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### Re: Question on Seemingly Unrelated Regression Posted: Mon Sep 09, 2019 12:36 pm
Understand that NLSYSTEM and SUR (in general) assume that the sigma matrix is unknown and has to be estimated. You're trying to freely estimate a 7 x 7 covariance matrix with 10 data points (and the residuals aren't even full rank to start).

1. That might not work at all (because of the remark in parentheses)
2. Even if it works, the sigma matrix will be so ill-conditioned that the results will be suspect. (SUR/NLSYSTEM uses the inverse of sigma; if sigma is nearly singular, that will throw the estimates off).

You need to come up with a different method for handling that. CV=%IDENTITY(7) will get you preliminary estimates of the coefficients. You would then need to do some type of shrinkage estimator on the covariance matrix to get something usable.

### Re: Question on Seemingly Unrelated Regression Posted: Mon Sep 09, 2019 1:35 pm
What about using panel data?

I have expenditures of deciles on each commodity. So, I have 100 observations for each each variables.

### Re: Question on Seemingly Unrelated Regression Posted: Mon Sep 09, 2019 3:02 pm
I really have no idea. Deciles of what? Deciles aren't individuals. You've never answered the question about whether you're taking a model which is supposed to predict individual behavior and applying it across time, or taking a model which is supposed to predict aggregate behavior across time and applying it to individuals. Neither adaptation is likely to be simple.

### Re: Question on Seemingly Unrelated Regression Posted: Tue Sep 10, 2019 1:10 pm
I want to estimate demand equations for 8 commodities over a ten year period based on the Stone-Geary utility function.
Maximizing this function subject to the expenditure restriction leads to the linear expenditure system: .
Where, p(i,t) is price of commodity i in year t and m(t) is total expenditures on all commodities in year t.

This model is nonlinear in terms of a(i) and u(i).
Fore solving it, first I estimate a(i)'s and then estimate u(i)'s.

A) Estimating a(i)'s

I have the aggregate value of households' expenditures on each commodities over the period. Therefore I have 8 observations for each commodity.
Now, I order households' expenditures for a given commodity in a given year from lowest to highest. Then, I divide the sorted data into ten equal parts, so that each part represents 1/10: 1st expenditure decile to 1oth expenditure decile. Therefore, I have 10 observations for each commodity in each year and 100 observations over the ten year period.
If I treat each expenditure decile as a "section", I can estimate a panel data model for each commodity separately as: C(i,t)=w+a(i)*M(t). [C(i,t) is the i'st expenditure decile in year t on commodity k; M(t) is the total expenditure of deciles in the year t on commodity k]. This is Engel function.
I can estimate a(i)'s for seven commodities in this way and for the last commodity I calculate a(8) as: a(8)=1-a(1)-a(2)-a(3)-a(4)-a(5)-a(6)-a(7).

B) estimating u(i)'s

At first, I substitute estimated a(i)'s from the former section into the linear expenditure system (for two commodities): and then I estimate u(i)'s using a SUR technique.

My questions:

1) Can can estimate part B in the RATS without getting the error ## REG12. SIGMA Is Singular/Not PSD At Row 7. Too Many Equations for Data Set Size?
2) Can I use a combination of panel data model and SUR for estimating part B?or panel simultaneous equations?

### Re: Question on Seemingly Unrelated Regression Posted: Tue Sep 10, 2019 2:03 pm
Is there a literature on estimating this model? If so, what type of data do they use? Offhand, it looks like it's supposed to be for a large number of individuals (which you don't have).

Your described use of "deciles" is clearly very, very wrong---you are grouping data based upon the values of the dependent variable.

### Re: Question on Seemingly Unrelated Regression Posted: Fri Aug 27, 2021 7:33 am
Dear Tom,
I ,again, want to estimate a Linear Expenditure System based on the Stone-Geary utility function: There are five groups of commodities over 15 years. I wrote a code (based on consumer.rpf) and run it. Here is the code:
`pen data D:\five.xlsxdata(format=xlsx,org=cols) / c1   c2   c3   c4   c5  p1   p2   p3   p4   p5  ynonlin(parmset=base) mu1 mu2 mu3 mu4 mu5 \$   a1 a2 a3 a4 a5 nonlin(parmset=nonnegative) mu1>=0.0 mu2>=0.0 mu3>=0.0 mu4>=0.0 \$    mu5>=0.0nonlin(parmset=summation) a1+a2+a3+a4+a5==1*frml fx1 c1 = (1-a1)*mu1*p1+a1*y-a1*mu2*p2+\$   -a1*mu3*p3-a1*mu4*p4-a1*mu5*p5frml fx2 c2 = (1-a2)*mu2*p2+a2*y-a2*mu1*p1+\$   -a2*mu3*p3-a2*mu4*p4-a2*mu5*p5frml fx3 c3 = (1-a3)*mu3*p3+a3*y-a3*mu1*p1+\$   -a3*mu2*p2-a3*mu4*p4-a3*mu5*p5frml fx4 c4 = (1-a4)*mu4*p4+a4*y-a4*mu1*p1+\$   -a4*mu2*p2-a4*mu3*p3-a4*mu5*p5*compute mu1=mu2=mu3=mu4=mu5=0.0  compute a1=a2=a3=a4=a5=0.0*nlsystem(parmset=base+nonnegative+summation,iters=500) / fx1 fx2 fx3 fx4`

And here is the result:

`Non-Linear System EstimationConvergence in   474 Iterations. Final criterion was  0.0000000 <=  0.0000100Usable Observations                         15Log Likelihood                       -966.6275Dependent Variable C1Mean of Dependent Variable        46494219.467Std Error of Dependent Variable   25002427.493Standard Error of Estimate         7648651.761Sum of Squared Residuals           8.77528e+14Durbin-Watson Statistic                 0.9187Dependent Variable C2Mean of Dependent Variable        8776947.4667Std Error of Dependent Variable   3895075.1077Standard Error of Estimate        1649934.7074Sum of Squared Residuals           4.08343e+13Durbin-Watson Statistic                 2.1847Dependent Variable C3Mean of Dependent Variable        41941281.067Std Error of Dependent Variable   23558927.974Standard Error of Estimate         6686228.170Sum of Squared Residuals           6.70585e+14Durbin-Watson Statistic                 1.6681Dependent Variable C4Mean of Dependent Variable        7399545.8667Std Error of Dependent Variable   3688252.3484Standard Error of Estimate        1078352.0334Sum of Squared Residuals           1.74426e+13Durbin-Watson Statistic                 1.1589    Variable                         Coeff      Std Error      T-Stat      Signif*************************************************************************************1.  MU1                           53169.225864 60007.662267      0.88604  0.375595642.  MU2                              -0.000000     0.000000      0.00000  0.000000003.  MU3                          134204.859039 52688.314835      2.54715  0.010860784.  MU4                               0.000000     0.000000      0.00000  0.000000005.  MU5                          852919.765401 45563.599847     18.71932  0.000000006.  A1                                0.467941     0.016459     28.43130  0.000000007.  A2                                0.092000     0.008779     10.47901  0.000000008.  A3                                0.361248     0.015149     23.84640  0.000000009.  A4                                0.078810     0.007386     10.67004  0.0000000010. A5                                0.000000     0.000000      0.00000  0.00000000`

As you can see MU2 and MU4 are zero. Also A5 is zero. Why did I get such a results? What's wrong with it?
I also run the code for four groups of commodities and again MU2 and A4 are zero.

### Re: Question on Seemingly Unrelated Regression Posted: Sun Aug 29, 2021 9:10 am
Dear Tom,
I really need your help and guide about the above post.

### Re: Question on Seemingly Unrelated Regression Posted: Mon Aug 30, 2021 8:55 am
The MU's are zero because of the non-negativity constraint. The A5 can't be estimated separately because of the adding up constraint. (And if you drop to 4 parameters, it would be A4 that isn't estimated).

### Re: Question on Seemingly Unrelated Regression Posted: Mon Aug 30, 2021 2:48 pm Posted: Mon Aug 30, 2021 6:57 pm