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686
Weighted Least Squares Approximate Restricted Likelihood Estimation for Vector Autoregressive Processes
"... We derive a weighted least squares approximate restricted likelihood estimator for a kdimensional pth order autoregressive model with intercept, for which exact likelihood optimization is generally infeasible due to the parameter space which is complicated and highdimensional, 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 stationary and nonstationary processes. Furthermore, at the unit root, the limiting distribution of the weighted least squares approximate restricted
THE INTEGRATION ORDER OF VECTOR AUTOREGRESSIVE PROCESSES
"... 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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Cited by 6 (5 self)
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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
, 2007
"... Assume that observations are generated from a nonstationary autoregressive (AR) processes of infinite order. We adopt a finiteorder approximation model to predict future observations and obtain an asymptotic expression for the meansquared prediction error (MSPE) of the least squares predictor. Thi ..."
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Cited by 2 (2 self)
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Assume that observations are generated from a nonstationary autoregressive (AR) processes of infinite order. We adopt a finiteorder approximation model to predict future observations and obtain an asymptotic expression for the meansquared prediction error (MSPE) of the least squares predictor
Estimation in nonstationary random coefficient autoregressive models
, 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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Cited by 2 (0 self)
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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
, 2007
"... We propose a modified representation of the Wishart Autoregressive model, introduced by Gourieroux, Jasiak, and Sufana (2004), to capture the dynamics of variancecovariance matrices with large variations in time and high persistence. A specification of the model under nonstationarity and cointegrat ..."
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Cited by 1 (0 self)
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We propose a modified representation of the Wishart Autoregressive model, introduced by Gourieroux, Jasiak, and Sufana (2004), to capture the dynamics of variancecovariance matrices with large variations in time and high persistence. A specification of the model under nonstationarity
Cointegrated Vector Autoregressive Systems
, 2009
"... The problem of portfolio choice is an example of sequential decision making under uncertainty. 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 logprices of the assets evolve according a simple linear system known as a cointegrated vector autoregressive (VAR) process. While MVO portfolio choice remains the most popular formulation for singlestage asset allocation problems in both academia
Sparse Vector Autoregressive Modeling
, 2012
"... The vector autoregressive (VAR) model has been widely used for modeling temporal dependence 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 difficulttointerpre ..."
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Cited by 1 (0 self)
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The vector autoregressive (VAR) model has been widely used for modeling temporal dependence 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
, 2008
"... Summary. This paper investigates multistep prediction errors for nonstationary autoregressive processes with both model order and true parameters unknown. We give asymptotic expressions for the multistep mean squared prediction errors and accumulated prediction errors of two important methods, plu ..."
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Cited by 1 (1 self)
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Summary. This paper investigates multistep prediction errors for nonstationary autoregressive processes with both model order and true parameters unknown. We give asymptotic expressions for the multistep mean squared prediction errors and accumulated prediction errors of two important methods
NonStationary Volatility
, 2007
"... Testing for cointegration in vector autoregressions with nonstationary volatility by ..."
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Testing for cointegration in vector autoregressions with nonstationary volatility by
Generaltospecific reductions of Vector Autoregressive Processes
 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 generaltospecif ..."
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Cited by 23 (17 self)
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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
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686