WHAT DO THESE PLOTS SAY ABOUT THE REGRESSION OUTCOMES?

[d6k5d3]

I have run a regression, and in the output the following charts are generated. I cannot relate these charts with the parameter estimates. Could someone give some idea?

For example, does the chart imply that the predicted value of Abs_Jump is 0 by all the regressors except Abs_Surp_Mex14? How could this be if I have the parameter estimate for Abs_Surp_Mex8 which is 0.469 with p-value 0.01? Please enlighten me.

[PaigeMiller]

Can we see the code you used? Can we see the rest of the output from this regression?

[d6k5d3]

proc qlim data= MXN_Jump_News;
model Abs_Jump= DW_Mon DW_Tue DW_Wed DW_Thu Sin1 Sin2 Sin3 Sin4 Cos1 Cos2 Cos3 Cos4 Abs_Surp_US4
		Abs_Surp_US7 Abs_Surp_US8 Abs_Surp_US9 Abs_Surp_US11 Abs_Surp_US16 Abs_Surp_US20
		Abs_Surp_US22 PS_US30 Abs_Surp_GER12 Abs_Surp_MEX5 Abs_Surp_MEX6 Abs_Surp_MEX8
		Abs_Surp_MEX10 Abs_Surp_MEX14 PS_MEX16 SP4 SP16 SP21 SP23; nloptions maxiter= 500;
endogenous Abs_Jump ~ censored (lb= 0);
run;

DW_Mon to DW_Thu are dummies. SP4 SP16 SP21 SP23 are also dummies. Sin1-Sin4 and Cos1-Cos4 are sine and cosine values. The dependent variable has only positive values. The other independent variables take both positive and negative values.

[ballardw]

When I see code such as (reformatted for legibility on the forum)

proc qlim data= MXN_Jump_News;
model Abs_Jump= DW_Mon DW_Tue DW_Wed DW_Thu 
      Sin1 Sin2 Sin3 Sin4 Cos1 Cos2 Cos3 Cos4 
      Abs_Surp_US4 Abs_Surp_US7 Abs_Surp_US8 Abs_Surp_US9 
      Abs_Surp_US11 Abs_Surp_US16 Abs_Surp_US20 Abs_Surp_US22 PS_US30 
      Abs_Surp_GER12 
      Abs_Surp_MEX5 Abs_Surp_MEX6 Abs_Surp_MEX8 Abs_Surp_MEX10 Abs_Surp_MEX14 PS_MEX16 
      SP4 SP16 SP21 SP23; nloptions maxiter= 500;
endogenous Abs_Jump ~ censored (lb= 0);
run;

I wonder if when the values for the variables with US in the name are populated whether the ones with GER or MEX have values other than 0 and vice versa. Maybe just too many variables with 0 for values.

It may not hurt to describe, if not list some data, why the Abs_Surp variables have such different suffixes.

[Reeza]

Why is your title in upper case?
Random guess based on not a whole lot, you have a rare event or data that's very weirdly distributed in some manner. What does the distribution of your response variable look like?

[d6k5d3]

Sorry for the upper case.

[d6k5d3]

Would you please look at the code and the full result above? Much thanks.

[PaigeMiller]

As a general rule, when you get such un-intuitive results from a model fit, the causes are most likely one of the following

1. Extreme outlier(s)
2. Overfitting
3. Extreme multi-collinearity between the X-variables
4. Humongous amounts of noise in the y-variables — possibly because of an extreme outlier(s)

Now, I have never used PROC QLIM and so I can't really say any more about your results, and there may be other reasons why this is happening specifically to PROC QLIM. Nevertheless, the above 4 possibilities are things you need to investigate yourself, since you have the data.

[Reeza]

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@d6k5d3 wrote:
Would you please look at the code and the full result above? Much thanks.

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My previous question is still unanswered, which will likely help answer the other questions. What does the distribution of your response variable look like before modelling? If you take a close look at your output it seems like there isn't even an upper bound which seems a bit weird to me, which may mean your data is massively skewed as I suspected.