Modelling Non-Stationary Time Series: A Multivariate - download pdf or read online

By Professor Simon P. Burke;Professor John Hunter

ISBN-10: 0230005780

ISBN-13: 9780230005785

ISBN-10: 1403901724

ISBN-13: 9781403901729

ISBN-10: 1403901732

ISBN-13: 9781403901736

ISBN-10: 1403902038

ISBN-13: 9781403902030

Co-integration, equilibrium and equilibrium correction are key strategies in glossy purposes of econometrics to genuine global difficulties. This publication offers path and advice to the now monstrous literature dealing with scholars and graduate economists. Econometric conception is associated with useful concerns corresponding to tips on how to establish equilibrium relationships, the right way to take care of structural breaks linked to regime adjustments and what to do while variables are of alternative orders of integration.

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Additional resources for Modelling Non-Stationary Time Series: A Multivariate Approach

Sample text

This is relaxed in later chapters. 1 Long-run relationships The idea of equilibrium is fundamental to the interrelationship of economic processes. In the time series econometric context, the idea is encapsulated in many ways. In general, the concept implies an underlying relationship about which the process or processes under examination vary, without deviating too far or for too long away from the values that would have to exist if the relationship held exactly at each period in time. 1 In a static model A static model is one in which all the processes appear with the same time index so that only current values are concerned and there are no intertemporal links between them.

To develop the idea, it is necessary to consider the relationship between series, rather than the properties of individual series alone. This is the subject of the next chapter. 1 Introduction The previous chapter dealt with the properties of univariate time series, and in particular non-stationarity as characterized by the autoregressive unit root. This chapter develops the theme by looking at the way in which this type of non-stationarity can be modelled as a common feature such that the nonstationarity in one series is fully explained by that present in an appropriate combination of other series.

23) covers them all. It is clear that the value of ␣ (1) is very important. It directly determines the speed of adjustment to equilibrium as it is the coefficient of the lagged equilibrium error term. It should also be negative if the equilibrium error interpretation is to make sense. e. equilibrium) solution to exist, it must be non-zero because it appears as a divisor in the equilibrium error term. The condition ␣ (1) ≠ 0 means the lag polynomial ␣ (L) must have no unit roots. Of course, as ␣ (1) → 0, the speed of adjustment gets slower.

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Modelling Non-Stationary Time Series: A Multivariate Approach by Professor Simon P. Burke;Professor John Hunter

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