This generalizes all earlier results of this type. In Python, you can find the MacKinnon test in the statsmodels library. Pure Point spectrum for measure dynamical systems on locally compact Abelian groups Daniel Lenz, Nicolae Strungaru We show equivalence of pure point diffraction and pure point dynamical spectrum for measurable dynamical systems build from locally finite measures on locally compact Abelian groups. Other popular cointegration tests have been developed by Engle and Granger and Søren Johansen. Luckily, the work of James MacKinnon provides extensive insights into tests for cointegration. If you are interested in learning about the generating process itself, this approach is likely mo r e expedient. Obviously, cointegration is nothing new to econometricians and statisticians. Statistical tests - the classical statistics way. On the other hand, the above result also suggests that adding the original time-series as a feature might be a good idea in general. The primary implication from cointegration is then to apply differencing with some care. As long as the resulting model is performant and reliable, nearly anything goes.Īs usually, the ‘best’ model can be selected based on cross-validation and out-of-sample performance tests. If our goal is primarily to build the most accurate forecast, we don’t necessarily need to detect cointegration at all. Therefore, two different approaches come to mind:Ĭross-validation and backtesting - the pragmatic, ‘data sciency’ approach. Typically, time-series analysis is concerned either with forecasting or inference. The above result begs the question of what we should do to handle cointegration.
0 kommentar(er)
