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文件名:  Semi-Parametric-Copula-Approach-master.zip
资料下载链接地址: https://bbs.pinggu.org/a-2933926.html
附件大小:
已经下载论坛很多书籍，大概6000论坛币已经剩下只有300左右吧，此贴不收金币。我太累，已经看不了那么多书。

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原则上中国数量经济已经封闭群，只实行邀请制，没通过我的邀请无法进群。

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群嘛，不知道怎么介绍。中国数量金融群号码：949614744

其中应该是半参数那个，作者在程序里设置了Frank copula ,程序运行中途无法继续。我已经修改为 t copula
可以放心运行。贴上半参数里其中一个主程序的代码，可以不复制到你的文件里，因为程序全部在 ZIP 文件里。
这个程序应该是我在 github 网站下载，如果想看更新最新的代码，可以自己去搜索。

  1. %This MATLAB script runs the Semi-Parametric Copula Method presented in Manheim &
  2. %Detwiler (2019) for various analytical test cases. It was originally coded and developed by Derek Manheim,
  3. %at the University of California, Irvine, Department of Civil & Environmental
  4. %Engineering using MATLAB r2015b. It is a fully Global, Moment Independent, Monte Carlo (MC) based sampling method
  5. %that relies on the use of parametric copula models to describe the joint pdf between
  6. %model inputs (i.e., parameters) and outputs. The user must specify what
  7. %test case to run (testfunc, options are 1,2,3), the copula model used
  8. %(options are 'Gaussian','t','Clayton','Frank','Gumbel'), the number of MC sampling points used to construct
  9. %and sample the Copula model (Nevalpts) as well as the repetition number (Rep), which specifies the
  10. %location of the Sobol sequence used for QMC sampling. In addition to the files provided in this download folder,
  11. %in order to run this code users must have the statistics and machine learning toolbox package installed
  12. %and should be running MATLAB r2015 or later. The results are automatically
  13. %saved after running this script. *Note - running over 65536 Nevalpts will
  14. %require more advanced parallel computation (8-16 cores or more) and is not recommended to be run locally.
  15. %Final Version, updated 5-8-2019
  16. clear ; close all; clc

  18. %Run specifications
  19. Rep = 1; %Location in the sobol sequence (1,2,3,4) to set up different repetitions
  20. testfunc = 1; %Which analytical test cases?
  21. CopulaModel = 't'; %Which parametric Copula Model to use?
  22. Nevalpts = 256; %How many MC evaluation points used to construct and sample the Copula model? should be a factor of 2
  23. Delay=500+((Rep-1)*Nevalpts); %This sets the location in the sobol sequence for QMC sampling

  25. %Set up variables required for MLE optimization
  26. if strcmp(CopulaModel,'Clayton')
  27. model = 1;
  28. elseif strcmp(CopulaModel,'Frank')
  29. model = 2;
  30. elseif strcmp(CopulaModel,'Gumbel')
  31. model = 3;
  32. elseif strcmp(CopulaModel,'Gaussian')
  33. model = 4;
  34. elseif strcmp(CopulaModel,'t')
  35. model = 5;
  36. end

  38. %%Set up variables to evaluate each test case
  39. if testfunc == 1 %linear test case
  40. Nvar = 6;
  41. f = @(Xs) (1.5.*Xs(:,1)+1.6.*(Xs(:,2))+1.7.*(Xs(:,3))+1.8.*(Xs(:,4))+1.9.*(Xs(:,5))+2.*(Xs(:,6)));
  42. p = sobolset(Nvar,'skip',Delay);
  43. X = p(1:Nevalpts,:);
  44. for j = 1:Nvar
  45. Xs(:,j) = sqrt(2)*erfinv(2.*X(:,j)-1); %Model inputs
  46. end
  47. [Yn] = f(Xs);
  48. Y = Yn; %Model output
  49. elseif testfunc == 2 %non-linear test case
  50. Nvar = 6;
  51. bi = [4.5,2.5,1.5,0.5,0.3,0.1];
  52. f = @(x,bi) exp(sum(bi.*x,2))-prod((exp(bi)-1)./bi);
  53. p = sobolset(Nvar,'skip',Delay);
  54. X = p(1:Nevalpts,:);
  55. for j = 1:Nevalpts
  56. Yn(j,1) = exp(sum(bi.*X(j,:),2))-prod((exp(bi)-1)./bi);
  57. end
  58. Y = Yn; %model outputs
  59. for j = 1:Nvar
  60. Xs(:,j) = sqrt(2)*erfinv(2.*X(:,j)-1); %model inputs
  61. end
  62. elseif testfunc == 3 %Ishigami test case (non-linear/non-monotonic)
  63. Nvar = 3;
  64. lb1 = repmat([-pi -pi -pi],[Nevalpts 1]);
  65. ub1 = repmat([pi pi pi],[Nevalpts 1]);
  66. %Parameters
  67. a = 7; b = 0.1;
  68. f = @(x) (sin(x(:,1))+ a.*(sin(x(:,2)).^2)+(b.*(x(:,3).^4).*sin(x(:,1))));
  69. p = sobolset(Nvar,'skip',Delay);
  70. X = p(1:Nevalpts,:);
  71. Xss = lb1+((ub1-lb1).*X);
  72. %Evaluate model
  73. [Yn] = f(Xss);
  74. Y = Yn; %model output
  75. %Std normal space for Xs
  76. for j = 1:Nvar
  77. Xs(:,j) = sqrt(2)*erfinv(2.*X(:,j)-1); %model inputs
  78. end
  79. end

  81. %%Estimate alpha values for the Rolling Pin method
  82. %using the maximum likelihood (MLE) optimization approach
  83. %Here, an external call to LSHADEEpSinNLS algorithm is performed
  84. %Note, on 2 cores this will likely take a bit of time, especially if
  85. %Nevalpts is high
  86. ry = Y;
  87. rx = Xs;
  88. Ngens = 20000; %This is the number of generations used for MLE optimization
  89. Npop = 50; %This is the number of population members used for MLE optimization
  90. nTol = 1E-6; %This is the tolerance used to moniter convergence
  91. paramL = zeros(1,Nvar+1); paramU = ones(1,Nvar+1); %boundaries of the alpha parameter values, Rolling Pin method
  92. paramU(1,1) = 0;
  93. x2{1,1} = ry; x2{1,2} = rx; x2{1,3} = model;
  94. [solset] = LSHADEpSinNLS(@MLEObjFuncFinalAll, Nvar+1, paramL, paramU, Npop, nTol, Ngens,x2); %Run optimization call
  95. [m,p] = find(nonzeros(solset.Xbest(:,2)));
  96. alpha = solset.Xbest(m(end),:); %Extract best alpha values for Rolling Pin method

  98. %%Semi-Parametric method begins here
  99. %Estimate Yi, transformed values, Equation 4
  100. for j = 1:Nvar
  101. Yi(:,j) = (1-alpha(1,j+1)).*(ry(:,1))+alpha(1,j+1).*(rx(:,j));
  102. end

  104. %Find the PDFs and CDFs of the transformed distributions, Yi and Xs (Eqs.
  105. %5 and 6)
  106. parfor j = 1:Nvar
  107. tic
  108. pd1{j,1} = fitdist(Yi(:,j),'Kernel');
  109. FYr(:,j) = cdf(pd1{j,1},Yi(:,j));
  110. FX(:,j) = normcdf(rx(:,j),0,1);
  111. toc
  112. end

  114. %Fit Elliptical or Archimidean parametric copula model here using MATLAB's built in
  115. %functions
  116. for j = 1:Nvar
  117. u{j,1} = [FYr(:,j) FX(:,j)];
  118. %Construct Parametric Copula from CDF matrix, t-distribution is a
  119. %special case
  120. if model == 5
  121. [r{j,1},nus{j,1}] = copulafit(CopulaModel,u{j,1});
  122. else
  123. [r{j,1}] = copulafit(CopulaModel,u{j,1});
  124. end
  125. end

  127. %Sample from constructed parametric copula model, using MATLAB's built in
  128. %functions
  129. parfor j = 1:Nvar
  130. tic
  131. if model == 5
  132. cs = copularnd(CopulaModel,r{j,1},nus{j,1},Nevalpts);
  133. else
  134. cs = copularnd(CopulaModel,r{j,1},Nevalpts);
  135. end
  136. FYrn(:,j) = cs(:,1);
  137. FXn(:,j) = cs(:,2);
  138. Yin(:,j) = icdf(pd1{j,1},FYrn(:,j));
  139. Xn(:,j) = norminv(FXn(:,j),0,1);
  140. toc
  141. end

  143. %Find the marginal PDFs of the sampled dists, Yin and Xn (Eqs. 5 and 6)
  144. parfor j = 1:Nvar
  145. tic
  146. FYpdf1(:,j) = pdf(pd1{j,1},Yin(:,j));
  147. Fpdf(:,j) = normpdf(Xn(:,j),0,1);
  148. toc
  149. end

  151. %Transform the sample back to original space, inverse of Equation 4
  152. parfor j = 1:Nvar
  153. Ys(:,j) = (Yin(:,j)-alpha(1,j+1).*Xn(:,j))./(1-alpha(1,j+1));
  154. pd3{j,1} = fitdist(Ys(:,j),'Kernel');
  155. FyPDF(:,j) = pdf(pd3{j,1},Ys(:,j));
  156. end

  158. %Evaluate the parametric copula density
  159. for j = 1:Nvar
  160. %Evaluate joint pdf directly at pts
  161. a = FYpdf1(:,j).*(1-alpha(1,j+1));
  162. b = normpdf(Xn(:,j),0,1).*(1-alpha(1,1));
  163. if model == 5
  164. fXa1(:,j) = copulapdf(CopulaModel,[FYrn(:,j),FXn(:,j)],r{j,1},nus{j,1});
  165. else
  166. fXa1(:,j) = copulapdf(CopulaModel,[FYrn(:,j),FXn(:,j)],r{j,1});
  167. end
  168. fX(:,j) = fXa1(:,j).*a.*b; %Equation 7
  169. end

  171. %Estimate the MI sensitivity indices, Equation 8
  172. for j = 1:Nvar
  173. deltast(:,j) = abs(((FyPDF(:,j).*Fpdf(:,j))./(fX(:,j)))-1);
  174. delta(1,j) = 0.5*mean(deltast(:,j));
  175. end

  177. %Save results here
  178. filename = strcat(['SemiParamCopula_',CopulaModel,'_Test',num2str(testfunc),'_N',num2str(Nevalpts),'_Rep',num2str(Rep),'.mat']);
  179. save(filename,'delta','deltast','solset','Xs','Y','ry','rx');
复制代码


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