求教 空间面板matlab 程序中后缀为wk1的文件怎么打开？

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空间面板matlab程序中，后缀为wk1的文件， 我打不开 ， 网上有人介绍了 一些能打开它的软件，  我下了，  但还是打不开 。我想打开看看 ，才能知道空间面板数据输入的方式。有人打开过吗?能不能帮我下。

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关键词：MATLAB atlab matla 空间面板 Mat MATLAB 程序 面板 空间 后缀

不用在下载什么其他软件的，MATLAB 有一个读文件的命令可以打开这个格式的文件。
.wk1 格式的文件是 lotus 软件的。在spatial econometrics 里，一些数据是以这个格式存储。

1. A=wk1read('cigarette.wk1',1,0); % data set with T=30
   
2. W1=wk1read('Spat-Sym-US.wk1');
   
3. % Dataset downloaded from www.wiley.co.uk/baltagi/
   
4. % Spatial weights matrix constructed by Elhorst
   
5. %
   
6. % written by: J.Paul Elhorst summer 2010
   
7. % University of Groningen
   
8. % Department of Economics
   
9. % 9700AV Groningen
   
10. % the Netherlands
    
11. % j.p.elhorst@rug.nl
    
12. %
    
13. % REFERENCES:
    
14. % Elhorst JP (2010) Matlab Software for Spatial Panels. Under review.
    
15. %
    
16. % Elhorst JP (2010) Spatial Panel Data Models. In Fischer MM, Getis A (Eds.)
    
17. % Handbook of Applied Spatial Analysis, Ch. C.2. Springer: Berlin Heidelberg New York.
    
18. %
    
19. % dimensions of the problem
    
20. T=30; % number of time periods
    
21. N=46; % number of regions
    
22. % row-normalize W
    
23. W=normw(W1); % function of LeSage
    
24. y=A(:,3); % column number in the data matrix that corresponds to the dependent variable
    
25. x=A(:,[4,6]); % column numbers in the data matrix that correspond to the independent variables
    
26. xconstant=ones(N*T,1);
    
27. [nobs K]=size(x);
    
28. %%
    
29. % ols estimation
    
30. results=ols(y,[xconstant x]);
    
31. vnames=char('logcit','intercept','logp','logy');
    
32. prt_reg(results,vnames,1);
    
33. sige=results.sige*((nobs-K)/nobs);
    
34. loglikols=-nobs/2*log(2*pi*sige)-1/(2*sige)*results.resid'*results.resid;
    
35. 
    
36. % The (robust)LM tests developed by Elhorst
    
37. 
    
38. LMsarsem_panel(results,W,y,[xconstant x]); % (Robust) LM tests
    
39. 
    
40. % The lm tests developed by Donald Lacombe
    
41. % see http://www.rri.wvu.edu/lacombe/~lacombe.htm
    
42. 
    
43. lm1=lmlag_panel(y,[xconstant x],W);
    
44. prt_tests(lm1);
    
45. 
    
46. lm2=lmerror_panel(y,[xconstant x],W);
    
47. prt_tests(lm2);
    
48. 
    
49. lm3=lmlag_robust_panel(y,[xconstant x],W);
    
50. prt_tests(lm3);
    
51. 
    
52. lm4=lmerror_robust_panel(y,[xconstant x],W);
    
53. prt_tests(lm4);
    
54. 
    
55. %%
    
56. % spatial fixed effects + (robust) LM tests for spatial lag and spatial error model
    
57. % fixed effects, within estimator
    
58. % demeaning of the y and x variables
    
59. model=1;
    
60. [ywith,xwith,meanny,meannx,~,meantx]=demean(y,x,N,T,model);
    
61. results=ols(ywith,xwith);
    
62. vnames=char('logcit','logp','logy'); % should be changed if x is changed
    
63. prt_reg(results,vnames);
    
64. sfe=meanny-meannx*results.beta; % including the constant term
    
65. yme = y - mean(y);
    
66. et=ones(T,1);
    
67. error=y-kron(et,sfe)-x*results.beta;
    
68. rsqr1 = error'*error;
    
69. rsqr2 = yme'*yme;
    
70. FE_rsqr2 = 1.0 - rsqr1/rsqr2; % r-squared including fixed effects
    
71. sige=results.sige*((nobs-K)/nobs);
    
72. logliksfe=-nobs/2*log(2*pi*sige)-1/(2*sige)*results.resid'*results.resid;
    
73. LMsarsem_panel(results,W,ywith,xwith); % (Robust) LM tests
    
74. 
    
75. lm1=lmlag_panel(ywith,xwith,W);
    
76. prt_tests(lm1);
    
77. 
    
78. lm2=lmerror_panel(ywith,xwith,W);
    
79. prt_tests(lm2);
    
80. 
    
81. lm3=lmlag_robust_panel(ywith,xwith,W);
    
82. prt_tests(lm3);
    
83. 
    
84. lm4=lmerror_robust_panel(ywith,xwith,W);
    
85. prt_tests(lm4);
    
86. %%
    
87. % time-period fixed effects + (robust) LM tests for spatial lag and spatial error model
    
88. % fixed effects, within estimator
    
89. % demeaning of the y and x variables
    
90. model=2;
    
91. [ywith,xwith,~,~,meanty,meantx]=demean(y,x,N,T,model);
    
92. results=ols(ywith,xwith);
    
93. vnames=char('logcit','logp','logy'); % should be changed if x is changed
    
94. prt_reg(results,vnames);
    
95. tfe=meanty-meantx*results.beta; % including the constant term
    
96. yme = y - mean(y);
    
97. en=ones(N,1);
    
98. error=y-kron(tfe,en)-x*results.beta;
    
99. rsqr1 = error'*error;
    
100. rsqr2 = yme'*yme;
     
101. FE_rsqr2 = 1.0 - rsqr1/rsqr2; % r-squared including fixed effects
     
102. sige=results.sige*((nobs-K)/nobs);
     
103. logliktfe=-nobs/2*log(2*pi*sige)-1/(2*sige)*results.resid'*results.resid;
     
104. LMsarsem_panel(results,W,ywith,xwith); % (Robust) LM tests
     
105. 
     
106. lm1=lmlag_panel(ywith,xwith,W);
     
107. prt_tests(lm1);
     
108. 
     
109. lm2=lmerror_panel(ywith,xwith,W);
     
110. prt_tests(lm2);
     
111. 
     
112. lm3=lmlag_robust_panel(ywith,xwith,W);
     
113. prt_tests(lm3);
     
114. 
     
115. lm4=lmerror_robust_panel(ywith,xwith,W);
     
116. prt_tests(lm4);
     
117. %%
     
118. % spatial and time period fixed effects + (robust) LM tests for spatial lag and spatial error model
     
119. % fixed effects, within estimator
     
120. % demeaning of the y and x variables
     
121. model=3;
     
122. [ywith,xwith,meanny,meannx,meanty,meantx]=demean(y,x,N,T,model);
     
123. results=ols(ywith,xwith);
     
124. vnames=char('logcit','logp','logy'); % should be changed if x is changed
     
125. prt_reg(results,vnames);
     
126. intercept=mean(y)-mean(x)*results.beta;
     
127. sfe=meanny-meannx*results.beta-kron(en,intercept);
     
128. tfe=meanty-meantx*results.beta-kron(et,intercept);
     
129. yme = y - mean(y);
     
130. ent=ones(N*T,1);
     
131. error=y-kron(tfe,en)-kron(et,sfe)-x*results.beta-kron(ent,intercept);
     
132. rsqr1 = error'*error;
     
133. rsqr2 = yme'*yme;
     
134. FE_rsqr2 = 1.0 - rsqr1/rsqr2; % r-squared including fixed effects
     
135. sige=results.sige*((nobs-K)/nobs);
     
136. loglikstfe=-nobs/2*log(2*pi*sige)-1/(2*sige)*results.resid'*results.resid;
     
137. 
     
138. LMsarsem_panel(results,W,ywith,xwith); % (Robust) LM tests
     
139. 
     
140. lm1=lmlag_panel(ywith,xwith,W);
     
141. prt_tests(lm1);
     
142. 
     
143. lm2=lmerror_panel(ywith,xwith,W);
     
144. prt_tests(lm2);
     
145. 
     
146. lm3=lmlag_robust_panel(ywith,xwith,W);
     
147. prt_tests(lm3);
     
148. 
     
149. lm4=lmerror_robust_panel(ywith,xwith,W);
     
150. prt_tests(lm4);
     
151. %%
     
152. % Tests for the joint significance of spatial and/or time-period fixed effects
     
153. LR=-2*(logliktfe-loglikstfe);
     
154. dof=N;
     
155. probability=1-chis_prb(LR,dof);
     
156. % Note: probability > 0.05 implies rejection of spatial fixed effects
     
157. fprintf(1,'LR-test joint significance spatial fixed effects, degrees of freedom and probability = %9.4f,%6d,%9.4f \n',LR,dof,probability);
     
158. LR=-2*(logliksfe-loglikstfe);
     
159. dof=T;
     
160. probability=1-chis_prb(LR,dof);
     
161. % Note: probability > 0.05 implies rejection of spatial fixed effects
     
162. fprintf(1,'LR-test joint significance time-periode fixed effects, degrees of freedom and probability = %9.4f,%6d,%9.4f \n',LR,dof,probability);

复制代码

这个是Elhorst 的空间面板程序。第一行，第二行。都是读入数据。用的命令是A=wk1read('cigarette.wk1',1,0);，有这个专用的函数。
不过，MATLAB升级后提示，以后的更高版本将不支持这个格式的文件，不提供读这个格式文件的命令。

懂了  谢谢tulipsliu

2# tulipsliu      运行程序后  结果中没有显示46各地区个体固定效应的值  我想在程序结果中显示各地区个体固定效应的值  在程序中哪些地方需要改一下？

Pooled model with spatially lagged dependent variable, spatial and time period fixed effects
Dependent Variable =           logcit
R-squared          =    0.9017   
corr-squared       =    0.4000   
sigma^2            =    0.0052
Nobs,Nvar,#FE      =   1380,     5,    79  
log-likelihood     =        1691.3765
# of iterations    =      1   
min and max rho    =   -1.0000,   1.0000
total time in secs =    0.2600
time for optimiz   =    0.0420
time for lndet     =    0.0820
time for t-stats   =    0.0180
No lndet approximation used
***************************************************************
Variable        Coefficient  Asymptot t-stat    z-probability
logp              -1.000929       -24.357098         0.000000
logy               0.602499        10.275488         0.000000
W*logp             0.098477         1.199756         0.230234
W*logy            -0.315374        -3.946235         0.000079
W*dep.var.         0.268601         8.405988         0.000000

    direct    t-stat   indirect    t-stat   total    t-stat

ans =

   -1.0123  -24.4405   -0.2158   -2.3248   -1.2282  -11.6947
    0.5905   10.1725   -0.1974   -2.1148    0.3931    4.4148

Direct        Coefficient           t-stat           t-prob         lower 05         upper 95
logp            -1.011059       -24.649478         0.000000        -1.092786        -0.932449
logy             0.592300        10.276515         0.000000         0.475786         0.704131

Indirect      Coefficient           t-stat           t-prob         lower 05         upper 95
logp            -0.219392        -2.230008         0.030668        -0.410712        -0.044342
logy            -0.195287        -2.090119         0.042163        -0.377556        -0.009990

Total         Coefficient           t-stat           t-prob         lower 05         upper 95
logp            -1.230451       -11.447830         0.000000        -1.447621        -1.032379
logy             0.397013         4.578789         0.000036         0.226768         0.569779


Wald_spatial_lag =

   18.3474


prob_spatial_lag =

  1.0373e-004


LR_spatial_lag =

   15.7775


prob_spatial_lag =

  3.7494e-004


Wald_spatial_error =

    8.0949


prob_spatial_error =

    0.0175


LR_spatial_error =

    8.2759


prob_spatial_error =

    0.0160

4# celon081211
我很少做空间计量模型，一直都是按照金融学的要求，做金融建模的，也不知道这个是你要的程序不。
我贴上程序给你吧。上面的结果就是用这个程序做的。

1. % Demonstration file for Elhorst Panel Data code
   
2. %
   
3. % Dataset downloaded from www.wiley.co.uk/baltagi/
   
4. % Spatial weights matrix constructed by Elhorst
   
5. %
   
6. % written by: J.Paul Elhorst summer 2010
   
7. % University of Groningen
   
8. % Department of Economics
   
9. % 9700AV Groningen
   
10. % the Netherlands
    
11. % j.p.elhorst@rug.nl
    
12. %
    
13. % REFERENCE:
    
14. % Elhorst JP (2010) Matlab Software for Spatial Panels. Under review.
    
15. %
    
16. % Elhorst JP (2010) Spatial Panel Data Models. In Fischer MM, Getis A (Eds.)
    
17. % Handbook of Applied Spatial Analysis, Ch. C.2. Springer: Berlin Heidelberg New York.
    
18. %
    
19. % New:
    
20. 
    
21. % 1) Direct/Indirect effect esimates of the explanatory variables
    
22. % LeSage JP, Pace RK (2009) Introduction to Spatial Econometrics. Boca Raton, Taylor & Francis Group.
    
23. % routine direct_indirect_effects_estimates(results,W,spat_model) is written by J.P. Elhorst
    
24. % routines panel_effects_sar(results,vnames,W) and panel_effects_sar(results,vnames,W)
    
25. % are written and made available by D. Lacombe
    
26. % User may use both routines (note: results are slightly different from each other since they are based on draws from a distrobution
    
27. % or choose one particular routine. If N is large, user should choose
    
28. % Lacombe's routines, since this one is much more efficient computationally
    
29. 
    
30. % 2) Bias correction of coefficient estimates
    
31. % Lee Lf, Yu J. (2010) Estimation of spatial autoregressive models with
    
32. % fixed effects, Journal of Econometrics 154: 165-185.
    
33. 
    
34. % 3) Selection framework to determine which spatial panel data model best
    
35. % describes the data.
    
36. 
    
37. % dimensions of the problem
    
38. A=wk1read('cigarette.wk1',1,0);
    
39. W1=wk1read('Spat-Sym-US.wk1');
    
40. T=30; % number of time periods
    
41. N=46; % number of regions
    
42. % row-normalize W
    
43. W=normw(W1); % function of LeSage
    
44. y=A(:,[3]); % column number in the data matrix that corresponds to the dependent variable
    
45. x=A(:,[4,6]); % column numbers in the data matrix that correspond to the independent variables
    
46. for t=1:T
    
47.     t1=(t-1)*N+1;t2=t*N;
    
48.     wx(t1:t2,:)=W*x(t1:t2,:);
    
49. end
    
50. xconstant=ones(N*T,1);
    
51. [nobs K]=size(x);
    
52. % ----------------------------------------------------------------------------------------
    
53. % No fixed effects + spatially lagged dependent variable
    
54. info.lflag=0; % required for exact results
    
55. info.model=0;
    
56. info.fe=0; % Do not print intercept and fixed effects; use info.fe=1 to turn on
    
57. % New routines to calculate effects estimates
    
58. results=sar_panel_FE(y,[xconstant x],W,T,info);
    
59. vnames=char('logcit','intercept','logp','logy');
    
60. % Print out coefficient estimates
    
61. prt_sp(results,vnames,1);
    
62. % Print out effects estimates
    
63. spat_model=0;
    
64. direct_indirect_effects_estimates(results,W,spat_model);
    
65. panel_effects_sar(results,vnames,W);
    
66. % ----------------------------------------------------------------------------------------
    
67. % No fixed effects + spatially lagged dependent variable + spatially
    
68. % independent variables
    
69. info.lflag=0; % required for exact results
    
70. info.model=0;
    
71. info.fe=0; % Do not print intercept and fixed effects; use info.fe=1 to turn on
    
72. % New routines to calculate effects estimates
    
73. results=sar_panel_FE(y,[xconstant x wx],W,T,info);
    
74. vnames=char('logcit','intercept','logp','logy','W*logp','W*logy');
    
75. % Print out coefficient estimates
    
76. prt_sp(results,vnames,1);
    
77. % Print out effects estimates
    
78. spat_model=1;
    
79. direct_indirect_effects_estimates(results,W,spat_model);
    
80. panel_effects_sdm(results,vnames,W);
    
81. % ----------------------------------------------------------------------------------------
    
82. % Spatial fixed effects + spatially lagged dependent variable
    
83. info.lflag=0; % required for exact results
    
84. info.model=1;
    
85. info.fe=0; % Do not print intercept and fixed effects; use info.fe=1 to turn on
    
86. % New routines to calculate effects estimates
    
87. results=sar_panel_FE(y,x,W,T,info);
    
88. vnames=char('logcit','logp','logy');
    
89. % Print out coefficient estimates
    
90. prt_sp(results,vnames,1);
    
91. % Print out effects estimates
    
92. spat_model=0;
    
93. direct_indirect_effects_estimates(results,W,spat_model);
    
94. panel_effects_sar(results,vnames,W);
    
95. % ----------------------------------------------------------------------------------------
    
96. % Spatial fixed effects + spatially lagged dependent variable + spatially
    
97. % independent variables
    
98. info.lflag=0; % required for exact results
    
99. info.model=1;
    
100. info.fe=0; % Do not print intercept and fixed effects; use info.fe=1 to turn on
     
101. % New routines to calculate effects estimates
     
102. results=sar_panel_FE(y,[x wx],W,T,info);
     
103. vnames=char('logcit','logp','logy','W*logp','W*logy');
     
104. % Print out coefficient estimates
     
105. prt_sp(results,vnames,1);
     
106. % Print out effects estimates
     
107. spat_model=1;
     
108. direct_indirect_effects_estimates(results,W,spat_model);
     
109. panel_effects_sdm(results,vnames,W);
     
110. % ----------------------------------------------------------------------------------------
     
111. % Time period fixed effects + spatially lagged dependent variable
     
112. info.lflag=0; % required for exact results
     
113. info.model=2;
     
114. info.fe=0; % Do not print intercept and fixed effects; use info.fe=1 to turn on
     
115. % New routines to calculate effects estimates
     
116. results=sar_panel_FE(y,x,W,T,info);
     
117. vnames=char('logcit','logp','logy');
     
118. % Print out coefficient estimates
     
119. prt_sp(results,vnames,1);
     
120. % Print out effects estimates
     
121. spat_model=0;
     
122. direct_indirect_effects_estimates(results,W,spat_model);
     
123. panel_effects_sar(results,vnames,W);
     
124. % ----------------------------------------------------------------------------------------
     
125. % Time period fixed effects + spatially lagged dependent variable + spatially
     
126. % independent variables
     
127. info.lflag=0; % required for exact results
     
128. info.model=2;
     
129. info.fe=0; % Do not print intercept and fixed effects; use info.fe=1 to turn on
     
130. % New routines to calculate effects estimates
     
131. results=sar_panel_FE(y,[x wx],W,T,info);
     
132. vnames=char('logcit','logp','logy','W*logp','W*logy');
     
133. % Print out coefficient estimates
     
134. prt_sp(results,vnames,1);
     
135. % Print out effects estimates
     
136. spat_model=1;
     
137. direct_indirect_effects_estimates(results,W,spat_model);
     
138. panel_effects_sdm(results,vnames,W);
     
139. % ----------------------------------------------------------------------------------------
     
140. % Spatial and time period fixed effects + spatially lagged dependent variable
     
141. info.lflag=0; % required for exact results
     
142. info.model=3;
     
143. info.fe=0; % Do not print intercept and fixed effects; use info.fe=1 to turn on
     
144. % New routines to calculate effects estimates
     
145. results=sar_panel_FE(y,x,W,T,info);
     
146. vnames=char('logcit','logp','logy');
     
147. % Print out coefficient estimates
     
148. prt_sp(results,vnames,1);
     
149. % Print out effects estimates
     
150. spat_model=0;
     
151. direct_indirect_effects_estimates(results,W,spat_model);
     
152. panel_effects_sar(results,vnames,W);
     
153. % ----------------------------------------------------------------------------------------
     
154. % Spatial and time period fixed effects + spatially lagged dependent variable + spatially
     
155. % independent variables
     
156. % No bias correction
     
157. info.bc=0;
     
158. info.lflag=0; % required for exact results
     
159. info.model=3;
     
160. info.fe=0; % Do not print intercept and fixed effects; use info.fe=1 to turn on
     
161. % New routines to calculate effects estimates
     
162. results=sar_panel_FE(y,[x wx],W,T,info);
     
163. vnames=char('logcit','logp','logy','W*logp','W*logy');
     
164. % Print out coefficient estimates
     
165. prt_sp(results,vnames,1);
     
166. % Print out effects estimates
     
167. spat_model=1;
     
168. direct_indirect_effects_estimates(results,W,spat_model);
     
169. panel_effects_sdm(results,vnames,W);
     
170. % Wald test for spatial Durbin model against spatial lag model
     
171. btemp=results.parm;
     
172. varcov=results.cov;
     
173. Rafg=zeros(K,2*K+2);
     
174. for k=1:K
     
175.     Rafg(k,K+k)=1; % R(1,3)=0 and R(2,4)=0;
     
176. end
     
177. Wald_spatial_lag=(Rafg*btemp)'*inv(Rafg*varcov*Rafg')*Rafg*btemp
     
178. prob_spatial_lag=1-chis_cdf (Wald_spatial_lag, K) % probability greater than 0.05 points to insignificance
     
179. % LR test spatial Durbin model against spatial lag model (requires
     
180. % estimation results of the spatial lag model to be available)
     
181. resultssar=sar_panel_FE(y,x,W,T,info);
     
182. LR_spatial_lag=-2*(resultssar.lik-results.lik)
     
183. prob_spatial_lag=1-chis_cdf (LR_spatial_lag,K) % probability greater than 0.05 points to insignificance
     
184. % Wald test spatial Durbin model against spatial error model
     
185. R=zeros(K,1);
     
186. for k=1:K
     
187.     R(k)=btemp(2*K+1)*btemp(k)+btemp(K+k); % k changed in 1, 7/12/2010
     
188. %   R(1)=btemp(5)*btemp(1)+btemp(3);
     
189. %   R(2)=btemp(5)*btemp(2)+btemp(4);
     
190. end
     
191. Rafg=zeros(K,2*K+2);
     
192. for k=1:K
     
193.     Rafg(k,k)    =btemp(2*K+1); % k changed in 1, 7/12/2010
     
194.     Rafg(k,K+k)  =1;
     
195.     Rafg(k,2*K+1)=btemp(k);
     
196. %   Rafg(1,1)=btemp(5);Rafg(1,3)=1;Rafg(1,5)=btemp(1);
     
197. %   Rafg(2,2)=btemp(5);Rafg(2,4)=1;Rafg(2,5)=btemp(2);
     
198. end   
     
199. Wald_spatial_error=R'*inv(Rafg*varcov*Rafg')*R
     
200. prob_spatial_error=1-chis_cdf (Wald_spatial_error,K) % probability greater than 0.05 points to insignificance
     
201. % LR test spatial Durbin model against spatial error model (requires
     
202. % estimation results of the spatial error model to be available
     
203. resultssem=sem_panel_FE(y,x,W,T,info);
     
204. LR_spatial_error=-2*(resultssem.lik-results.lik)
     
205. prob_spatial_error=1-chis_cdf (LR_spatial_error,K) % probability greater than 0.05 points to insignificance

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1. % ----------------------------------------------------------------------------------------
   
2. % Spatial and time period fixed effects + spatially lagged dependent variable + spatially
   
3. % independent variables
   
4. info.lflag=0; % required for exact results
   
5. info.model=3;
   
6. info.fe=0; % Do not print intercept and fixed effects; use info.fe=1 to turn on
   
7. info.bc=1;
   
8. % New routines to calculate effects estimates
   
9. results=sar_panel_FE(y,[x wx],W,T,info);
   
10. vnames=char('logcit','logp','logy','W*logp','W*logy');
    
11. % Print out coefficient estimates
    
12. prt_sp(results,vnames,1);
    
13. % Print out effects estimates
    
14. spat_model=1;
    
15. direct_indirect_effects_estimates(results,W,spat_model);
    
16. panel_effects_sdm(results,vnames,W);
    
17. % Wald test for spatial lag model
    
18. btemp=results.parm;
    
19. varcov=results.cov;
    
20. Rafg=zeros(K,2*K+2);
    
21. for k=1:K
    
22.     Rafg(k,K+k)=1; % R(1,3)=0 and R(2,4)=0;
    
23. end
    
24. Wald_spatial_lag=(Rafg*btemp)'*inv(Rafg*varcov*Rafg')*Rafg*btemp
    
25. prob_spatial_lag= 1-chis_cdf (Wald_spatial_lag, K) % probability greater than 0.05 points to insignificance
    
26. % LR test spatial Durbin model against spatial lag model (requires
    
27. % estimation results of the spatial lag model to be available)
    
28. resultssar=sar_panel_FE(y,x,W,T,info);
    
29. LR_spatial_lag=-2*(resultssar.lik-results.lik)
    
30. prob_spatial_lag=1-chis_cdf (LR_spatial_lag,K) % probability greater than 0.05 points to insignificance
    
31. % Wald test for spatial error model
    
32. R=zeros(K,1);
    
33. for k=1:K
    
34.     R(k)=btemp(2*K+1)*btemp(k)+btemp(K+k); % k changed in 1, 7/12/2010
    
35. %   R(1)=btemp(5)*btemp(1)+btemp(3);
    
36. %   R(2)=btemp(5)*btemp(2)+btemp(4);
    
37. end
    
38. Rafg=zeros(K,2*K+2);
    
39. for k=1:K
    
40.     Rafg(k,k)    =btemp(2*K+1); % k changed in 1, 7/12/2010
    
41.     Rafg(k,K+k)  =1;
    
42.     Rafg(k,2*K+1)=btemp(k);
    
43. %   Rafg(1,1)=btemp(5);Rafg(1,3)=1;Rafg(1,5)=btemp(1);
    
44. %   Rafg(2,2)=btemp(5);Rafg(2,4)=1;Rafg(2,5)=btemp(2);
    
45. end   
    
46. Wald_spatial_error=R'*inv(Rafg*varcov*Rafg')*R
    
47. prob_spatial_error= 1-chis_cdf (Wald_spatial_error,K) % probability greater than 0.05 points to insignificance
    
48. % LR test spatial Durbin model against spatial error model (requires
    
49. % estimation results of the spatial error model to be available
    
50. resultssem=sem_panel_FE(y,x,W,T,info);
    
51. LR_spatial_error=-2*(resultssem.lik-results.lik)
    
52. prob_spatial_error=1-chis_cdf (LR_spatial_error,K) % probability greater than 0.05 points to insignificance
    
53. % needed for Hausman test later on
    
54. logliklag=results.lik;
    
55. blagfe=results.parm(1:end-1);
    
56. covblagfe=results.cov(1:end-1,1:end-1);
    
57. %
    
58. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% random effects estimator by ML %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    
59. %
    
60. % Spatial random effects and time period fixed effects + spatially lagged dependent variable + spatially
    
61. % independent variables
    
62. [ywith,xwith,meanny,meannx,meanty,meantx]=demean(y,[x wx],N,T,2); % 2=time dummies
    
63. info.model=1;
    
64. results=sar_panel_RE(ywith,xwith,W,T,info);
    
65. prt_sp(results,vnames,1);
    
66. % Print out effects estimates
    
67. spat_model=1;
    
68. direct_indirect_effects_estimates(results,W,spat_model);
    
69. panel_effects_sdm(results,vnames,W);
    
70. % Wald test for spatial lag model
    
71. btemp=results.parm(1:2*K+2);
    
72. varcov=results.cov(1:2*K+2,1:2*K+2);
    
73. Rafg=zeros(K,2*K+2);
    
74. for k=1:K
    
75.     Rafg(k,K+k)=1; % R(1,3)=0 and R(2,4)=0;
    
76. end
    
77. Wald_spatial_lag=(Rafg*btemp)'*inv(Rafg*varcov*Rafg')*Rafg*btemp
    
78. prob_spatial_lag= 1-chis_cdf (Wald_spatial_lag, K) % probability greater than 0.05 points to insignificance
    
79. % Wald test for spatial error model
    
80. R=zeros(K,1);
    
81. for k=1:K
    
82.     R(k)=btemp(2*K+1)*btemp(k)+btemp(K+k); % k changed in 1, 7/12/2010
    
83. %   R(1)=btemp(5)*btemp(1)+btemp(3);
    
84. %   R(2)=btemp(5)*btemp(2)+btemp(4);
    
85. end
    
86. Rafg=zeros(K,2*K+2);
    
87. for k=1:K
    
88.     Rafg(k,k)    =btemp(2*K+1); % k changed in 1, 7/12/2010
    
89.     Rafg(k,K+k)  =1;
    
90.     Rafg(k,2*K+1)=btemp(k);
    
91. %   Rafg(1,1)=btemp(5);Rafg(1,3)=1;Rafg(1,5)=btemp(1);
    
92. %   Rafg(2,2)=btemp(5);Rafg(2,4)=1;Rafg(2,5)=btemp(2);
    
93. end   
    
94. Wald_spatial_error=R'*inv(Rafg*varcov*Rafg')*R
    
95. prob_spatial_error= 1-chis_cdf (Wald_spatial_error,K) % probability greater than 0.05 points to insignificance
    
96. % needed for Hausman test later on
    
97. logliklagre=results.lik;
    
98. blagre=results.parm(1:end-2);
    
99. covblagre=results.cov(1:end-2,1:end-2);
    
100. % ----------------------------------------------------------------------------------------
     
101. % Hausman test FE versus RE
     
102. hausman=(blagfe-blagre)'*inv(covblagre-covblagfe)*(blagfe-blagre);
     
103. dof=length(blagfe);
     
104. probability=1-chis_prb(abs(hausman),dof);
     
105. % Note: probability < 0.025 implies rejection of random effects model in favor of fixed effects model
     
106. % Use 0.025, since it is a one-sided test
     
107. fprintf(1,'Hausman test-statistic, degrees of freedom and probability = %9.4f,%6d,%9.4f \n',abs(hausman),dof,probability);

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这两端代码都是同一个程序的，你把他们放在同一个文件夹里。
因为上传代码，超过10000字就发不了。
就截成两端了。

你把今晚我上传的这两段程序合并在一起用。


哦哦，哈哈，刚才那个一个demo里的。
这里还有一个更恰当的demo，发布代码太占屏幕，我保存为.txt 的文本文件了。你在MATLAB里新建一个空白文件，复制我上传的.txt文件的全部内容，粘贴进去就可以了。

谢谢tulipsliu  ：）  
我再试试

看您是高手，能请教您一个问题么，您在调用prt_sp函数时没有报错是么？我在调用时结果出现说是在prt_sp.m中没有定义norm_prb这个函数，难道我下载的空间面板工具箱中程序有错么？您能传一份完整的面板工具箱给我么？真的非常感谢，我可以给您论坛币，尽最大努力，拜托了，我都耗了半个月了，谢谢您！