Stationarity and Cointegration analysis

Stationarityand Cointegration analysisBy Tinashe Unit root testing Cointegration Vector Auto-regressions Cointegration in Multivariate systems Introduction Stationarityorotherwiseofaseriescanstron glyinfluenceitsbehaviourandproperties. Forinstancea shock diesawaywithstationaritybutispersistenti fnonstationary. Spuriousregressions -ifvariablesaretrendedovertimeitmayprodu cesignificantcoefficientsandhighR2butiti sameaninglessrelationship. Twotypesoftrend StochasticTrend-[randomwalk] DeterministicTrend Whydistinguishbetweenthem? Maylookthesamebuthaveverydifferentproper tiesDeterministic trendsDeterministic trends Taking the first difference of a trend stationary series removes the non-stationaritybut at the cost of introducing an MA(1) process in the invertible MA process cannot be written as an AR processDeterministic trendStochastic trends Consider the followingStochastic trends Take this process forward Speriods in time: As S approaches infinity the values of Y do not become independent of the error terms; and the drift term increases over time This process is know as the stochastic trend because it is dependent on the drift and the stochastic progression of error termsStochastic trendDetecting unit root-dickey fuller tests Dickey and Fuller (Fuller, 1976; Dickey and Fuller, 1979).

•Multiequation time series model •Considers a number of interrelated variables •Imposes zero restrictions on estimation of parameters •Atheoretical i.e. no strict reliance on theory to formulate the model •‘Everything causes everything’ •However, the number of estimated parameters makes the model difficult to interpret

Loading..

Tags:

  Model

Information

Related search queries