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1. 1-s2.0-S0304407601000793-main.pdf (81.79 KB)
   2015-4-18 18:16:48 上传
   
   
2. local n1 = 100 // number of individuals
   
3. local n2 = 10 // number of periods
   
4. local n = `n1'*`n2'
   
5. set obs `n'
   
6. quietly gen N = 0
   
7. *设定N个 个体
   
8. forvalues i = 1(1)`n1'{
   
9.         local k = `n2'*(`i'-1)+1
   
10.         local s = `n2'*`i'
    
11.         quietly replace N = `i' in `k'/`s'
    
12. }
    
13. *设定panel data期数
    
14. egen T = seq(), from(1)to(`n2')
    
15. 
    
16. xtset N T
    
17. 
    
18. 
    
19. 
    
20. * part II: to obtain unobserved individual-specific effect a
    
21. 
    
22. gen a = 0
    
23. 
    
24. forvalues j= 1(1)`n1'{
    
25.              local k = `n2'*(`j'-1)+1
    
26.              local s = `n2'*`j'
    
27.              local b = invnormal(uniform())
    
28.              quietly replace a = `b' in `k'/`s'
    
29. }
    
30. 
    
31. * part III: to obtain Xit.
    
32. 
    
33. * correlation coefficient between X and a: T
    
34. * 解释变量X与个体效应a相关
    
35. local Tao = 0.5
    
36. gen w = invnormal(uniform())
    
37. local t = sqrt(1 - `Tao'^2)
    
38. gen X = `Tao'*a + `t'*w
    
39. 
    
40. * part IV: draw  U
    
41. 
    
42. *产生随机扰动项u
    
43. gen u = invnormal(0.5*uniform()+0.5)
    
44. 
    
45. * to generate exogenous variabe z
    
46. 
    
47. gen z1 = rnormal(10,100)
    
48. gen z2 = rnormal(5,60)
    
49. label var z1 "exogenous regressors"
    
50. label var z2 "exogenous regressors"
    
51. 
    
52. * to generate x with measurement e
    
53. gen e = invnormal(uniform())
    
54. gen x = X + e
    
55. 
    
56. 
    
57. * part V: to generate y, X有测量偏误e
    
58. 
    
59. gen y = a + X + z1 + z2 + u
    
60. 
    
61. 
    
62. 
    
63. 
    
64. 
    
65. 
    
66. 
    
67. * ancillary matrix
    
68. 
    
69. matrix one1 = J(`n2',1,1) // to generate a vector whose elements are 1.
    
70. mat one1_t = one1'
    
71. mat A`n2' = I(`n2') - (1/`n2')*one1*one1_t
    
72. mat MR_1 = vec(A`n2')
    
73. mat MR = I(`n2'^2) - (1/(`n2'-1))*MR_1*MR_1'
    
74. 
    
75. 
    
76. mat define M = J(`n2'^2 + 2,`n2'^2 + 2,0)
    
77. mat M[1,1] = MR
    
78. mat M[`n2'^2 + 1,`n2'^2 + 1] = I(2)
    
79. 
    
80. * krocker product of a part of B
    
81. 
    
82. forvalues i = 1(1)`n1'{
    
83. mkmat x if N==`i',matrix(x`i')
    
84. mat x`i'I = x`i'#I(`n2')
    
85. }
    
86. 
    
87. 
    
88. *Zi
    
89. 
    
90. forvalues j = 1(1)`n1'{
    
91. mkmat z1 z2 if N == `j', matrix(Z_`j')
    
92. mat Z_`j'trans =Z_`j''
    
93. }
    
94. 
    
95. * construction of Bi
    
96. 
    
97. forvalues i = 1(1)`n1' {
    
98. 
    
99. mat define B`i' = J(`n2'^2 + 2,`n2',0)
    
100. mat B`i'[1,1] = x`i'I
     
101. mat B`i'[`n2'^2 + 1,1] = Z_`i'trans
     
102. }
     
103. 
     
104. 
     
105. * within transformation
     
106. egen y_meanw = mean(y), by(N)
     
107. egen x_meanw = mean(x), by(N)
     
108. 
     
109. egen z1_meanw = mean(z1), by(N)
     
110. egen z2_meanw = mean(z2), by(N)
     
111. gen dy = y - y_meanw
     
112. gen dx = x - x_meanw
     
113. gen dz1 = z1 - z1_meanw
     
114. gen dz2 = z2 - z2_meanw
     
115. 
     
116. 
     
117. *krocker product of a part of C
     
118. 
     
119. forvalues j = 1(1)`n1'{
     
120. mkmat dx if N == `j' ,matrix(dx`j')
     
121. 
     
122. mkmat dz1 if N == `j', matrix(dz1`j')
     
123. mkmat dz2 if N == `j', matrix(dz2`j')
     
124. 
     
125. mat_capp xz`j'_ : dx`j' dz1`j'
     
126. mat_capp xz`j' : xz`j'_ dz2`j'
     
127. }
     
128. 
     
129. forvalues i = 1(1)`n1'{
     
130. mat C`i'=B`i'*xz`i'
     
131. }
     
132. 
     
133. * construction of dn
     
134. 
     
135. forvalues j = 1(1)`n1'{
     
136. 
     
137. mkmat dy if N == `j', matrix(dy`j')
     
138. mat d`j' = B`j'*dy`j'
     
139. }
     
140. ***************************
     
141. 
     
142. * mean of C and d
     
143. 
     
144. 
     
145. local m = 1
     
146. mat dddd = J(`n2'^2 + 2, 1, 0)
     
147. while `m' <= `n1'{
     
148. mat dddd = d`m' + dddd
     
149. local m = `m' + 1
     
150. }
     
151. 
     
152. mat d_mean = (1/`n1')*dddd
     
153. quietly mat list d_mean
     
154. 
     
155. 
     
156. mat b1 = J(`n2'^2 + 2 , 3 ,0)
     
157. 
     
158. local n = 1
     
159. while `n' <= `n1'{
     
160. 
     
161. mat b1 = C`n' + b1
     
162. 
     
163. local n = `n' + 1
     
164. 
     
165. }
     
166. 
     
167. mat C_mean = (1/`n1')*b1
     
168. 
     
169. 
     
170. 
     
171. * computation of a first step consistent GMM estimator
     
172. 
     
173. * matrix C_mean_trans = C_mean'
     
174. 
     
175. 
     
176. 
     
177. matrix lamda1 = inv(C_mean'*M*C_mean)*C_mean'*M*d_mean
     
178. 
     
179. mat list lamda1
     
180. 
     
181. * its weighting matrix
     
182. 
     
183. matrix dc = J(`n2'^2 + 2,1,0)
     
184. 
     
185. forvalues i = 1(1)`n1'{
     
186. matrix dc`i' = J(`n2'^2 + 2,1,0)
     
187. matrix dc`i' = d`i' - C`i'*lamda1
     
188. }
     
189. 
     
190. mat W1 = J(`n2'^2 + 2, `n2'^2 + 2 , 0)
     
191. 
     
192. local j = 1
     
193. 
     
194. while `j' <= `n1'{
     
195. 
     
196. mat W1 = dc`j'*dc`j'' + W1
     
197. 
     
198. local j = `j' + 1
     
199. }
     
200. 
     
201. mat W_hat = (1/`n1')*W1
     
202. 
     
203. 
     
204. 
     
205. 
     
206. mat lamda2 = inv(C_mean'*M*inv(W_hat)*M*C_mean)*C_mean'*M*inv(W_hat)*M*d_mean
     
207. 
     
208. mat list lamda2

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如上面附件，毕业设计老师让根据这篇文献的内容做一个蒙特卡罗模拟。我的主要内容 是根据文献第三部门的模型做的，即 面板数据的一个解释变量存在测量偏误的情况下，如何通过GMM方法得到有测量偏误解释变量的一致估计量（consistent estimator）。但是，我按照文献中的两步法得到的估计量只有外生变量的系数是一致的，有测量偏误的系数差距比较大。后来我用STATA验证了文献中推导用到的假设，都基本没问题。所以想请STATA高手帮我看看是不是代码存在问题？

谢谢！

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关键词：求帮忙 Construction Individuals coefficient correlation replace number local