Search

Begin New Search
Proceed to Checkout

Search Results for All:
(Showing results 1 to 2 of 2)



Explaining Cointegration Analysis: Part 1

David F. Hendry and Katarina Juselius

Year: 2000
Volume: Volume21
Number: Number 1
DOI: 10.5547/ISSN0195-6574-EJ-Vol21-No1-1
View Abstract

Abstract:
'Classical' econometric theory assumes that observed data come from a stationary process, where means and variances are constant over time. Graphs of economic time series, and the historical record of economic forecasting, reveal the invalidity of such an assumption. Consequently, we discuss the importance of stationarity for empirical modeling and inference; describe the effects of incorrectly assuming stationarity; explain the basic concepts of non-stationarity; note some sources of non-stationarity; formulate a class of non-stationary processes (autoregressions with unit roots) that seem empirically relevant for analyzing economic time series; and show when an analysis can be transformed by means of differencing and cointegrating combinations so stationarity becomes a reasonable assumption. We then describe how to test for unit roots and cointegration. Monte Carlo simulations and empirical examples illustrate the analysis.



Explaining Cointegration Analysis: Part II

David F. Hendry and Katarina Juselius

Year: 2001
Volume: Volume22
Number: Number 1
DOI: 10.5547/ISSN0195-6574-EJ-Vol22-No1-4
View Abstract

Abstract:
We describe the concept of cointegration, its implications in modelling and forecasting, and discuss inference procedures appropriate in integrated-cointegrated vector autoregressive processes (VARs). Particular attention is paid to the properties of VARs, to the modelling of deterministic terms, and to the determination of the number of cointegration vectors. The analysis is illustrated by empirical examples.





Begin New Search
Proceed to Checkout

 

© 2020 International Association for Energy Economics | Privacy Policy | Return Policy