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How to calculate serial correlation at lag 12?
First, you have monthly data, and river flow is a very natural candidate to be seasonal. It evolves in 12-month cycles, thus creating serial correlation at lag 12. What you could do is either seasonally adjust the data before fitting the VAR model or include monthly dummies into the VAR model.
How is a Var time series model modeled?
In the VAR model, each variable is modeled as a linear combination of past values of itself and the past values of other variables in the system. Since you have multiple time series that influence each other, it is modeled as a system of equations with one equation per variable (time series).
Why are there two LAGs in Var ( 2 )?
Since the Y terms in the equations are interrelated, the Y’s are considered as endogenous variables, rather than as exogenous predictors. Likewise, the second order VAR (2) model for two variables would include up to two lags for each variable (Y1 and Y2).
Why is var not a good model for data?
The selection of zero lag suggests that VAR might not be a good model for your data, so you might want to explore alternative models. If zero lag presents mean there is no contribution of past value of the exogenous variable.
How is the length of the lag, p-cross validated?
Then the residuals are used the as the dependent variable and regressed over independent variables and its first p-lags. However a drawback of the BG test is that the value of p, the length of the lag, cannot be specified a priori. My question is; what is the best way to determine p?
Can a Var ( 12 ) change the residual structure?
If I simply use a VAR (12), the residual structure would not change much: Hardly changes the residual structure. It is of course useless to model such a high-order VAR, but just to demonstrate here the “stubbornity” of the residual correlation. First, you have monthly data, and river flow is a very natural candidate to be seasonal.
Can I leave the default lags.pt = 16?
Can I leave the default lags.pt = 16 and lags.bg = 5 ? Given that the selection criteria show: do I have enough justification that the VAR (2) model is the best fit based on the tests results above (besides the normality tests favor VAR (2) as well)? How do I know if to include a const or not (in this case not included type = “none”)
Which is an example of a VAR model?
VAR models (vector autoregressive models) are used for multivariate time series. The structure is that each variable is a linear function of past lags of itself and past lags of the other variables. As an example suppose that we measure three different time series variables, denoted by x t, 1, x t, 2, and x t, 3.
How are predictors added to a Var ( 2 ) model?
Each variable is a linear function of the lag 1 values for all variables in the set. In a VAR (2) model, the lag 2 values for all variables are added to the right sides of the equations, In the case of three x-variables (or time series) there would be six predictors on the right side of each equation, three lag 1 terms and three lag 2 terms.