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Levels, differences and ECMs - principles for improved econometric forecasting

Research output: Working paper

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Levels, differences and ECMs - principles for improved econometric forecasting. / Fildes, R A; Allen, P G.

Lancaster University : The Department of Management Science, 2004. (Management Science Working Paper Series).

Research output: Working paper

Harvard

Fildes, RA & Allen, PG 2004 'Levels, differences and ECMs - principles for improved econometric forecasting' Management Science Working Paper Series, The Department of Management Science, Lancaster University.

APA

Fildes, R. A., & Allen, P. G. (2004). Levels, differences and ECMs - principles for improved econometric forecasting. (Management Science Working Paper Series). The Department of Management Science.

Vancouver

Fildes RA, Allen PG. Levels, differences and ECMs - principles for improved econometric forecasting. Lancaster University: The Department of Management Science. 2004. (Management Science Working Paper Series).

Author

Fildes, R A ; Allen, P G. / Levels, differences and ECMs - principles for improved econometric forecasting. Lancaster University : The Department of Management Science, 2004. (Management Science Working Paper Series).

Bibtex

@techreport{70a0356a845045ff8dc932b2cae057da,
title = "Levels, differences and ECMs - principles for improved econometric forecasting",
abstract = "An avalanche of articles has described the testing of a time series for the presence of unit roots. However, economic model builders have disagreed on the value of testing and how best to operationalise the tests. Sometimes the characterization of the series is an end in itself. More often, unit root testing is a preliminary step, followed by cointegration testing, intended to guide final model specification. A third possibility is to specify a general vector autoregression model, then work to a more specific model by sequential testing and the imposition of parameter restrictions to obtain the simplest data-congruent model 'fit for purpose'. Restrictions could be in the form of cointegrating vectors, though a simple variable deletion strategy could be followed instead. Even where cointegration restrictions are sought, some commentators have questioned the value of unit root and cointegration tests, arguing that restrictions based on theory are at least as effective as those derived from tests with low power. Such a situation is, we argue, unsatisfactory from the point of view of the practitioner. What is needed is a set of principles that limit and define the role of the tacit knowledge of the model builders. In searching for such principles, we enumerate the various possible strategies and argue for the middle ground of using these tests to improve the specification of an initial general vector-autoregression model for the purposes of forecasting. The evidence from published studies supports our argument, though not as strongly as practitioners would wish.",
keywords = "unit root test, cointegration test, vector autoregression, error-correction model econometric methods, model specification, tacit knowledge",
author = "Fildes, {R A} and Allen, {P G}",
year = "2004",
language = "English",
series = "Management Science Working Paper Series",
publisher = "The Department of Management Science",
type = "WorkingPaper",
institution = "The Department of Management Science",

}

RIS

TY - UNPB

T1 - Levels, differences and ECMs - principles for improved econometric forecasting

AU - Fildes, R A

AU - Allen, P G

PY - 2004

Y1 - 2004

N2 - An avalanche of articles has described the testing of a time series for the presence of unit roots. However, economic model builders have disagreed on the value of testing and how best to operationalise the tests. Sometimes the characterization of the series is an end in itself. More often, unit root testing is a preliminary step, followed by cointegration testing, intended to guide final model specification. A third possibility is to specify a general vector autoregression model, then work to a more specific model by sequential testing and the imposition of parameter restrictions to obtain the simplest data-congruent model 'fit for purpose'. Restrictions could be in the form of cointegrating vectors, though a simple variable deletion strategy could be followed instead. Even where cointegration restrictions are sought, some commentators have questioned the value of unit root and cointegration tests, arguing that restrictions based on theory are at least as effective as those derived from tests with low power. Such a situation is, we argue, unsatisfactory from the point of view of the practitioner. What is needed is a set of principles that limit and define the role of the tacit knowledge of the model builders. In searching for such principles, we enumerate the various possible strategies and argue for the middle ground of using these tests to improve the specification of an initial general vector-autoregression model for the purposes of forecasting. The evidence from published studies supports our argument, though not as strongly as practitioners would wish.

AB - An avalanche of articles has described the testing of a time series for the presence of unit roots. However, economic model builders have disagreed on the value of testing and how best to operationalise the tests. Sometimes the characterization of the series is an end in itself. More often, unit root testing is a preliminary step, followed by cointegration testing, intended to guide final model specification. A third possibility is to specify a general vector autoregression model, then work to a more specific model by sequential testing and the imposition of parameter restrictions to obtain the simplest data-congruent model 'fit for purpose'. Restrictions could be in the form of cointegrating vectors, though a simple variable deletion strategy could be followed instead. Even where cointegration restrictions are sought, some commentators have questioned the value of unit root and cointegration tests, arguing that restrictions based on theory are at least as effective as those derived from tests with low power. Such a situation is, we argue, unsatisfactory from the point of view of the practitioner. What is needed is a set of principles that limit and define the role of the tacit knowledge of the model builders. In searching for such principles, we enumerate the various possible strategies and argue for the middle ground of using these tests to improve the specification of an initial general vector-autoregression model for the purposes of forecasting. The evidence from published studies supports our argument, though not as strongly as practitioners would wish.

KW - unit root test

KW - cointegration test

KW - vector autoregression

KW - error-correction model econometric methods

KW - model specification

KW - tacit knowledge

M3 - Working paper

T3 - Management Science Working Paper Series

BT - Levels, differences and ECMs - principles for improved econometric forecasting

PB - The Department of Management Science

CY - Lancaster University

ER -