On the Mining of Cointegrated Econometric Models

Author(s):  
J.L. van Velsen ◽  
R. Choenni

The authors describe a process of extracting a cointegrated model from a database. An important part of the process is a model generator that automatically searches for cointegrated models and orders them according to an information criterion. They build and test a non-heuristic model generator that mines for common factor models, a special kind of cointegrated models. An outlook on potential future developments is given.

1990 ◽  
Vol 20 (5) ◽  
pp. 569-608 ◽  
Author(s):  
J. J. McArdle ◽  
H. H. Goldsmith

2002 ◽  
Vol 5 (3) ◽  
pp. 309-321 ◽  
Author(s):  
Richard T. Baillie ◽  
G. Geoffrey Booth ◽  
Yiuman Tse ◽  
Tatyana Zabotina

Psychometrika ◽  
1970 ◽  
Vol 35 (1) ◽  
pp. 111-128 ◽  
Author(s):  
Roderick P. McDonald

2018 ◽  
Author(s):  
Mijke Rhemtulla ◽  
Riet van Bork ◽  
Denny Borsboom

Previous research and methodological advice has focused on the importance of accounting for measurement error in psychological data. That perspective assumes that psychological variables conform to a common factor model, such that they consist of construct variance plus error. In this paper, we explore what happens when a set of items that are not generated from a common factor construct model are nonetheless modeled as reflecting a common factor. Through a series of hypothetical examples and an empirical re-analysis, we show that (1) common factor models tend to produce extremely biased and highly variable structural parameter estimates when the population model is not a common factor model; (2) model fit is a poor indicator of the degree of bias; and (3) composite models are sometimes more reliable than common factor models under alternative measurement structures, though they also lead to unacceptably bad solutions in some cases.


Sign in / Sign up

Export Citation Format

Share Document