Cointegration and Error Correction Modelling in Time-Series Analysis: A Brief Introduction
Criminological research is often based on time-series data showing some type of trend movement. Trending time-series may correlate strongly even in cases where no causal relationship exists (spurious causality). To avoid this problem researchers often apply some technique of detrending their data, s...
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doaj-0cf71fb4b09d4819884bf0a4760b9fff2020-11-25T01:58:15ZengUniversity of BielefeldInternational Journal of Conflict and Violence1864-13852015-07-0182199208Cointegration and Error Correction Modelling in Time-Series Analysis: A Brief IntroductionHelmut Thome0University of Halle-Wittenberg, GermanyCriminological research is often based on time-series data showing some type of trend movement. Trending time-series may correlate strongly even in cases where no causal relationship exists (spurious causality). To avoid this problem researchers often apply some technique of detrending their data, such as by differencing the series. This approach, however, may bring up another problem: that of spurious non-causality. Both problems can, in principle, be avoided if the series under investigation are “difference-stationary” (if the trend movements are stochastic) and “cointegrated” (if the stochastically changing trendmovements in different variables correspond to each other). The article gives a brief introduction to key instruments and interpretative tools applied in cointegration modelling.http://ijcv.org/index.php/ijcv/article/view/475/pdf_115Cointegrationdeterministic trendsdifference- and trend-stationary processeserror-correction modellingstochastic trendstime-series analysis |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Helmut Thome |
spellingShingle |
Helmut Thome Cointegration and Error Correction Modelling in Time-Series Analysis: A Brief Introduction International Journal of Conflict and Violence Cointegration deterministic trends difference- and trend-stationary processes error-correction modelling stochastic trends time-series analysis |
author_facet |
Helmut Thome |
author_sort |
Helmut Thome |
title |
Cointegration and Error Correction Modelling in Time-Series Analysis: A Brief Introduction |
title_short |
Cointegration and Error Correction Modelling in Time-Series Analysis: A Brief Introduction |
title_full |
Cointegration and Error Correction Modelling in Time-Series Analysis: A Brief Introduction |
title_fullStr |
Cointegration and Error Correction Modelling in Time-Series Analysis: A Brief Introduction |
title_full_unstemmed |
Cointegration and Error Correction Modelling in Time-Series Analysis: A Brief Introduction |
title_sort |
cointegration and error correction modelling in time-series analysis: a brief introduction |
publisher |
University of Bielefeld |
series |
International Journal of Conflict and Violence |
issn |
1864-1385 |
publishDate |
2015-07-01 |
description |
Criminological research is often based on time-series data showing some type of trend movement. Trending time-series may correlate strongly even in cases where no causal relationship exists (spurious causality). To avoid this problem researchers often apply some technique of detrending their data, such as by differencing the series. This approach, however, may bring up another problem: that of spurious non-causality. Both problems can, in principle, be avoided if the series under investigation are “difference-stationary” (if the trend movements are stochastic) and “cointegrated” (if the stochastically changing trendmovements in different variables correspond to each other). The article gives a brief introduction to key instruments and interpretative tools applied in cointegration modelling. |
topic |
Cointegration deterministic trends difference- and trend-stationary processes error-correction modelling stochastic trends time-series analysis |
url |
http://ijcv.org/index.php/ijcv/article/view/475/pdf_115 |
work_keys_str_mv |
AT helmutthome cointegrationanderrorcorrectionmodellingintimeseriesanalysisabriefintroduction |
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