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Weighted Least Squares Approximate Restricted Likelihood Estimation for Vector Autoregressive Processes

by W. Chen
"... We derive a weighted least squares approximate restricted likelihood estimator for a k-dimensional pth order autoregressive model with intercept, for which exact likelihood opti-mization is generally infeasible due to the parameter space which is complicated and high-dimensional, involving pk2 param ..."
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parameters. The weighted least squares estimator has significantly reduced bias and mean squared error than the ordinary least squares estimator for both sta-tionary and non-stationary processes. Furthermore, at the unit root, the limiting distribution of the weighted least squares approximate restricted

THE INTEGRATION ORDER OF VECTOR AUTOREGRESSIVE PROCESSES

by Massimo Franchi, Massimo Franchi
"... Abstract. We show that the order of integration of a vector autoregressive process is equal to the difference between the multiplicity of the unit root in the characteristic equation and the multiplicity of the unit root in the adjoint matrix polynomial. The equivalence with the standard I(1) and I( ..."
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Abstract. We show that the order of integration of a vector autoregressive process is equal to the difference between the multiplicity of the unit root in the characteristic equation and the multiplicity of the unit root in the adjoint matrix polynomial. The equivalence with the standard I(1) and I

Prediction errors in nonstationary autoregressions of infinite order

by Ching-kang Ing, Shu-hui Yu , 2007
"... Assume that observations are generated from a nonstationary autoregressive (AR) processes of infinite order. We adopt a finite-order approximation model to predict future observations and obtain an asymptotic expression for the mean-squared prediction error (MSPE) of the least squares predictor. Thi ..."
Abstract - Cited by 2 (2 self) - Add to MetaCart
Assume that observations are generated from a nonstationary autoregressive (AR) processes of infinite order. We adopt a finite-order approximation model to predict future observations and obtain an asymptotic expression for the mean-squared prediction error (MSPE) of the least squares predictor

Estimation in nonstationary random coefficient autoregressive models

by István Berkes, Lajos Horváth, Shiqing Ling , 903
"... We investigate the estimation of parameters in the random coefficient autoregressive model Xk = (ϕ + bk)Xk−1 + ek, where (ϕ,ω 2,σ 2) is the parameter of the process, Eb 2 0 = ω 2, Ee 2 0 = σ2. We consider a nonstationary RCA process satisfying E log |ϕ + b0 | ≥ 0 and show that σ 2 cannot be estimat ..."
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We investigate the estimation of parameters in the random coefficient autoregressive model Xk = (ϕ + bk)Xk−1 + ek, where (ϕ,ω 2,σ 2) is the parameter of the process, Eb 2 0 = ω 2, Ee 2 0 = σ2. We consider a nonstationary RCA process satisfying E log |ϕ + b0 | ≥ 0 and show that σ 2 cannot

Nonstationary Wishart autoregressive model, Working Paper

by Roxana Chiriac , 2007
"... We propose a modified representation of the Wishart Autoregressive model, introduced by Gourieroux, Jasiak, and Sufana (2004), to capture the dynamics of variance-covariance matrices with large variations in time and high persistence. A specification of the model under nonstationarity and cointegrat ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
We propose a modified representation of the Wishart Autoregressive model, introduced by Gourieroux, Jasiak, and Sufana (2004), to capture the dynamics of variance-covariance matrices with large variations in time and high persistence. A specification of the model under nonstationarity

Cointegrated Vector Autoregressive Systems

by Melanie Beth Rudoy, Melanie Beth Rudoy , 2009
"... The problem of portfolio choice is an example of sequential decision making under un-certainty. Investors must consider their attitudes towards risk and reward in face of an unknown future, in order to make complex financial choices. Often, mathematical models of investor preferences and asset retur ..."
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-variance optimal (MVO) portfolio selection, and that the log-prices of the assets evolve according a simple linear sys-tem known as a cointegrated vector autoregressive (VAR) process. While MVO portfolio choice remains the most popular formulation for single-stage asset allocation problems in both academia

Sparse Vector Autoregressive Modeling

by Richard A. Davis, Pengfei Zang, Tian Zheng , 2012
"... The vector autoregressive (VAR) model has been widely used for modeling temporal de-pendence in a multivariate time series. For large (and even moderate) dimensions, the number of AR coefficients can be prohibitively large, resulting in noisy estimates, unstable predictions and difficult-to-interpre ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
The vector autoregressive (VAR) model has been widely used for modeling temporal de-pendence in a multivariate time series. For large (and even moderate) dimensions, the number of AR coefficients can be prohibitively large, resulting in noisy estimates, unstable predictions and difficult

Toward optimal multistep forecasts in nonstationary autoregressions

by Ching-kang Ing, Jin-lung Lin, Shu-hui Yu , 2008
"... Summary. This paper investigates multistep prediction errors for nonstationary autore-gressive processes with both model order and true parameters unknown. We give asymptotic expressions for the multistep mean squared prediction errors and accumulated prediction er-rors of two important methods, plu ..."
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Summary. This paper investigates multistep prediction errors for nonstationary autore-gressive processes with both model order and true parameters unknown. We give asymptotic expressions for the multistep mean squared prediction errors and accumulated prediction er-rors of two important methods

Non-Stationary Volatility

by Anders Rahbek, A. M. Robert Taylor , 2007
"... Testing for co-integration in vector autoregressions with non-stationary volatility by ..."
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Testing for co-integration in vector autoregressions with non-stationary volatility by

General-to-specific reductions of Vector Autoregressive Processes

by Hans-martin Krolzig - Econometric Studies - A Festschrift in Honour of Joachim Frohn , 2001
"... Unrestricted reduced form vector autoregressive (VAR) models have become a dominant research strategy in empirical macroeconomics since Sims (1980) critique of traditional macroeconometric modeling. They are however subjected to the curse of dimensionality. In this paper we propose general-to-specif ..."
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Unrestricted reduced form vector autoregressive (VAR) models have become a dominant research strategy in empirical macroeconomics since Sims (1980) critique of traditional macroeconometric modeling. They are however subjected to the curse of dimensionality. In this paper we propose general
Results 11 - 20 of 686