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C:\jim\COURSES\8858\code-bk 2012\M5-3.gms Monday, January 09, 2012 7:08:29 AM Page 1 $TITLE: M5-3.GMS reading from and writing to EXCEL $ontext demonstrate reading and writing from/to excel here we read in from file M5.XLS, data is found in

  1. C:\jim\COURSES\8858\code-bk 2012\M5-3.gms Monday, January 09, 2012 7:08:29 AM Page 1 $TITLE: M5-3.GMS reading from and writing to EXCEL $ontext demonstrate reading and writing from/to excel here we read in from file M5.XLS, data is found in sheet2, range (rng) from cell B3 to cell E9 be sure that this file M5.GMS is in the project directory. "echo" output is written to a file M5.XLS sheet 2 cell B12 Results of regression are written to sheet 3 $offtext SETS I o b s e r v a t i o n s /I1*I6/ J dep and ind var /J1*J3/ K(J) set of independent variables /J2*J3/ L i n t e r c e p t /L1/; PARAMETERS Y0(I) X0(I,K); PARAMETERS BENCH(I,J);

  2. C:\jim\COURSES\8858\code-bk 2012\M5-3.gms Monday, January 09, 2012 7:08:29 AM Page 2 $CALL GDXXRW M5.xls par=BENCH rng=sheet2!B3:E9 $GDXIN M5.gdx $LOAD BENCH $GDXIN DISPLAY BENCH; Execute_Unload 'M5.gdx' BENCH execute 'gdxxrw.exe M5.gdx par=BENCH rng=SHEET2!B12'; Y0(I) = BENCH(I, "J1"); X0(I,K) = BENCH(I, K); DISPLAY Y0, X0; VARIABLES A L P H A intercept BETA(K) slope coefficients (elasticities since estimated in logs) D E V sum of squared deviations YHAT(I) fitted values of the dependent variable; EQUATIONS O B J E C T I V E objective function = sum of squared residuals EYHAT(I) equation for the fitted values of Y (log linear) C R S constraint constant returns: sum of slope coefficients = 1;

  3. C:\jim\COURSES\8858\code-bk 2012\M5-3.gms Monday, January 09, 2012 7:08:29 AM Page 3 OBJECTIVE.. DEV =E= SUM (I, (YHAT(I) - Y0(I))*(YHAT(I) - Y0(I))); EYHAT(I).. LOG(YHAT(I)) =E= ALPHA + SUM (K, BETA(K)*LOG(X0(I,K))); CRS.. SUM (K, BETA(K)) =E= 1; * model OLS: unconstrainted OLS MODEL OLS /OBJECTIVE, EYHAT/; ALPHA.L = 1; BETA.L(K) = 1; YHAT.L(I) = 2; SOLVE OLS USING NLP MINIMIZING DEV; * model OLSC: constrainted least squares, imposes CRS MODEL OLSC /ALL/; SOLVE OLSC USING NLP MINIMIZING DEV; * process output to get observed and fitted values of Y

  4. C:\jim\COURSES\8858\code-bk 2012\M5-3.gms Monday, January 09, 2012 7:08:29 AM Page 4 PARAMETER RESULTSA(L, *) RESULTSS(K, *) RESULTSF(I,*); RESULTSA(L, "INTERCEPT") = ALPHA.L; RESULTSS(K, "SLOPES") = BETA.L(K); RESULTSF(I, "YHAT") = YHAT.L(I); RESULTSF(I, "Y0") = Y0(I); DISPLAY RESULTSA, RESULTSS, RESULTSF; Execute_Unload 'M5.gdx' RESULTSA execute 'gdxxrw.exe M5.gdx par=RESULTSA rng=SHEET3!B3' Execute_Unload 'M5.gdx' RESULTSS execute 'gdxxrw.exe M5.gdx par=RESULTSS rng=SHEET3!B6' Execute_Unload 'M5.gdx' RESULTSF execute 'gdxxrw.exe M5.gdx par=RESULTSF rng=SHEET3!B10'

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