#THRESHOLD REGRESSION EVIEWS SERIES#

The correlations among pairs of predictor variables are large.The t-tests for each of the individual slopes are non-significant ( P > 0.05), but the overall F-test for testing all of the slopes are simultaneously 0 is significant ( P The analysis exhibits the signs of multicollinearity - such as, estimates of the coefficients vary from model to model.Some of the common methods used for detecting multicollinearity include: This can be expected given that the solving process is iterative.Okay, now that we know the effects that multicollinearity can have on our regression analyses and subsequent conclusions, how do we tell when it exists? That is, how can we tell if multicollinearity is present in our data? There were small differences between the two. When I did run in R an M-estimation of the bisquare type I almost got the exact same results as EViews. After closer inspection, I noticed that the EViews M-estimation I ran was of the bisquare type (vs. This leads me to conclude that R is somewhat more flexible than EViews on this count. I don't know why I could not run it at first with all the dummy variables in at once (maybe I did an error in coding). And, now I eventually could run my MM-estimation with all the dummy variables. I did that to observe when the MM-estimation model would break down. Next, I added just one single dummy variable at a time and each time I reran my MM-estimation. So, I reran my MM-estimation by first eliminating all my dummy variables. Just as in EViews I got an error message stating the regression/simulation did not reach convergence after 20 iterations. Similarly, I firt ran into trouble when attempting an MM-estimation.

Just as in EViews I had no problem running an M-estimation. And, ran Robust Regression using R with MASS package using rlm() function.

And, see if it is feasible.ДЇollowing up on the above, I did just that. I may attempt to rerun MM-estimation in R. However, I am unclear if this is a limitation of EViews or if this is truly an algorithm limitation.

#THRESHOLD REGRESSION EVIEWS SERIES#

And, some of them do not have that many observations (8 consecutive ones out of a time series data with 217 observations). The forum moderator indicated that if a model has the presence of dummy variables without that many observations, such estimations may not reach convergence and generate the error as mentioned above. Invariably, I run into the same error.ДЄt an EViews forum, another fellow ran into the exact same problem for both MM-estimation and S-estimation. But, every time I run a Robust Regression MM-estimation I run into the same error: 'Maximum number of singular subsamples reached.' I have played around with the MM-estimation specifications by increasing/decreasing number of iterations, convergence level, etc. I can easily run a Robust Regression M-estimation. I am now attempting to run Robust Regressions (EViews calls them Robust Least Square). I have developed a fairly simple multivariate regression econometrics model.

Threshold regression eviews