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基于MATLAB的spatial panel model 程序阅读与学习。

基于MATLAB的spatial panel model 程序阅读与学习。

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发此文的目的是通过研读程序，更深入的了解Elhost在写这个程序时的思想，通过认真研读程序，深入掌握空间面板程序建模的问题。%DemonstrationfileforElhorstPanelDatacode%%Datasetdownloadedfromwww.wiley.co.uk/ba ...
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发此文的目的是通过研读程序，更深入的了解Elhost在写这个程序时的思想，通过认真研读程序，深入掌握空间面板程序建模的问题。
  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:

  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

  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.

  34. % 3) Selection framework to determine which spatial panel data model best
  35. % describes the data.

  37. % dimensions of the problem
  38. load cigarette_mat;
  39. load US_W;
  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);
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