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Variance Decomposition Analysis While impulse response functions trace the effects of a shock to one endogenous variable on other variables in the VAR, variance decomposition breaks down the variance of the forecast error into components that can be attributed to each of the endogenous variables.(Perspectives on Economic Development and Policy in India: In Honour of Suresh D. A shock to any variable in the system does not only affect that variable directly but is also transmitted to all of the endogenous variables. Impulse Response Analysis The impulse response function traces the effect of a one standard deviation shock to one of the variables on current and future values of all the endogenous variables.There are some command references given below which can be used to assess various statistic values in the VAR analysis in EViews. Lag selection can be programmed manually in the same way as it is done for ARMA model (see Chapter 3). Thus, either additional exogenous factors should be found to include in the model or another structure of the model should be employed in this case.įigure 6.3: Output for the lag length selection procedure Indeed, we know from the CAPM that market portfolio returns affect the stock returns contemporaneously and are not in lag relationship. This means that the VAR model is inappropriate model to explain IBM and market portfolio returns.
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In the appeared Lag Specification window we choose pmax = 8 (maximal lag order).Īll criteria indicate that the optimal lag order of the model is 0. to determine the optimal model structure. In the workfile menu choose View/Lag Structure/Lag Length Criteria. EViews provides a tool to choose the most suitable lag order. This can happen because we possibly omitted some important exogenous variables or the order of the model is inappropriately selected. This means that the estimated model cannot explain variation in the market portfolio returns. The second equation (for market portfolio) is not significant as suggested by the F-statistics. As expected, there is a unidirectional dynamic relationship from the market portfolio returns to the IBM returns, Thus, the IBM return is affected by the past movements of the market while past movements of IBM stock returns do not affect the market portfolio returns. The only significant coefficient besides the intercept one is at the second lag of the market portfolio returns in the IBM equation. Two columns correspond to two equation in the VAR model. For example,Ĭlick OK and EViews produces an estimation output for the specified VAR model.įigure 6.2: Output for the VAR model estimation If one wishes to include exogenous variables besides the intercept, it can be done by typing a symbol followed by a list of exogenous variables. Then, specifications of the lags pairs and the list of endogenous variables follow. Here ibm2 is a name of the var-object which will be saved in the workfile, Is indicates the estimation method in this case it is OLS estimation method of the unrestricted VAR model. The estimation of the above mentioned example will look like There is a separate object, called var, to declare the VAR model. The Endogenous Variables box will be filled in automatically.įinally, we can estimate VAR model from the command line. We do not specify any exogenous variables apart from the intercept term c.Īnother way of calling the VAR estimation dialog window is to select both endogenous variables in the workfile and in the context menu (right button click) choose Open/as VAR. This means, we include all lags beginning from the first one and ending with the lag of order 2. If we want to build a model with only two lags, we write 1 2. In the Lag Intervals for Endogenous we have to specify the order of the model, that is interval of lags to be included in the model.