Hi there, I'm in desperate need to figure out how to solve this issue. I need to estimate a recursive model for a time series data of asset returns. The dependent variable is the asset return and then I have a set of k variables, a lagged value of the dependent variable (plus an intercept) as regressors. My sample period (monthly observations) starts on Jan 1972. What I need to do is the following:
1)use a moving window regression (window of 60 observations, i.e. 5 years) 2)estimate all the possible model (Jan 1972 Dec 1977) using a subset of the k variables (intercept and lagged values always present) and choose the best model according to thee AIC criterion 3)once the best model is chosen, make one-step ahead prediction with that model 4)go back to step 2 shifting the sample period one month ahead (i.e. Feb 1972, Jan 1978) and then repeat step 2 and 3 5)keep going until the end of the sample (May 2009) Hope it helps -- View this message in context: http://www.nabble.com/Recursive-regression-tp25682804p25682804.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
