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Recent Tagsapplications character vectors CML CMLMT Constrained Optimization datasets dates dlibrary dllcall error error handling errors Excel FANPACMT file i/o floating network GAUSS Engine graphics GUI hotkeys installation Java API license licensing linux loading data matrices matrix matrix manipulation Maxlik MaxLikMT Memory optimization Optmum output PQG graphics procs RAM random numbers string functions strings structures threading Time Series writing data
Time Series 2.0 MT
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Time Series MT 2.1
var garch in mean program
i have difficuty in programing trivariate VAR(2) GARCH in Mean using FANPACMT. Any suggestion for making option on puting each conditional standard deviation in each mean equations ?
The option for a diagonal in-mean for multivariate models is a great suggestion which will get passed on to the developers. In the meantime, you could get the same result by constraining the off-diagonal elements to zero. First, run the model setting maxIters to 1 and print the vector of parameters. This will give you the order of the parameters so that you can then use apply linear constraints in sqpSolvemt:
library fanpacmt; #include fanpacmt.sdf load index[368,9] = index.asc; x0 = index[.,2:3]; x = 200 * ln(x0[2:368,.]./x0[1:367,.]); struct FanControl f0; f0 = fanControlCreate; f0.p = 1; f0.q = 1; f0.inmean = 1; // nonzero is in-mean model f0.sqrtcv = 1; // square root of conditional variance is used f0.SQPsolveMTControlProc = &sqpc; proc sqpc(struct sqpsolvemtControl c0); c0.maxiters = 1; retp(c0); endp; struct DS d0; d0 = dsCreate; d0.dataMatrix = x; struct fanEstimation out; out = CCCGarch(f0,d0); lbl = pvGetParNames(out.par); x = pvGetParVector(out.par); format /rd 10,4; for i(1,rows(x),1); print lbl[i];; print x[i]; endfor;
beta0[1,1] 0.2759 beta0[2,1] 0.0355 delta_s[1,1] -0.0507 delta_s[1,2] 0.0508 delta_s[2,1] 0.0053 delta_s[2,2] -0.0507 omega[1,1] 2.3277 omega[2,1] 0.6059 garch[1,1] 0.0106 garch[1,2] 0.0201 arch[1,1] 0.1112 arch[1,2] -0.0900 cor[2,1] 0.0509
We will want to constrain the off-diagonal elements of delta_s to zero, which are the 4th and 5th parameters of the 13 parameters in the parameter vector. We do this by adding lines to the linear constraints in sqpSolvemt:
proc sqpc(struct sqpsolvemtControl c0); c0.printIters = 1; c0.maxiters = 5; local A,B; A = zeros(2,13); B = zeros(2,1); A[1,4] = 1; A[2,5] = 1; c0.A = c0.A | A; c0.B = c0.B | B; retp(c0); endp;
The rows of A are the constraints we want to add, and the columns is equal to the number of parameters in the parameter vector. The constraint is Ax = B where x is the parameter vector. Next rerun the command file and we get
beta0[1,1] 0.3029 beta0[2,1] 0.1124 delta_s[1,1] -0.0223 delta_s[1,2] 0.0000 delta_s[2,1] 0.0000 delta_s[2,2] -0.0220 omega[1,1] 2.3488 omega[2,1] 0.6270 garch[1,1] 0.1316 garch[1,2] 0.0311 arch[1,1] 0.1318 arch[1,2] 0.0311 cor[2,1] 0.1213