). Bootstrap Inference on the Boundary of the Parameter Space with Application to Conditional Volatility Models. University of Copenhagen. Institute of Economics. Discussion Papers (Online) Nr. 18-10 https://doi.org/10.2139/ssrn.3282935

Vancouver

Cavaliere G, Nielsen HB, Pedersen RS, Rahbek A. Bootstrap Inference on the Boundary of the Parameter Space with Application to Conditional Volatility Models. 2018 dec 5. https://doi.org/10.2139/ssrn.3282935

Author

Cavaliere, Giuseppe ; Nielsen, Heino Bohn ; Pedersen, Rasmus Søndergaard ; Rahbek, Anders. / Bootstrap Inference on the Boundary of the Parameter Space with Application to Conditional Volatility Models. 2018. (University of Copenhagen. Institute of Economics. Discussion Papers (Online); Nr. 18-10).

Bibtex

@techreport{88df3ebb15db4d2dbe54c67ee518d9dd,
title = "Bootstrap Inference on the Boundary of the Parameter Space with Application to Conditional Volatility Models",
abstract = "It is a well-established fact that testing a null hypothesis on the boundary of the parameter space, with an unknown number of nuisance parameters at the boundary, is infeasible in practice in the sense that limiting distributions of standard test statistics are non-pivotal. In particular, likelihood ratio statistics have limiting distributions which can be characterized in terms of quadratic forms minimized over cones, where the shape of the cones depends on the unknown location of the (possibly mulitiple) model parameters not restricted by the null hypothesis. We propose to solve this inference problem by a novel bootstrap, which we show to be valid under general conditions, irrespective of the presence of (unknown) nuisance parameters on the boundary. That is, the new bootstrap replicates the unknown limiting distribution of the likelihood ratio statistic under the null hypothesis and is bounded (in probability) under the alternative. The new bootstrap approach, which is very simple to implement, is based on shrinkage of the parameter estimates used to generate the bootstrap sample toward the boundary of the parameter space at an appropriate rate. As an application of our general theory, we treat the problem of inference in finite-order ARCH models with coefficients subject to inequality constraints. Extensive Monte Carlo simulations illustrate that the proposed bootstrap has attractive finite sample properties both under the null and under the alternative hypothesis.",
keywords = "Faculty of Social Sciences, Inference on the boundary, Nuisance parameters on the boundary, ARCH models, Bootstrap",
author = "Giuseppe Cavaliere and Nielsen, {Heino Bohn} and Pedersen, {Rasmus S{\o}ndergaard} and Anders Rahbek",
year = "2018",
month = dec,
day = "5",
doi = "10.2139/ssrn.3282935",
language = "English",
series = "University of Copenhagen. Institute of Economics. Discussion Papers (Online)",
number = "18-10",
type = "WorkingPaper",

}

RIS

TY - UNPB

T1 - Bootstrap Inference on the Boundary of the Parameter Space with Application to Conditional Volatility Models

AU - Cavaliere, Giuseppe

AU - Nielsen, Heino Bohn

AU - Pedersen, Rasmus Søndergaard

AU - Rahbek, Anders

PY - 2018/12/5

Y1 - 2018/12/5

N2 - It is a well-established fact that testing a null hypothesis on the boundary of the parameter space, with an unknown number of nuisance parameters at the boundary, is infeasible in practice in the sense that limiting distributions of standard test statistics are non-pivotal. In particular, likelihood ratio statistics have limiting distributions which can be characterized in terms of quadratic forms minimized over cones, where the shape of the cones depends on the unknown location of the (possibly mulitiple) model parameters not restricted by the null hypothesis. We propose to solve this inference problem by a novel bootstrap, which we show to be valid under general conditions, irrespective of the presence of (unknown) nuisance parameters on the boundary. That is, the new bootstrap replicates the unknown limiting distribution of the likelihood ratio statistic under the null hypothesis and is bounded (in probability) under the alternative. The new bootstrap approach, which is very simple to implement, is based on shrinkage of the parameter estimates used to generate the bootstrap sample toward the boundary of the parameter space at an appropriate rate. As an application of our general theory, we treat the problem of inference in finite-order ARCH models with coefficients subject to inequality constraints. Extensive Monte Carlo simulations illustrate that the proposed bootstrap has attractive finite sample properties both under the null and under the alternative hypothesis.

AB - It is a well-established fact that testing a null hypothesis on the boundary of the parameter space, with an unknown number of nuisance parameters at the boundary, is infeasible in practice in the sense that limiting distributions of standard test statistics are non-pivotal. In particular, likelihood ratio statistics have limiting distributions which can be characterized in terms of quadratic forms minimized over cones, where the shape of the cones depends on the unknown location of the (possibly mulitiple) model parameters not restricted by the null hypothesis. We propose to solve this inference problem by a novel bootstrap, which we show to be valid under general conditions, irrespective of the presence of (unknown) nuisance parameters on the boundary. That is, the new bootstrap replicates the unknown limiting distribution of the likelihood ratio statistic under the null hypothesis and is bounded (in probability) under the alternative. The new bootstrap approach, which is very simple to implement, is based on shrinkage of the parameter estimates used to generate the bootstrap sample toward the boundary of the parameter space at an appropriate rate. As an application of our general theory, we treat the problem of inference in finite-order ARCH models with coefficients subject to inequality constraints. Extensive Monte Carlo simulations illustrate that the proposed bootstrap has attractive finite sample properties both under the null and under the alternative hypothesis.

KW - Faculty of Social Sciences

KW - Inference on the boundary

KW - Nuisance parameters on the boundary

KW - ARCH models

KW - Bootstrap

U2 - 10.2139/ssrn.3282935

DO - 10.2139/ssrn.3282935

M3 - Working paper

T3 - University of Copenhagen. Institute of Economics. Discussion Papers (Online)

BT - Bootstrap Inference on the Boundary of the Parameter Space with Application to Conditional Volatility Models

ER -

ID: 215683655