First of all, sorry if the question is basic.
I'm exploring the issue of modelling non-linear time series. I have read in
several articles that a correct strategy is often to start by fitting a
linear model (for example a simple Autoregressive model) and if it is not
satisfactory, then try to fit a TAR, SETAR or Markov Switching Model,.... I
think this is a classical approach.
In the papers that I have read, in order to detect deviation from linearity
most authors apply several test over the residuals of the simple first
estimated AR model (for example RESET test, BDS test, Mc. Leod test are
common in this context). Then after reject the Null in these test, they
proceed with the non-linear model.
Playing with some series in Gretl, I have seen that after estimating an
AR(1) model or ARIMA model with the options built in Gretl-GUI, the window
"test" post-estimation option only allows to test for Normality or ARCH,
but other options as RESET or Non-linearity are not activated.
Only after estimating a model via OLS menu with the dependent variable and
its lags -that is not exactly the same of estimating the AR(1) model, so
the constant change as usual- these post-estimation options are allowed.
I have checked that in Eviews, for example, the options RESET, etc. are
(like not in Gretl) allowed after estimating both, the AR(1) model and the
equation with the lagged dependent variable.
Y C AR(1) AR residual term model
Y C Y(-1) model with the lagged endogenous variable
So, my question is: Why this difference among Gretl and Eviews, the
disallowed options in Gretl are for some special considerations?
If I want to perform this kind of analysis in Gretl, with an AR model,
which is the correct form to proceed?. Save the residuals of my estimated
AR model and export them to R or other software to perform the BDS or RESET
test as usual in literature?
Thanks in advance.
University of Alicante
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