#Eviews 9 student version saving issue series#

It involves just a single-equation set-up, making it simple to implement and interpret.It can be used with a mixture of I(0) and I(1) data.(2001) has a number of features that many researchers feel give it some advantages over conventional cointegration testing. The ARDL / Bounds Testing methodology of Pesaran and Shin (1999) and Pesaran et al. What do we do in such cases if we want to model the data appropriately and extract both long-run and short-run relationships? This is where the ARDL model enters the picture. In other words, things just aren't as 'clear cut' as in the three situations noted above. Some of the variables in question may be stationary, some may be I(1) or even fractionally integrated, and there is also the possibility of cointegration among some of the I(1) variables. Now, let's return to the more complicated situation mentioned above.This model will represent the short-run dynamics of the relationship between the variables. (ii) An error-correction model (ECM), estimated by OLS. This will provide the long-run equilibrating relationship between the variables. In this case, we can estimate two types of models: (i) An OLS regression model using the levelsВ of the data.

#Eviews 9 student version saving issue series#

We know that all of the series are integrated of the same order, and they are cointegrated.In this case, we can just (appropriately) difference each series, and estimate a standard regression model using OLS. We know that all of the series are integrated of the same order ( e.g., I(1)), but they are notВ cointegrated.In this case, we can simply model the data in their levels, using OLS estimation, for example. We know that all of the series are I(0), and hence stationary.First, note that there are three straightforward situations that we're going to put to one side, because they can be dealt with in standard ways: Let's suppose that we have a set of time-series variables, and we want to model the relationship between them, taking into account any unit roots and/or cointegration associated with the data. The reasons for this will become apparent as we go along. Specifically, we'll work with a mixture of differences and levels of the data. We're going to modify this model somewhat for our purposes here.