WinBUGS在统计分析中的应用（第一部分）

你好，我刚学WINBUGS软件，现在想用来做SV-N模型，但是在模型的编辑（compile）这一步时，软件总是显示array index is greater than array bound for y 不知为什么，麻烦请帮忙指导一下，真的很急，谢谢！！！

model
{
for(i in 1:N)
{y~dnorm(0,p)
p<-exp(-theta)
}
theta[1]~dnorm(mu,itau2)
for(j in 2:N)
{theta[j]~dnorm(theta2[j],itau2)
theta2[j]<-mu+phi*(theta[j-1]-mu)}
phi<-2*phi1-1
mu~dnorm(0,0.01)
itau2~dgamma(2.5,0.025)
phi1~dbeta(20,1.5)
}
list(N=1000,
y=c(-0.01332,0.215247,0.165196,0.268095,-0.08932,0.784046,0.194879,-0.55396,-0.06357,-0.75685,
-0.80325,-0.24164,-0.14004,-0.06396,-0.03859,0.03763,-0.41906,-0.2917,-0.01348,-0.01342,
-0.21579,0.189124,0.215324,0.778916,-0.60096,-0.97844,0.646815,-0.92657,0.341113,-0.08932,
-0.06394,-0.19079,-0.16536,0.393219,0.242184,0.064052,-0.08932,0.03867,-0.01245,-0.06368,
-0.06368,-0.01235,0.270668,0.764389,0.249,-0.42764,-0.01135,-0.45268,0.09219,0.300745,
-0.14142,-0.21945,0.275452,0.434102,-0.03683,-0.0368,0.015803,0.200342,0.016219,0.759039,
0.123898,-0.14267,-0.19551,-0.19539,-0.56526,-0.45794,0.252919,-0.74644,0.357049,-0.32588,
-0.0368,0.173698,0.306509,-0.27424,0.14849,0.361428,0.176777,0.204207,0.823436,0.072621,
-0.11633,-1.51045,-0.77916,-0.14219,0.414057,-0.61912,0.148742,0.255552,0.711109,-0.83659,
0.150263,-1.33421,-0.71908,0.093948,-0.97636,0.014628,-0.08932,-0.7889,-0.45015,-0.29492,
0.06484,0.348767,0.454509,0.248824,-1.28074,0.013718,0.323902,-0.03755,0.195911,0.248824,
-0.29755,0.197099,-0.08932,-0.11539,0.067213,0.38176,-0.06309,0.015692,0.252739,-0.03659,
-0.19475,0.042482,0.280657,0.149247,0.28292,-0.14258,0.123898,-0.06264,-0.03593,0.124525,
-0.14283,-0.16953,-0.08932,0.151481,0.286416,-0.08932,0.126021,-0.35843,-0.2236,0.018089,
-0.06245,-0.16991,-0.08932,-0.08932,0.233432,-0.11626,0.315482,0.154349,-0.17061,-0.00803,
-0.14352,-0.30583,-0.2244,0.045758,0.208499,-0.00794,-0.08932,-0.00788,0.346171,-0.55197,
-0.00783,-0.11649,0.209957,0.047011,0.40302,0.185253,0.351572,0.021206,-0.47564,-0.17191,
0.048362,-0.72111,-0.19879,0.266892,-0.28129,-0.11672,0.10259,0.130458,0.241257,0.408604,
0.188379,-0.58863,-0.03397,-0.14468,0.049125,0.104826,0.077391,-0.14492,-0.03372,0.35661,
-0.42396,0.049973,0.30174,-0.11731,0.247001,-0.03316,0.361129,0.136667,-0.1176,-0.14585,
-0.20228,0.08017,-0.54066,-0.20184,-0.03308,0.729776,-0.08932,0.280049,-0.57207,-0.96368,
-0.06124,-0.53775,0.359109,0.276515,0.193005,-0.65318,-0.65002,0.162603,0.388274,-0.03298,
-0.08932,0.136348,0.30683,0.393839,-0.37382,0.166687,0.195904,0.196719,-0.17522,0.082549,
-0.11799,-0.03198,-0.06064,-0.03193,-0.08932,0.313325,-0.49197,0.025555,0.198448,-0.29085,
-0.11808,0.227453,0.141692,-0.11823,-0.06042,-0.06041,-0.0604,-0.06039,-0.205,-0.69442,
-0.06059,0.025687,0.169932,0.112786,-0.0315,0.113312,-0.58072,-0.37726,0.314021,-0.37759,
0.343392,0.055333,-0.00243,0.317183,0.027126,0.290074,-0.08932,-0.00156,-0.14784,-0.08932,
-0.49796,-0.08932,-0.14756,0.582508,-0.84845,0.318723,0.320395,0.410459,0.028637,-0.20728,
0.590852,0.059156,-0.26747,0.237524,-0.2083,-0.14876,-0.23776,0.029408,-0.26736,0.118424,
-0.6522,-0.26642,-0.05983,-0.35446,-0.14815,0.14618,-0.05985,-0.53056,-0.11867,-0.26522,
0.115927,-0.47016,-0.23541,-0.38087,-0.14753,-0.69846,-0.08932,0.287326,-0.03125,-0.61076,
0.403076,0.844174,-0.26502,-0.26471,-0.78781,-0.08932,-0.35,-0.11824,-0.0604,-0.40701,-0.40601,
-0.06057,-0.4051,-0.2039,-0.3752,0.196556,-0.11795,-0.2609,-0.43159,-0.06084,-0.2885,
-0.62795,0.705459,0.110364,-0.71557,-0.06094,-0.1177,-0.06094,-0.08932,-0.74005,-0.62371,
-0.11737,-0.14539,-0.48093,-0.39594,0.049934,-0.42321,-0.20037,-0.36641,-0.06165,-0.22762,
0.27064,-0.25562,-0.06163,0.299244,0.300759,-0.14514,-0.5903,-0.56017,0.104289,-0.117,
-0.3105,0.353524,-0.00607,0.523323,-0.28466,0.357731,-0.00528,-0.42508,-0.14517,-0.86797,
0.021543,0.494743,0.217984,-0.31291,0.386403,-1.03852,0.691714,-0.64783,-0.39518,-0.61542,
-0.42017,-0.33674,-1.26308,-0.1707,-0.22481,-0.5486,-0.19708,-0.00851,-0.06237,-0.51966,
0.314067,-0.00845,-0.70769,-0.33025,-0.32967,0.204521,-1.10078,-0.1952,-0.32713,-0.32657,
0.438662,-0.27443,0.069324-0.14223,0.680627,-0.91215,-0.2478,0.572669,0.791271,0.071622,
-0.43771,-0.11607,1.311186,0.673001,-0.19858,-0.17119,-0.55197,-0.44167,-0.33253,0.316358,
-1.35499,0.151803,0.367729,-1.02804,0.28511,1.015364,0.45401,-0.84916,-1.45865,-1.01833,
0.26577,-1.961,-0.27197,0.171707,-1.85103,0.270853,1.783782,2.16811,-3.62531,-4.12899,
7.486337,-10.1731,-1.89421,0.125552,-2.59122,-1.0174,-0.02001,-0.57347,-0.73123,1.732474,
0.775472,0.145696,1.763023,-0.56756,-1.7923,-2.29282,0.324472,-1.12061,1.241889,1.823496,
-0.86352,0.944279,-0.84209,-0.27663,4.87394,-2.0368,-2.11779,-0.77208,0.216165,-3.44495,
-0.0438,1.401702,3.390926,-4.83265,-2.81318,0.378125,-1.24284,-1.4475,-0.5452,0.75897,
-0.3511,0.281736,1.120681,-1.08041,0.724853,0.420149,0.601489,1.148178,-0.08932,-0.08932,
-0.38321,0.772166,1.055641,0.072024,1.327823,0.992787,1.100318,1.308628,-0.62188,-0.90683,
1.06676,-2.10378,-0.82262,0.004995,1.264457,0.678805,0.00711,1.539893,0.64887,0.753937,
-0.98196,2.23317,-0.79426,-1.26155,-0.68265,0.826866,-2.78887,-0.83712,0.223592,-0.47431,
-0.85488,-0.42242,2.023095,-0.74221,-0.73797,-0.9002,-2.96907,0.095478,-0.6427,-1.95726,
-1.78997,-1.01689,1.193881,0.670628,0.789557,0.114598,-1.97659,0.804938,0.496265,-2.01332,
0.332573,-2.05039,-0.95833,0.344236,-0.58775,-1.24988,0.747503,-0.32606,-0.23968,1.033073,
-0.08932,2.394049,1.571026,-1.86087,-0.46647,0.132358,-0.5101,0.887812,-0.37902,-0.84304,
1.491123,0.834836,1.785019,1.164467,-2.89992,1.260041,-1.4614,0.754565,0.139983,-0.8896,
1.33272,1.282952,0.780965,-1.28694,0.825211,0.264661,1.411242,1.312344,1.20892,0.976496,
4.031302,-1.66091,-1.46025,0.492296,1.366828,-1.34238,-1.42722,1.121591,-1.75057,0.185644,
0.714905,3.219945,3.360211,-2.09393,1.591838,1.483805,0.513915,0.048286,1.409041,0.190633,
2.466183,2.828889,0.029197,0.148137,1.80064,-1.89009,0.05951,0.448314,0.210618,-1.19464,
2.133659,2.526614,-3.58232,-1.34607,0.626885,-1.30976,-0.08932,3.556209,-2.93283,2.020461,
1.074907,-2.43449,-2.32189,0.73835,-1.47475,0.057155,1.446897,0.388148,-0.80468,1.135854,
1.76174,1.765445,0.505266,2.196492,0.943612,2.474078,-4.93029,-1.84982,2.084334,-4.45138,
1.632738,1.505209,0.924307,-0.78729,0.863671,0.358535,0.489458,-0.34697,1.4993,1.69114,
-2.3913,0.628462,-0.15479,0.074425,-0.1876,2.162429,1.225378,-0.83307,0.451037,-0.19086,
-0.22455,2.096557,2.074811,-2.42596,0.014144,-0.26171,1.823447,0.121574,1.043033,0.696677,
0.378053,0.70663,0.092463,0.567855,0.756738,0.838501,1.375493,-0.08932,-0.20277,0.289323,
0.138555,-0.05129,0.981434,-0.24299,0.179754,0.373642,-0.43675,1.503254,-1.2958,1.588112,
-1.65063,0.376517,-0.16711,-0.67083,0.881748,2.560625,1.935039,-1.55098,1.372336,-2.31388,
1.808575,-8.60344,-0.98385,-0.86552,2.031688,2.039227,1.186551,3.55609,-0.89318,0.472703,
1.615957,-0.00739,-1.99723,-4.0315,0.453315,-6.23177,-0.78137,0.895548,2.134639,-1.90924,
1.655664,-0.94706,2.430082,2.300002,-0.24521,-2.20845,2.772436,3.83118,-1.42673,-0.77137,
1.807809,2.7627,0.79508,-1.76721,0.620345,-0.08932,2.327586,-0.04639,1.993183,0.394089,
2.137942,-0.71797,-1.24643,0.220893,-0.26671,2.924173,1.661068,1.408833,-0.04214,0.668616,
3.690198,-2.52847,-0.18566,-0.66542,0.390525,1.608512,0.745858,2.966263,1.345129,-0.14091,
-0.34685,0.013612,0.065277,0.947494,0.223831,1.703975,1.304055,-0.08932,2.479827,1.19239,
-1.37103,-0.80657,1.573688,-0.70231,-0.03375,0.188997,0.413613,0.078887,0.022974,0.756945,
-1.16006,-1.42564,0.632302,0.862266,1.612364,-1.67846,-0.033,0.079835,1.33156,-0.03206,
-1.90549,0.361637,-1.21293,-0.53525,-0.42247,1.756884,0.704333,0.882948,0.371508,-0.89439,
-0.31816,-0.37463,0.539431,0.082844,0.428963,0.60593,0.435308,0.203333,-0.90661,0.143507,
0.027296,0.085861,-0.5558,0.027092,0.085554,1.025386,-1.14567,0.0275,0.261966,0.912749,
2.06645,-0.33116,0.273655,-0.33145,0.943808,-0.02822,-0.15043,-0.57683,0.94949,0.89834,
-2.1158,0.459293,0.955234,-0.45924,1.398616,-0.5256,-1.81563,0.400875,-0.76273,-0.75822,
-2.60292,-0.73734,-0.90804,-0.55417,0.901074,0.556789,0.62032,0.148349,-2.26652,-0.26379,
0.20163,0.553767,-3.49121,-0.08932,-0.03262,-1.04897,-0.53775,1.432263,-0.65557,0.023672,
-0.25877,-0.14574,0.874131,-0.48716,0.024185,0.708858,0.139904,-0.43296,-0.48875,-0.26002,
1.399824,1.305239,0.262378,-0.90804,0.085554,1.202534,-0.56102,0.382377,-4.08638,-1.7227,
-0.31254,-1.58313,1.85141,-0.42471,2.453906,2.637856,1.272855,0.388862,0.210708,-2.99151,
-2.22628,2.457112,-0.38198,-1.42461,-2.36992,4.171541,3.564873,0.645525,0.527203,1.846424,
2.659333,1.412502,-0.08932,0.108241,0.43943,1.851187,1.820695,0.393937,3.002983,0.555149,
0.559329,1.73488,-2.70575,-0.51881,0.411932,0.414457,-0.01715,0.999618,1.455394,0.505475,
1.716596,1.133,-0.39631,1.14431,0.689498,0.61684,-0.32526,0.14662,0.860052,0.548639,
0.391834,-0.33019,0.071192,0.636195,-0.00838,0.723691,0.074077,-0.08932,-0.49732,-1.14239,
-0.16987,-0.00877,-0.57164,0.473607,0.395723,0.316687,0.809744,-0.00719,-0.25353,0.734406,
-0.00657,-0.08932,-1.64993,0.07381,0.565881,0.487523,1.074448,-1.17041,-1.89265,-0.97894,
1.861959,1.566345,0.412771,-0.00539,0.923345,0.676965,1.374329,0.084289,-1.29832,-0.60302,
0.338578,-0.08932,0.426586,0.515954,1.39607,1.329141,1.078181,-0.08932,-1.07809,1.715781,
0.092993,-0.08932,0.367926,0.831495,0.374286,-0.46038,0.281736,-0.18222,0.562753,-0.27606,
0.847892,-0.08932,-0.37141,1.043835,-0.18424,0.38619,-0.18461,-1.41384,-0.37088,-0.08932))



list(mu=0,itau1=0.02,phi1=0.975)

当我运行compile这一步是，软件界面上说educational version cannot do this model，是什么意思啊！有知道的麻烦告诉一声，有急用！谢谢

请问用openbugs时，前面模型都没有问题，但是在load inits这步，总显示unable to generate initial values for node [011483COH] of type Grapht.Mixing,是什么意思？难道初值设定有问题吗？谢谢

请教大家一下，如果炫耀载入的数据非常多，如何载入那么多数据啊，有没有简便方法？

[未知用户] 更正一下，是需要载入的数据多

您好，winbugs软件有详细的说明吗

等WinBUGS的update时，顺便把评论都看了一遍，觉得很多朋友对Bayes分析还是很有兴趣的，但椰丝儿们留错了地方，cos论坛的椰子板块会更靠谱。看到后来发现很多评论挂着一大长串代码的都在问一件事情：SV模型怎么做？这让我觉得很惊讶，不过想来也合理。

国内的金融方面最近几年一直很热，学生极多。很多学校会对金融的学生要求论文，而高质量的也是学生保研，各种评优的重要依据。最快的方法，就是用各种统计和计量方法来让自己的文章升级（记得有个神文写的是如何拿计量经济五大杀器力克CSSCI）。我接触的一些研究生，甚至是本科生，都在搞一些很高级的处理方法和模型（高级的意思是读一两本书都搞不清楚）。金融方面比较好用的，无非就是波动模型，因此在GARCH模型已经搞烂掉的现在，SV模型凭借它”拒人于千里之外“的难理解的特点越来越受到学生的青睐。这里的MCMC之所以被提如此之多，就是因为目前SV估计比较好的方法，就是它了。

有时候一想起这些就会有些郁闷，但觉得孩子们本身没错儿，靠自己的努力和智力，迅速学一些别人学不会的方法来争取自己的前程是很棒的。令我郁闷的是这种特别的规则而引发的浮躁心态以及对思考问题的态度。就拿SV来说，有个孩子学经济的，问想拿MCMC估计SV，但理解不了那个超长的函数，我就说这个叫做后验概率密度，他说什么叫后验密度？我说是通过贝叶斯原理得到的，他说贝叶斯不是统计学的吗？我要估计的是SV模型……尽最快速度达到目标，却忽略了积累与思索，这便得不偿失了。如果有负责的老师在这方面加以引导和帮助，我想这便是孩子们在大学，甚至是近十年的一大幸事。可惜的是这可遇而不可求，因为老师同样面临和孩子们一样的境况，只不过从保研变成了评职称而已，并且他们只能靠自己。

如果真的不想要培养研究人才，拜托请不要变相用这种方式折磨孩子们的思维了。至少在绝大多数工作中，老板不会让你没事就去研究MCMC……而在看过评论之后唯一欣慰的是，COS的编辑们很热心负责的，他们抱着对统计认真的态度，在帮助在这条路上迷乱的人，尽管这很有限，但也让很多人满载而归了。

深夜吐槽 尽请见谅~那我也来做点有用的吧。SV不多说，R上面也有包可以做，MCMC方法cos上也有文章介绍。如果你确实初学，没接触R也不想学非要用WinBUGS，这里有个paper
http://www.mysmu.edu/faculty/yujun/Research/YuEJ2000.pdf
我觉得这个东西，结合上面文章的内容，绝大多SV问题就应该解决了。另外有其他问题可以到cos论坛金融区去请教版主，版主在这方面可是很强的。

[未知用户] 神评论:)

[未知用户] 同惊叹！
不知道Ron大侠是否愿意写一篇科普文投给COS主站呢？

[未知用户] 您好，请问你这个问题解决了吗？我最近在做用SV预测波动率的实证，遇到了同样的问题，难道theta[j]需要初始化吗？

[未知用户] 请问你的问题解决了吗？我也碰到一样的问题

请问谢老师，用openbugs时，在load inits这步，总显示unable to generate initial values for node要怎么解决？

MODEL:
Y=a*T1*P1*W*E*P2/(1+P2/b)+c*T2+d

Input data：
T1= [0.9902 0.9696 0.9851 0.9341 0.9787 0.9502 0.8941 0.9987 0.9987 0.9897 0.9946 0.7887]
T2= [18.261 22.945 22.074 24.285 22.474 23.742 25.379 20.631 19.377 21.735 18.711 11.493]
P1= [0.5961 0.5777 0.5989 0.6213 0.6258 0.6478 0.6416 0.6554 0.6514 0.6553 1.0000 1.0000]
P2= [19265.5 18877 18052.5 15476 20851.5 21657 22012 6481 21738 20884 17597.5 18969]
W= [0.8755 0.8492 0.8791 0.9119 0.9176 0.9511 0.9417 0.9625 0.9552 0.9615 0.9525 0.9180]
E= [0.2927 0.2817 0.3708 0.4006 0.3684 0.4377 0.4311 0.4939 0.4847 0.4762 0.4602 0.4043]

Yo= [-6.23 -7.24 2.21 6.1 15.18 16.38 13.63 2.53 10.51 4.08 0.73 3.51]


Cost function:
J(p) = 1/2*[(Yo-Y(p))'Co ^(-1) (Yo-Y(p))+(p-pb) ' Pb^(-1) (p-pb)]
where Yo represents the data vector, Y(p) is the model output vector, Co and Pb the error covariance matrix on data and model parameters, respectively. p is the parameter vector. pb is a priori values of p.
pb = [0.06, 1600, 0.05, 1];

My question is how to estimate the parameter (a, b, c, d) using WinBUGS?

[未知用户] 请问如何输入幂的形式的，比如a^b，但是中间那个符号不能识别

[未知用户] 您好，我也在做这个dcc-msv模型。代码部分也是这个。可是我到了compile这一步以后就一直提示“vairable N is not defined”.请问你遇到了这个问题吗？谢谢

[未知用户] ”请问如何输入幂的形式的，比如a^b，但是中间那个符号不能识别“ 请问这个问题您解决了没，求教

[未知用户] 请问您的问题解决了吗？我也遇到这个问题

[未知用户] pow(a,b)

[未知用户] 请问怎么导出估计的theta[j]啊？谢谢