Autoregressive processes in optimization

1988 ◽  
Vol 25 (02) ◽  
pp. 302-312
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.

Autoregressive processes in optimization

1988 ◽  
Vol 25 (2) ◽  
pp. 302-312 ◽  
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.

COMMENTS ON THE PAPER BY MINXIAN YANG: “SOME PROPERTIES OF VECTOR AUTOREGRESSIVE PROCESSES WITH MARKOV-SWITCHING COEFFICIENTS”

2002 ◽  
Vol 18 (3) ◽  
pp. 815-818 ◽  
Author(s):  
Christian Francq ◽  
Jean-Michel Zakoïan
Keyword(s):  
Markov Switching ◽  
Second Order ◽  
Autoregressive Processes ◽  
Vector Autoregressive ◽  
First Order ◽  
Vector Autoregressive Processes ◽  
Stationarity Conditions

This paper discusses the stationarity conditions proposed by M. Yang (2000, Econometric Theory 16, 23–43), in the framework of Markov-switching first-order autoregressions. A weaker second-order stationarity assumption is proposed.

VECTOR AUTOREGRESSIVE PROCESSES WITH NONLINEAR TIME TRENDS IN COINTEGRATING RELATIONS

2001 ◽  
Vol 5 (4) ◽  
pp. 577-597 ◽  
Author(s):  
Antti Ripatti ◽  
Pentti
Keyword(s):  
Structural Changes ◽  
Asymptotic Theory ◽  
Var Model ◽  
Level Shift ◽  
Autoregressive Processes ◽  
Short Term ◽  
Vector Autoregressive ◽  
Market Rate ◽  
Nonlinear Trends ◽  
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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