Likelihood Inference for a Nonstationary Fractional Autoregressive Model

Publikation: Working paperForskning

Standard

Likelihood Inference for a Nonstationary Fractional Autoregressive Model. / Johansen, Søren; Nielsen, Morten Ørregaard.

Department of Economics, University of Copenhagen, 2007.

Publikation: Working paperForskning

Harvard

Johansen, S & Nielsen, MØ 2007 'Likelihood Inference for a Nonstationary Fractional Autoregressive Model' Department of Economics, University of Copenhagen.

APA

Johansen, S., & Nielsen, M. Ø. (2007). Likelihood Inference for a Nonstationary Fractional Autoregressive Model. Department of Economics, University of Copenhagen.

Vancouver

Johansen S, Nielsen MØ. Likelihood Inference for a Nonstationary Fractional Autoregressive Model. Department of Economics, University of Copenhagen. 2007.

Author

Johansen, Søren ; Nielsen, Morten Ørregaard. / Likelihood Inference for a Nonstationary Fractional Autoregressive Model. Department of Economics, University of Copenhagen, 2007.

Bibtex

@techreport{1f9a7200913211dcbee902004c4f4f50,
title = "Likelihood Inference for a Nonstationary Fractional Autoregressive Model",
abstract = "This paper discusses model based inference in an autoregressive model for fractional processes based on the Gaussian likelihood. The model allows for the process to be fractional of order d or d - b; where d = b > 1/2 are parameters to be estimated. We model the data X¿, ..., X¿ given the initial values Xº-n, n = 0, 1, ..., under the assumption that the errors are i.i.d. Gaussian. We consider the likelihood and its derivatives as stochastic processes in the parameters, and prove that they converge in distribution when the errors are i.i.d. with suitable moment conditions and the initial values are bounded. We use this to prove existence and consistency of the local likelihood estimator, and to ?find the asymptotic distribution of the estimators and the likelihood ratio test of the associated fractional unit root hypothesis, which contains the fractional Brownian motion of type II",
keywords = "Faculty of Social Sciences, Dickey-Fuller test, fractional unit root, likelihood inference",
author = "S{\o}ren Johansen and Nielsen, {Morten {\O}rregaard}",
note = "JEL Classification: C22",
year = "2007",
language = "English",
publisher = "Department of Economics, University of Copenhagen",
address = "Denmark",
type = "WorkingPaper",
institution = "Department of Economics, University of Copenhagen",

}

RIS

TY - UNPB

T1 - Likelihood Inference for a Nonstationary Fractional Autoregressive Model

AU - Johansen, Søren

AU - Nielsen, Morten Ørregaard

N1 - JEL Classification: C22

PY - 2007

Y1 - 2007

N2 - This paper discusses model based inference in an autoregressive model for fractional processes based on the Gaussian likelihood. The model allows for the process to be fractional of order d or d - b; where d = b > 1/2 are parameters to be estimated. We model the data X¿, ..., X¿ given the initial values Xº-n, n = 0, 1, ..., under the assumption that the errors are i.i.d. Gaussian. We consider the likelihood and its derivatives as stochastic processes in the parameters, and prove that they converge in distribution when the errors are i.i.d. with suitable moment conditions and the initial values are bounded. We use this to prove existence and consistency of the local likelihood estimator, and to ?find the asymptotic distribution of the estimators and the likelihood ratio test of the associated fractional unit root hypothesis, which contains the fractional Brownian motion of type II

AB - This paper discusses model based inference in an autoregressive model for fractional processes based on the Gaussian likelihood. The model allows for the process to be fractional of order d or d - b; where d = b > 1/2 are parameters to be estimated. We model the data X¿, ..., X¿ given the initial values Xº-n, n = 0, 1, ..., under the assumption that the errors are i.i.d. Gaussian. We consider the likelihood and its derivatives as stochastic processes in the parameters, and prove that they converge in distribution when the errors are i.i.d. with suitable moment conditions and the initial values are bounded. We use this to prove existence and consistency of the local likelihood estimator, and to ?find the asymptotic distribution of the estimators and the likelihood ratio test of the associated fractional unit root hypothesis, which contains the fractional Brownian motion of type II

KW - Faculty of Social Sciences

KW - Dickey-Fuller test

KW - fractional unit root

KW - likelihood inference

M3 - Working paper

BT - Likelihood Inference for a Nonstationary Fractional Autoregressive Model

PB - Department of Economics, University of Copenhagen

ER -

ID: 1523903