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Persistent link: https://www.econbiz.de/10014228424
tests are accompanied by their bootstrap counterparts due to the limited sample sizes. Using unit-root tests allowing for an … bootstrap tests give results close to those from the asymptotic ones. …
Persistent link: https://www.econbiz.de/10010321602
This paper proposes a bootstrap unit root test in models with GARCH(1,1) errors and establishes its asymptotic validity … under mild moment and distributional restrictions. While the proposed bootstrap test for a unit root shares the power … particular, the bootstrap procedure does not require explicit estimation of nuisance parameters that enter the distribution of …
Persistent link: https://www.econbiz.de/10004968089
the driving stationary series. The situation is analysed from the point of view of bootstrap testing, and an exact … quantitative account is given of the error in rejection probability of a bootstrap test. A particular method of estimating the MA … parameter is recommended, as it leads to very little distortion even when the MA parameter is close to -1. A new bootstrap …
Persistent link: https://www.econbiz.de/10008794177
The asymptotic null distribution of the nonlinear IV panel unit root test due to Chang (2002, Journal of Econometrics 110, 261-292) is examined under the assumption of an invertible general linear process with a weak summability condition. An autoregressive approximation of order p, with p...
Persistent link: https://www.econbiz.de/10014615132
Summary We study a model with an abrupt change in the mean and dependent errors that form a linear process. Different kinds of statistics are considered, such as maximum-type statistics (particularly different CUSUM procedures) or sum-type statistics. Approximations of the critical values for...
Persistent link: https://www.econbiz.de/10014621341
In this paper, we estimate the Shannon entropy S(f)=-E[log(f(x))] of a one-sided linear process with probability density function f(x). We employ the integral estimator Sn(f), which utilizes the standard kernel density estimator fn(x) of f(x). We show that Sn(f) converges to S(f) almost surely...
Persistent link: https://www.econbiz.de/10012611430
Persistent link: https://www.econbiz.de/10011419752
Persistent link: https://www.econbiz.de/10010470638
In this paper, we estimate the Shannon entropy S(f)=-E[log(f(x))] of a one-sided linear process with probability density function f(x). We employ the integral estimator Sn(f), which utilizes the standard kernel density estimator fn(x) of f(x). We show that Sn(f) converges to S(f) almost surely...
Persistent link: https://www.econbiz.de/10012384577