TY  - JOUR
AB  - 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.
DA  - 2015-06-22
DO  - 10.4119/ijcv-3055
LA  - eng
IS  - 2
M2  - 199
PY  - 2015-06-22
SN  - 1864-1385
SP  - 199-208
T2  - International Journal of Conflict and Violence (IJCV)
TI  - Cointegration and Error Correction Modelling in Time-Series Analysis: A Brief Introduction
UR  - https://doi.org/10.4119/ijcv-3055
Y2  - 2024-11-22T13:15:46
ER  -