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LIKELIHOOD INFERENCE FOR A FRACTIONALLY COINTEGRATED VECTOR AUTOREGRESSIVE MODEL
by
Johansen, Søren
, Nielsen, Morten Ørregaard
in
Applications
/ Asymptotic value
/ Autoregressive models
/ Cofractional processes
/ Cointegration
/ Cointegration analysis
/ cointegration rank
/ Convergence
/ Determinism
/ Distribution
/ Distribution theory
/ Economic models
/ Economic theory
/ Errors
/ Exact sciences and technology
/ fractional cointegration
/ Induktive Statistik
/ Inference
/ Insurance, economics, finance
/ Kointegration
/ likelihood inference
/ Limit theorems
/ Mathematics
/ Maximum likelihood estimation
/ Maximum likelihood estimators
/ Modeling
/ Normal distribution
/ Parametric models
/ Probability and statistics
/ Probability theory
/ Probability theory and stochastic processes
/ Regression analysis
/ Sciences and techniques of general use
/ Stationary processes
/ Statistics
/ Stochastic models
/ Stochastic processes
/ Studies
/ Theorie
/ VAR-Modell
/ Vector autoregression
/ vector autoregressive model
/ Vector-autoregressive models
2012
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LIKELIHOOD INFERENCE FOR A FRACTIONALLY COINTEGRATED VECTOR AUTOREGRESSIVE MODEL
by
Johansen, Søren
, Nielsen, Morten Ørregaard
in
Applications
/ Asymptotic value
/ Autoregressive models
/ Cofractional processes
/ Cointegration
/ Cointegration analysis
/ cointegration rank
/ Convergence
/ Determinism
/ Distribution
/ Distribution theory
/ Economic models
/ Economic theory
/ Errors
/ Exact sciences and technology
/ fractional cointegration
/ Induktive Statistik
/ Inference
/ Insurance, economics, finance
/ Kointegration
/ likelihood inference
/ Limit theorems
/ Mathematics
/ Maximum likelihood estimation
/ Maximum likelihood estimators
/ Modeling
/ Normal distribution
/ Parametric models
/ Probability and statistics
/ Probability theory
/ Probability theory and stochastic processes
/ Regression analysis
/ Sciences and techniques of general use
/ Stationary processes
/ Statistics
/ Stochastic models
/ Stochastic processes
/ Studies
/ Theorie
/ VAR-Modell
/ Vector autoregression
/ vector autoregressive model
/ Vector-autoregressive models
2012
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LIKELIHOOD INFERENCE FOR A FRACTIONALLY COINTEGRATED VECTOR AUTOREGRESSIVE MODEL
by
Johansen, Søren
, Nielsen, Morten Ørregaard
in
Applications
/ Asymptotic value
/ Autoregressive models
/ Cofractional processes
/ Cointegration
/ Cointegration analysis
/ cointegration rank
/ Convergence
/ Determinism
/ Distribution
/ Distribution theory
/ Economic models
/ Economic theory
/ Errors
/ Exact sciences and technology
/ fractional cointegration
/ Induktive Statistik
/ Inference
/ Insurance, economics, finance
/ Kointegration
/ likelihood inference
/ Limit theorems
/ Mathematics
/ Maximum likelihood estimation
/ Maximum likelihood estimators
/ Modeling
/ Normal distribution
/ Parametric models
/ Probability and statistics
/ Probability theory
/ Probability theory and stochastic processes
/ Regression analysis
/ Sciences and techniques of general use
/ Stationary processes
/ Statistics
/ Stochastic models
/ Stochastic processes
/ Studies
/ Theorie
/ VAR-Modell
/ Vector autoregression
/ vector autoregressive model
/ Vector-autoregressive models
2012
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LIKELIHOOD INFERENCE FOR A FRACTIONALLY COINTEGRATED VECTOR AUTOREGRESSIVE MODEL
Journal Article
LIKELIHOOD INFERENCE FOR A FRACTIONALLY COINTEGRATED VECTOR AUTOREGRESSIVE MODEL
2012
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Overview
We consider model based inference in a fractionally cointegrated (or cofractional) vector autoregressive model, based on the Gaussian likelihood conditional on initial values. We give conditions on the parameters such that the process X t is fractional of order d and cofractional of order d — b; that is, there exist vectors β for which βʹX t is fractional of order d — b and no other fractionality order is possible. For b = 1, the model nests the I(d — 1) vector autoregressive model. We define the statistical model by 0 < b ≤ d, but conduct inference when the true values satisfy 0 ≤ d₀ — b₀ < 1/2 and b₀ ≠ 1/2, for which ${{\\mathrm{\\beta }}^{\\prime }}_{0}{\\mathrm{X}}_{\\mathrm{t}}$ is (asymptotically) a stationary process. Our main technical contribution is the proof of consistency of the maximum likelihood estimators. To this end, we prove weak convergence of the conditional likelihood as a continuous stochastic process in the parameters when errors are independent and identically distributed with suitable moment conditions and initial values are bounded. Because the limit is deterministic, this implies uniform convergence in probability of the conditional likelihood function. If the true value b₀ > 1/2, we prove that the limit distribution of ${\\mathrm{T}}^{{\\mathrm{b}}_{0}}(\\hat{\\mathrm{\\beta }}-{\\mathrm{\\beta }}_{0})$ is mixed Gaussian, while for the remaining parameters it is Gaussian. The limit distribution of the likelihood ratio test for cointegration rank is a functional of fractional Brownian motion of type II. If b₀ < 1/2, all limit distributions are Gaussian or chi-squared. We derive similar results for the model with d = b, allowing for a constant term.
Publisher
Econometric Society,Blackwell Publishing Ltd,Wiley-Blackwell
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