Generalized Binary Vector Autoregressive Processes

We extend the conventional cointegrated VAR model to allow for general nonlinear deterministic trends. These nonlinear trends can be used to model gradual structural changes in the intercept term of the cointegrating relations. A general asymptotic theory of estimation and statistical inference is reviewed and a diagnostic test for the correct specification of an employed nonlinear trend is developed. The methods are applied to Finnish interest-rate data. A smooth level shift of the logistic form between the own-yield of broad money and the short-term money market rate is found appropriate for these data. The level shift is motivated by the deregulation of issuing certificates of deposit and its inclusion in the model solves the puzzle of the “missing cointegration vector” found in a previous study.

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Characterization of variable bit rate video traffic as vector autoregressive processes

Proceedings of GLOBECOM 95 GLOCOM-95 ◽

10.1109/glocom.1995.502637 ◽

2002 ◽

Author(s):

Jae-Jin Shin ◽

Jae-Kyoon Kim ◽

Dan Keun Sung

Keyword(s):

Autoregressive Processes ◽

Variable Bit Rate ◽

Bit Rate ◽

Video Traffic ◽

Vector Autoregressive ◽

Vector Autoregressive Processes ◽

Variable Bit Rate Video

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Testing for a Valid Normalization of Cointegrating Vectors in Vector Autoregressive Processes

Journal of Business and Economic Statistics ◽

10.1080/07350015.1999.10524810 ◽

1999 ◽

Vol 17 (2) ◽

pp. 195-204 ◽

Cited By ~ 3

Author(s):

Ritva Luukkonen ◽

Antti Ripatti ◽

Pentti Saikkonen

Keyword(s):

Autoregressive Processes ◽

Vector Autoregressive ◽

Vector Autoregressive Processes

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Testing normalization and overidentification of cointegrating vectors in vector autoregressive processes

Econometric Reviews ◽

10.1080/07474939908800444 ◽

1999 ◽

Vol 18 (3) ◽

pp. 235-257 ◽

Cited By ~ 12

Author(s):

Pentti Saikkonen

Keyword(s):

Autoregressive Processes ◽

Vector Autoregressive ◽

Vector Autoregressive Processes

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Online topology estimation for vector autoregressive processes in data networks

2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) ◽

10.1109/camsap.2017.8313211 ◽

2017 ◽

Cited By ~ 3

Author(s):

Bakht Zaman ◽

Luis M. Lopez-Ramos ◽

Daniel Romero ◽

Baltasar Beferull-Lozano

Keyword(s):

Data Networks ◽

Autoregressive Processes ◽

Vector Autoregressive ◽

Vector Autoregressive Processes ◽

Topology Estimation

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Autoregressive processes in optimization

Journal of Applied Probability ◽

10.2307/3214438 ◽

1988 ◽

Vol 25 (2) ◽

pp. 302-312 ◽

Cited By ~ 1

Author(s):

Tomáš Cipra

Keyword(s):

Dynamic Structure ◽

Rates Of Convergence ◽

Geometric Ergodicity ◽

Autoregressive Processes ◽

Stochastic Linear Programming ◽

Vector Autoregressive ◽

First Order ◽

Convergence Of Moments ◽

Geometric Convergence ◽

Vector Autoregressive Processes

Vector autoregressive processes of the first order are considered which are non-negative and optimize a linear objective function. These processes may be used in stochastic linear programming with a dynamic structure. By using Tweedie's results from the theory of Markov chains, conditions for geometric rates of convergence to stationarity (i.e. so-called geometric ergodicity) and for existence and geometric convergence of moments of these processes are obtained.

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