[下载]面板数据模型的matlab程序

怎么用呢?楼主？

[tongue]

楼主略搞笑啊。。。 明明是空间计量包中的一小部分程序  还硬说成是面板数据模型  我真是呵呵

xinwq 发表于 2006-3-17 20:31
谢谢，很好

顶啊啊啊啊啊啊

1. clear all;
   
2. 
   
3. A=wk1read('cigarette.wk1',1,0);
   
4. W1=wk1read('Spat-Sym-US.wk1');
   
5. % Dataset downloaded from www.wiley.co.uk/baltagi/
   
6. % Source:  Baltagi and Levin (1992) and Baltagi, Griffin and Xiong (2000).
   
7. % Description:  Panel Data, 46 U.S. States over the period 1963-1992.
   
8. 
   
9. % Spatial weights matrix constructed by Elhorst
   
10. 
    
11. 
    
12. %
    
13. % written by: J.Paul Elhorst 9/2004
    
14. % University of Groningen
    
15. % Department of Economics
    
16. % 9700AV Groningen
    
17. % the Netherlands
    
18. % j.p.elhorst@eco.rug.nl
    
19. %
    
20. % dimensions of the problem
    
21. T=30; % number of time periods
    
22. N=46; % number of regions
    
23. nobs=N*T;
    
24. % row-normalize W
    
25. W=normw(W1); % function of LeSage
    
26. y=A(:,[3]); % column number in the data matrix that corresponds to the dependent variable
    
27. x=A(:,[4,5,6]); % column numbers in the data matrix that correspond to the independent variables
    
28. xconstant=ones(nobs,1);
    
29. % ----------------------------------------------------------------------------------------
    
30. % pooled model corrected for spatial autocorrelation, including intercept
    
31. info.lflag=0; % required for exact results
    
32. info.model=0;
    
33. results=sar_panel(y,[xconstant x],W,T,info); % Elhorst
    
34. vnames=strvcat('logcit','constant','logp','logpn','logy');
    
35. prt_sp(results,vnames,1); % Elhorst
    
36. % ----------------------------------------------------------------------------------------
    
37. % spatial fixed effects + spatial autocorrelation
    
38. info.lflag=0;
    
39. info.model=1;
    
40. results=sar_panel(y,x,W,T,info);
    
41. vnames=strvcat('logcit','logp','logpn','logy');
    
42. prt_sp(results,vnames,1);
    
43. % ----------------------------------------------------------------------------------------
    
44. % time period fixed effects + spatial autocorrelation
    
45. info.lflag=0;
    
46. info.model=2;
    
47. results=sar_panel(y,x,W,T,info);
    
48. vnames=strvcat('logcit','logp','logpn','logy');
    
49. prt_sp(results,vnames,1);
    
50. % ----------------------------------------------------------------------------------------
    
51. % spatial and time period fixed effects + spatial autocorrelation
    
52. info.lflag=0;
    
53. info.model=3;
    
54. results=sar_panel(y,x,W,T,info);
    
55. vnames=strvcat('logcit','logp','logpn','logy');
    
56. prt_sp(results,vnames,1);

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1. clear all;
   
2. 
   
3. A=wk1read('cigarette.wk1',1,0);
   
4. W1=wk1read('Spat-Sym-US.wk1');
   
5. % Dataset downloaded from www.wiley.co.uk/baltagi/
   
6. % Spatial weights matrix constructed by Elhorst
   
7. %
   
8. % written by: J.Paul Elhorst 9/2004
   
9. % University of Groningen
   
10. % Department of Economics
    
11. % 9700AV Groningen
    
12. % the Netherlands
    
13. % j.p.elhorst@eco.rug.nl
    
14. %
    
15. % dimensions of the problem
    
16. T=30; % number of time periods
    
17. N=46; % number of regions
    
18. nobs=N*T;
    
19. % row-normalize W
    
20. W=normw(W1); % function of LeSage
    
21. y=A(:,[3]); % column number in the data matrix that corresponds to the dependent variable
    
22. x=A(:,[4,5,6]); % column numbers in the data matrix that correspond to the independent variables
    
23. xconstant=ones(nobs,1);
    
24. % ----------------------------------------------------------------------------------------
    
25. % pooled model corrected for spatial autocorrelation, including intercept
    
26. info.lflag=0; % required for exact results
    
27. info.model=0;
    
28. results=sem_panel(y,[xconstant x],W,T,info); % Elhorst
    
29. vnames=strvcat('logcit','constant','logp','logpn','logy');
    
30. prt_sp(results,vnames,1); % Elhorst
    
31. % ----------------------------------------------------------------------------------------
    
32. % spatial fixed effects + spatial autocorrelation
    
33. info.lflag=0;
    
34. info.model=1;
    
35. results=sem_panel(y,x,W,T,info);
    
36. vnames=strvcat('logcit','logp','logpn','logy');
    
37. prt_sp(results,vnames,1);
    
38. % ----------------------------------------------------------------------------------------
    
39. % time period fixed effects + spatial autocorrelation
    
40. info.lflag=0;
    
41. info.model=2;
    
42. results=sem_panel(y,x,W,T,info);
    
43. vnames=strvcat('logcit','logp','logpn','logy');
    
44. prt_sp(results,vnames,1);
    
45. % ----------------------------------------------------------------------------------------
    
46. % spatial and time period fixed effects + spatial autocorrelation
    
47. info.lflag=0;
    
48. info.model=3;
    
49. results=sem_panel(y,x,W,T,info);
    
50. vnames=strvcat('logcit','logp','logpn','logy');
    
51. prt_sp(results,vnames,1);

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1. function llike = f_sarpanel(rho,detval,epe0,eped,epe0d,n,T)
   
2. % PURPOSE: evaluates concentrated log-likelihood for the
   
3. %  spatial panel autoregressive model using sparse matrix algorithms
   
4. % ---------------------------------------------------
   
5. %  USAGE:llike = f_sar(rho,detval,epe0,eped,epe0d,n,T)
   
6. %  where: rho  = spatial autoregressive parameter
   
7. %         detval = a matrix with vectorized log-determinant information
   
8. %         epe0   = see below
   
9. %         eped   = see below
   
10. %         eoe0d  = see below
    
11. %         n      = # of obs
    
12. %         T      = number of time points
    
13. %          b0 = AI*xs'*ys;
    
14. %          bd = AI*xs'*Wys;
    
15. %          e0 = ys - xs*b0;
    
16. %          ed = Wys - xs*bd;
    
17. %          epe0 = e0'*e0;
    
18. %          eped = ed'*ed;
    
19. %          epe0d = ed'*e0;
    
20. % ---------------------------------------------------
    
21. %  RETURNS: a  scalar equal to minus the log-likelihood
    
22. %           function value at the parameter rho
    
23. % ---------------------------------------------------
    
24. 
    
25. % written by: James P. LeSage 1/2000
    
26. % University of Toledo
    
27. % Department of Economics
    
28. % Toledo, OH 43606
    
29. % jlesage@spatial-econometrics.com
    
30. 
    
31. % partly rewritten by J.P. Elhorst 9/2004 to account for spatial panels
    
32. % "Specification and Estimation of Spatial Panel Data Models",
    
33. % International Regional Science Review, Vol. 26, pp. 244-268.
    
34. 
    
35. if nargin == 7
    
36. gsize = detval(2,1) - detval(1,1);
    
37. % Note these are actually log detvalues
    
38. i1 = find(detval(:,1) <= rho + gsize);
    
39. i2 = find(detval(:,1) <= rho - gsize);
    
40. i1 = max(i1);
    
41. i2 = max(i2);
    
42. index = round((i1+i2)/2);
    
43. if isempty(index)
    
44. index = 1;
    
45. end;
    
46. 
    
47. detm = detval(index,2);
    
48. 
    
49. z = epe0 - 2*rho*epe0d + rho*rho*eped;
    
50. 
    
51. llike = (n/2)*log(z) - T*detm;
    
52. 
    
53. else
    
54. 
    
55. error('f_sar: Wrong # of input arguments');
    
56. 
    
57. end;

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1. function lik = f_sempanel(rho,eD,W,detval,T)
   
2. % PURPOSE: evaluates concentrated log-likelihood for the
   
3. %  spatial panel error model using sparse matrix algorithms
   
4. % ---------------------------------------------------
   
5. %  USAGE:llike = f_sem(rho,eD,W,detm,T)
   
6. %  where: rho  = spatial error parameter
   
7. %         eD   = begls residuals
   
8. %         W    = spatial weight matrix
   
9. %         detm =  matrix with [rho log determinant] values
   
10. %                computed in sem_panel.m using one of
    
11. %                Kelley Pace's routines
    
12. %         T    = number of time points
    
13. % ---------------------------------------------------
    
14. %  RETURNS: a  scalar equal to minus the log-likelihood
    
15. %           function value at the parameter rho
    
16. % ---------------------------------------------------
    
17. 
    
18. % written by: James P. LeSage 1/2000
    
19. % University of Toledo
    
20. % Department of Economics
    
21. % Toledo, OH 43606
    
22. % jlesage@spatial-econometrics.com
    
23. 
    
24. % partly rewritten by J.P. Elhorst 4/2004 to account for spatial panels
    
25. % "Specification and Estimation of Spatial Panel Data Models",
    
26. % International Regional Science Review, Vol. 26, pp. 244-268.
    
27. 
    
28. n = length(W);
    
29. gsize = detval(2,1) - detval(1,1);
    
30. i1 = find(detval(:,1) <= rho + gsize);
    
31. i2 = find(detval(:,1) <= rho - gsize);
    
32. i1 = max(i1);
    
33. i2 = max(i2);
    
34. index = round((i1+i2)/2);
    
35. if isempty(index)
    
36. index = 1;
    
37. end;
    
38. detm = detval(index,2);
    
39. B = speye(n) - rho*sparse(W);
    
40. Be=zeros(n*T,1);
    
41. for t=1:T
    
42.     t1=1+(t-1)*n;t2=t*n;
    
43.     Be([t1:t2],1)= B*eD([t1:t2],1);
    
44. end
    
45. epe = Be'*Be;
    
46. %lik = (n*T/2)*log(2*pi*epe) - T*detm;
    
47. lik = (n*T/2)*log(epe) - T*detm;

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1. function llike = f2_sarpanel(parm,y,x,W,detval,T)
   
2. % PURPOSE: evaluates log-likelihood -- given ML estimates
   
3. %  spatial panel autoregressive model using sparse matrix algorithms
   
4. % ---------------------------------------------------
   
5. %  USAGE:llike = f2_sar(parm,y,X,W,ldet,T)
   
6. %  where: parm = vector of maximum likelihood parameters
   
7. %                parm(1:k-2,1) = b, parm(k-1,1) = rho, parm(k,1) = sige
   
8. %         y    = dependent variable vector (n x 1)
   
9. %         X    = explanatory variables matrix (n x k)
   
10. %         W    = spatial weight matrix
    
11. %         ldet = matrix with [rho log determinant] values
    
12. %                computed in sar.m using one of Kelley Pace's routines  
    
13. %         T    = number of time points
    
14. % ---------------------------------------------------
    
15. %  RETURNS: a  scalar equal to minus the log-likelihood
    
16. %           function value at the ML parameters
    
17. % ---------------------------------------------------
    
18. 
    
19. % written by: James P. LeSage 1/2000
    
20. % University of Toledo
    
21. % Department of Economics
    
22. % Toledo, OH 43606
    
23. % jlesage@spatial.econometrics.com
    
24. 
    
25. % partly rewritten by J.P. Elhorst 9/2004 to account for spatial panels
    
26. % "Specification and Estimation of Spatial Panel Data Models",
    
27. % International Regional Science Review, Vol. 26, pp. 244-268.
    
28. 
    
29. n = length(y);
    
30. k = length(parm);
    
31. b = parm(1:k-2,1);
    
32. rho = parm(k-1,1);
    
33. sige = parm(k,1);
    
34. n1=n/T;
    
35. 
    
36. gsize = detval(2,1) - detval(1,1);
    
37. i1 = find(detval(:,1) <= rho + gsize);
    
38. i2 = find(detval(:,1) <= rho - gsize);
    
39. i1 = max(i1);
    
40. i2 = max(i2);
    
41. index = round((i1+i2)/2);
    
42. if isempty(index)
    
43. index = 1;
    
44. end;
    
45. detm = detval(index,2);
    
46. 
    
47. e=y-x*b;
    
48. for t=1:T
    
49.     t1=1+(t-1)*n1;t2=t*n1;
    
50.     e([t1:t2],1)= e([t1:t2],1)-rho*sparse(W)*y([t1:t2],1);
    
51. end
    
52. 
    
53. epe = e'*e;
    
54. tmp2 = 1/(2*sige);
    
55. llike = -(n/2)*log(2*pi*sige) + T*detm - tmp2*epe;

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