Fanelli, Luca and Paruolo, Paolo (2007): Speed of Adjustment in Cointegrated Systems.

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Abstract
This paper considers the speed of adjustment to longrun equilibria, in the context of cointegrated Vector Autoregressive Processes (VAR). We discuss the definition of multivariate plives for any indicator of predictive ability, concentrating on cumulated interim multipliers which converge to impact factor for increasing forecasting horizon. Interim multipliers are related to autoregressive Grangercausality coefficients, structural or generalized cumulative impulse responses. We discuss the relation of the present definition of multivariate plives with existing definitions for univariate time series and for nonlinear multivariate stationary processes. For multivariate (possibly cointegrated) VAR systems, plives are functions of the dynamics of the system only,and do not depend on the history path on which the forecast is based. Hence one can discuss inference on plives as (discrete) functions of parameters in the VAR model. We discuss a likelihoodbased approach, both for point estimation and for confidence regions. An illustrative application to adjustment to purchasingpower parity (PPP) is presented.
Item Type:  MPRA Paper 

Original Title:  Speed of Adjustment in Cointegrated Systems 
Language:  English 
Keywords:  plife, speed of adjustment, impact factors, vector equilibrium correction, shock absorption 
Subjects:  C  Mathematical and Quantitative Methods > C3  Multiple or Simultaneous Equation Models ; Multiple Variables > C32  TimeSeries Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C52  Model Evaluation, Validation, and Selection F  International Economics > F3  International Finance > F31  Foreign Exchange 
Item ID:  9174 
Depositing User:  Luca Fanelli 
Date Deposited:  17. Jun 2008 14:00 
Last Modified:  11. Mar 2015 07:12 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/9174 