Eviews is currently used in many universities and research centres across the world, as well as in many private consultancy and financial and economic analysis firms. This program, which currently is under version 10.0, has more than 20 years of experience, and their developers have widened the range of available techniques as econometric theory developed new approaches to economic problems.
PANEL REGRESSION EVIEWS SOFTWARE
This course aims at providing a brief introduction to panel data econometrics, using one of the most widespread software packages, Eviews. Departing from the basic models developed many years ago, the literature has pushed the boundaries of this tool of economic analysis, offering to the end-user a wide array of techniques that help in modelling many different economic problems, both micro and macro. The development of panel data econometrics has been the most vibrant and intense branch of applied economic during the last 15 years. I cant think of any reason why that could not be applied here.Īlso here I would recommend for searching for panels with duplicates, I know this is not a genuine duplicate, but mostly when this issue is being discussed many people call these observations duplicates so it will probably give you better results than searching just for unbalanced panel data which is usually applied to panel data where you just have different $T$ for each panel ID.AN INTRODUCTION TO PANEL DATA ECONOMETRICS IN EVIEWS For example, instead of within estimator you could use pooled OLS where you treat each observation as individual event with firm fixed effects. You could also aggregate values of X and Y across events on a single day and just include extra dummy for firms with multiple events in a given year.Ĭonsider different estimator. Multiple events on same day occur only once these would drop, and also my intuition tells me this would led to some methodological issues if you would try to lets say cluster errors on firm level as one firm would be treated differently based on no of events. However, downside with within estimator would be that if these So you could create new firm ID like this: So now your panel identifiers would be: Firm ID Event instead of Firm ID Year.Ĭreate new firm ID for each event. Run the model on dates instead of years if the events do not occur at exactly the same time. Here the problem is not the 'unbalanceness' but the fact that you will have 'duplicate' observations if you will run any panel model with Firm ID and Year as panel identifiers because you cant have one panel identifier assigned to two different different observations as that means your observations are not unique. This is more serious problem than just unbalanced panel. However, if I understand you correctly the problem you have is not just about panel being unbalanced but because there are duplicate observations like this: I think authors there refer to adjustments that have to be made when programming the function to account for this (as this creates some problems with matrices - at least that is my understanding from the document you linked).
PANEL REGRESSION EVIEWS CODE
So you can use unbalanced data without any additional adjustment to the plm code itself. Here no adjustments are necessary, you can easily try it yourself: install.packages("plm")
PANEL REGRESSION EVIEWS SERIES
Note the problem is not just that the series is unbalanced, for an ordinary unbalanced panel data-set where firms have different number of $T$ observation the command would still work. Here the solution would depend on what you want to accomplish. How do I run this kind of model in RStudio with unbalanced data? Let me know if this doesn't make sense and I can edit the question. This is because multiple "deals" were done in the year associated with the company ticker in the year, or sometimes no deals were run in the ticker/year combination. This is obvious - I am having issues because my panel data is unbalanced, where the ticker/year combination can be associated with multiple lines of data. table(index(your_pdataframe), useNA = "ifany") I run into the following error: duplicate couples (id-time) in resulting ame NUM_OF_EMPLOYEES, data = pdata, model = "within", effect = "time") SquareDebttoAssetRatio + FSCashShortTermInvestments + Percent + LogLiabBefore + logSalesBefore + EmplBefore + DebttoAssetRatio + Try1 <- plm(formula = logDipLoanTotal ~ PrimeFiling +
My code is as follows: pdata <- plm.data(b2, index = c("ticker", "year")) I am attempting to perform an unbalanced panel data regression in R.