Showing 1 - 10 of 203
This paper investigates the accuracy of bootstrap-based bias correction of persistence measures for long memory fractionally integrated processes. The bootstrap method is based on the semi-parametric sieve approach, with the dynamics in the long memory process captured by an autoregressive...
Persistent link: https://www.econbiz.de/10010958957
This paper investigates the accuracy of bootstrap-based inference in the case of long memory fractionally integrated processes. The re-sampling method is based on the semi-parametric sieve approach, whereby the dynamics in the process used to produce the bootstrap draws are captured by an...
Persistent link: https://www.econbiz.de/10010860410
This article describes a new Stata routine, xtbcfe, that performs the iterative bootstrap-based bias correction for the fixed effects (FE) estimator in dynamic panels proposed by Everaert and Pozzi (Journal of Economic Dynamics and Control, 2007). We first simplify the core of their algorithm...
Persistent link: https://www.econbiz.de/10011268870
The detection of long-range dependence in time series analysis is an important task to which this paper contributes by showing that whilst the theoretical definition of a long-memory (or long-range dependent) process is based on the autocorrelation function, it is not possible for long memory to...
Persistent link: https://www.econbiz.de/10011059967
It is shown that the sum of the sample autocorrelation function at lag h≥1 is always −12 for any stationary time series with arbitrary length T≥2 (Hassani, 2009 [1]). In this paper, the distribution of a set of the sample autocorrelation function using the properties of this quantity is...
Persistent link: https://www.econbiz.de/10011063467
Persistent link: https://www.econbiz.de/10008486816
In this paper we will investigate the consequences of applying the sieve bootstrap under regularity conditions that are sufficiently general to encompass both fractionally integrated and non-invertible processes. The sieve bootstrap is obtained by approximating the data generating process by an...
Persistent link: https://www.econbiz.de/10005149091
We apply bootstrap methodology to unit root tests for dependent panels with N cross-sectional units and T time series observations. More specifically, we let each panel be driven by a general linear process which may be different across cross-sectional units, and approximate it by a finite order...
Persistent link: https://www.econbiz.de/10005593302
Persistent link: https://www.econbiz.de/10005596317
Augmented Dickey-Fuller unit root tests may severely overreject when the DGP is a general linear process. The use of the AR sieve bootstrap, proposed by Park (2002) and Chang and Park (2003), may alleviate this problem. We propose sieve bootstraps based on MA and ARMA approximations. Invariance...
Persistent link: https://www.econbiz.de/10005609422