gdp.54 <- NA

gdp.55 <- NA

gdp.56 <- NA

gdp.57 <- NA

gdp.58 <- NA

gdp.59 <- NA

gdp.60 <- NA

gdp.61 <- NA

gdp.62 <- NA

gdp.63 <- NA

gdp.64 <- NA

gdp.65 <- NA

gdp.66 <- NA

gdp.67 <- NA

gdp.68 <- NA

gdp.69 <- NA

gdp.70 <- NA

gdp.71 <- NA

gdp.72 <- NA

gdp.73 <- NA

gdp.74 <- NA

gdp.75 <- NA

gdp.76 <- NA

gdp.101 <- NA

gdp.102 <- NA

gdp.103 <- NA

gdp.104 <- NA

gdp.105 <- NA

gdp.106 <- NA

gdp.107 <- NA

gdp.108 <- NA

gdp.109 <- NA

gdp.110 <- NA

gdp.111 <- NA

gdp.112 <- NA

gdp.113 <- NA

gdp.114 <- NA

gdp.115 <- NA

gdp.116 <- NA

gdp.117 <- NA

gdp.118 <- NA

gdp.119 <- NA

MAPPE <- matrix(rep(NA,m*k),nrow=m,ncol=k)

sPPE <- matrix(rep(NA,m*k),nrow=m,ncol=k)

factors.all <- data.frame (gdp.r, state, year,

gdp.r.tlag,

gdp.r.tslag,

pop.r.tlag,

pop.r.tslag,

lab.r.tlag,

lab.r.tslag)

f01 <- gdp.r ~ gdp.r.tlag

f02 <- gdp.r ~ gdp.r.tlag + pop.r.tlag

f03 <- gdp.r ~ gdp.r.tlag + lab.r.tlag

f04 <- gdp.r ~ gdp.r.tlag + pop.r.tlag + lab.r.tlag

f05 <- gdp.r ~ gdp.r.tlag + gdp.r.tslag

f06 <- gdp.r ~ gdp.r.tlag + lab.r.tslag

f07 <- gdp.r ~ gdp.r.tlag + pop.r.tslag

f08 <- gdp.r ~ gdp.r.tlag + pop.r.tlag + gdp.r.tslag

f09 <- gdp.r ~ gdp.r.tlag + lab.r.tlag + gdp.r.tslag

f10 <- gdp.r ~ gdp.r.tlag + pop.r.tlag + lab.r.tlag + gdp.r.tslag

f11 <- gdp.r ~ gdp.r.tlag + pop.r.tlag + pop.r.tslag

f12 <- gdp.r ~ gdp.r.tlag + lab.r.tlag + pop.r.tslag

f13 <- gdp.r ~ gdp.r.tlag + pop.r.tlag + lab.r.tlag + pop.r.tslag

f14 <- gdp.r ~ gdp.r.tlag + lab.r.tlag + lab.r.tslag

f15 <- gdp.r ~ gdp.r.tlag + pop.r.tlag + lab.r.tslag

f16 <- gdp.r ~ gdp.r.tlag + pop.r.tlag + lab.r.tlag + lab.r.tslag

f17 <- gdp.r ~ gdp.r.tlag + pop.r.tlag + lab.r.tlag + lab.r.tslag + pop.r.tslag + gdp.r.tslag

f18 <- gdp.r ~ gdp.r.tlag + pop.r.tlag + gdp.r.tslag + pop.r.tslag

f19 <- gdp.r ~ gdp.r.tlag + lab.r.tlag + gdp.r.tslag + lab.r.tslag

x01 <- data.frame(gdp.r.tlag) # monotone=c(1)

x02 <- data.frame(gdp.r.tlag,pop.r.tlag) # monotone=c(1,2)

x03 <- data.frame(gdp.r.tlag,lab.r.tlag)

x04 <- data.frame(gdp.r.tlag,pop.r.tlag,lab.r.tlag)

x05 <- data.frame(gdp.r.tlag,gdp.r.tslag)

x06 <- data.frame(gdp.r.tlag,lab.r.tslag)

x07 <- data.frame(gdp.r.tlag,pop.r.tslag)

x08 <- data.frame(gdp.r.tlag,pop.r.tlag,gdp.r.tslag)

x09 <- data.frame(gdp.r.tlag,lab.r.tlag,gdp.r.tslag)

x10 <- data.frame(gdp.r.tlag,pop.r.tlag,lab.r.tlag,gdp.r.tslag)

x11 <- data.frame(gdp.r.tlag,pop.r.tlag,pop.r.tslag)

x12 <- data.frame(gdp.r.tlag,lab.r.tlag,pop.r.tslag)

x13 <- data.frame(gdp.r.tlag,pop.r.tlag,lab.r.tlag,pop.r.tslag)

x14 <- data.frame(gdp.r.tlag,lab.r.tlag,lab.r.tslag)

x15 <- data.frame(gdp.r.tlag,pop.r.tlag,lab.r.tslag)

x16 <- data.frame(gdp.r.tlag,pop.r.tlag,lab.r.tlag,lab.r.tslag)

x17 <- data.frame(gdp.r.tlag,pop.r.tlag,lab.r.tlag,lab.r.tslag,pop.r.tslag,gdp.r.tslag)

x18 <- data.frame(gdp.r.tlag,pop.r.tlag,gdp.r.tslag,pop.r.tslag)

x19 <- data.frame(gdp.r.tlag,lab.r.tlag,gdp.r.tslag,lab.r.tslag)

x20 <- data.frame(gdp.r.tlag)

x21 <- data.frame(gdp.r.tlag,pop.r.tlag)

x22 <- data.frame(gdp.r.tlag,lab.r.tlag)

x23 <- data.frame(gdp.r.tlag,pop.r.tlag,lab.r.tlag)

x24 <- data.frame(gdp.r.tlag,gdp.r.tslag)

x25 <- data.frame(gdp.r.tlag,lab.r.tslag)

x26 <- data.frame(gdp.r.tlag,pop.r.tslag)

x27 <- data.frame(gdp.r.tlag,pop.r.tlag,gdp.r.tslag)

x28 <- data.frame(gdp.r.tlag,lab.r.tlag,gdp.r.tslag)

x29 <- data.frame(gdp.r.tlag,pop.r.tlag,lab.r.tlag,gdp.r.tslag)

x30 <- data.frame(gdp.r.tlag,pop.r.tlag,pop.r.tslag)

x31 <- data.frame(gdp.r.tlag,lab.r.tlag,pop.r.tslag)

x32 <- data.frame(gdp.r.tlag,pop.r.tlag,lab.r.tlag,pop.r.tslag)

x33 <- data.frame(gdp.r.tlag,lab.r.tlag,lab.r.tslag)

x34 <- data.frame(gdp.r.tlag,pop.r.tlag,lab.r.tslag)

x35 <- data.frame(gdp.r.tlag,pop.r.tlag,lab.r.tlag,lab.r.tslag)

x36 <- data.frame(gdp.r.tlag,pop.r.tlag,lab.r.tlag,lab.r.tslag,pop.r.tslag,gdp.r.tslag)

x37 <- data.frame(gdp.r.tlag,pop.r.tlag,gdp.r.tslag,pop.r.tslag)

x38 <- data.frame(gdp.r.tlag,lab.r.tlag,gdp.r.tslag,lab.r.tslag)

x39 <- data.frame(gdp.r.tlag) # monotone=c(1)

x40 <- data.frame(gdp.r.tlag,pop.r.tlag) # monotone=c(1,2)

x41 <- data.frame(gdp.r.tlag,lab.r.tlag)

x42 <- data.frame(gdp.r.tlag,pop.r.tlag,lab.r.tlag)

x43 <- data.frame(gdp.r.tlag,gdp.r.tslag)

x44 <- data.frame(gdp.r.tlag,lab.r.tslag)

x45 <- data.frame(gdp.r.tlag,pop.r.tslag)

x46 <- data.frame(gdp.r.tlag,pop.r.tlag,gdp.r.tslag)

x47 <- data.frame(gdp.r.tlag,lab.r.tlag,gdp.r.tslag)

x48 <- data.frame(gdp.r.tlag,pop.r.tlag,lab.r.tlag,gdp.r.tslag)

x49 <- data.frame(gdp.r.tlag,pop.r.tlag,pop.r.tslag)

x50 <- data.frame(gdp.r.tlag,lab.r.tlag,pop.r.tslag)

x51 <- data.frame(gdp.r.tlag,pop.r.tlag,lab.r.tlag,pop.r.tslag)

x52 <- data.frame(gdp.r.tlag,lab.r.tlag,lab.r.tslag)

x53 <- data.frame(gdp.r.tlag,pop.r.tlag,lab.r.tslag)

x54 <- data.frame(gdp.r.tlag,pop.r.tlag,lab.r.tlag,lab.r.tslag)

x55 <- data.frame(gdp.r.tlag,pop.r.tlag,lab.r.tlag,lab.r.tslag,pop.r.tslag,gdp.r.tslag)

x56 <- data.frame(gdp.r.tlag,pop.r.tlag,gdp.r.tslag,pop.r.tslag)

x57 <- data.frame(gdp.r.tlag,lab.r.tlag,gdp.r.tslag,lab.r.tslag)

x58 <- data.frame(gdp.r.tlag) # monotone=c(1)

x59 <- data.frame(gdp.r.tlag,pop.r.tlag) # monotone=c(1,2)

x60 <- data.frame(gdp.r.tlag,lab.r.tlag)

x61 <- data.frame(gdp.r.tlag,pop.r.tlag,lab.r.tlag)

x62 <- data.frame(gdp.r.tlag,gdp.r.tslag)

x63 <- data.frame(gdp.r.tlag,lab.r.tslag)

x64 <- data.frame(gdp.r.tlag,pop.r.tslag)

x65 <- data.frame(gdp.r.tlag,pop.r.tlag,gdp.r.tslag)

x66 <- data.frame(gdp.r.tlag,lab.r.tlag,gdp.r.tslag)

x67 <- data.frame(gdp.r.tlag,pop.r.tlag,lab.r.tlag,gdp.r.tslag)

x68 <- data.frame(gdp.r.tlag,pop.r.tlag,pop.r.tslag)

x69 <- data.frame(gdp.r.tlag,lab.r.tlag,pop.r.tslag)

x70 <- data.frame(gdp.r.tlag,pop.r.tlag,lab.r.tlag,pop.r.tslag)

x71 <- data.frame(gdp.r.tlag,lab.r.tlag,lab.r.tslag)

x72 <- data.frame(gdp.r.tlag,pop.r.tlag,lab.r.tslag)

x73 <- data.frame(gdp.r.tlag,pop.r.tlag,lab.r.tlag,lab.r.tslag)

x74 <- data.frame(gdp.r.tlag,pop.r.tlag,lab.r.tlag,lab.r.tslag,pop.r.tslag,gdp.r.tslag)

x75 <- data.frame(gdp.r.tlag,pop.r.tlag,gdp.r.tslag,pop.r.tslag)

x76 <- data.frame(gdp.r.tlag,lab.r.tlag,gdp.r.tslag,lab.r.tslag)

x101 <- data.frame(gdp.r.tlag) # monotone=c(1)

x102 <- data.frame(gdp.r.tlag,pop.r.tlag) # monotone=c(1,2)

x103 <- data.frame(gdp.r.tlag,lab.r.tlag)

x104 <- data.frame(gdp.r.tlag,pop.r.tlag,lab.r.tlag)

x105 <- data.frame(gdp.r.tlag,gdp.r.tslag)

x106 <- data.frame(gdp.r.tlag,lab.r.tslag)

x107 <- data.frame(gdp.r.tlag,pop.r.tslag)

x108 <- data.frame(gdp.r.tlag,pop.r.tlag,gdp.r.tslag)

x109 <- data.frame(gdp.r.tlag,lab.r.tlag,gdp.r.tslag)

x110 <- data.frame(gdp.r.tlag,pop.r.tlag,lab.r.tlag,gdp.r.tslag)

x111 <- data.frame(gdp.r.tlag,pop.r.tlag,pop.r.tslag)

x112 <- data.frame(gdp.r.tlag,lab.r.tlag,pop.r.tslag)

x113 <- data.frame(gdp.r.tlag,pop.r.tlag,lab.r.tlag,pop.r.tslag)

x114 <- data.frame(gdp.r.tlag,lab.r.tlag,lab.r.tslag)

x115 <- data.frame(gdp.r.tlag,pop.r.tlag,lab.r.tslag)

x116 <- data.frame(gdp.r.tlag,pop.r.tlag,lab.r.tlag,lab.r.tslag)

x117 <- data.frame(gdp.r.tlag,pop.r.tlag,lab.r.tlag,lab.r.tslag,pop.r.tslag,gdp.r.tslag)

x118 <- data.frame(gdp.r.tlag,pop.r.tlag,gdp.r.tslag,pop.r.tslag)

x119 <- data.frame(gdp.r.tlag,lab.r.tlag,gdp.r.tslag,lab.r.tslag)

for(j in 12:(k-1))

{

LEARNset <- year>=(year0+j) & year<=(year0+window-1+j)

TESTset <- year==(year0+window+j)

TESTset2 <- year==(year0+window-1+j)

gdp.actual <- gdp[TESTset]

plm01 <- plm(formula = f01, data=factors.all[LEARNset,], model='within', effect='individual',index=c('state','year'))

plm02 <- plm(formula = f02, data=factors.all[LEARNset,], model='within', effect='individual',index=c('state','year'))

plm03 <- plm(formula = f03, data=factors.all[LEARNset,], model='within', effect='individual',index=c('state','year'))

plm04 <- plm(formula = f04, data=factors.all[LEARNset,], model='within', effect='individual',index=c('state','year'))

plm05 <- plm(formula = f05, data=factors.all[LEARNset,], model='within', effect='individual',index=c('state','year'))

plm06 <- plm(formula = f06, data=factors.all[LEARNset,], model='within', effect='individual',index=c('state','year'))

plm07 <- plm(formula = f07, data=factors.all[LEARNset,], model='within', effect='individual',index=c('state','year'))

plm08 <- plm(formula = f08, data=factors.all[LEARNset,], model='within', effect='individual',index=c('state','year'))

plm09 <- plm(formula = f09, data=factors.all[LEARNset,], model='within', effect='individual',index=c('state','year'))

plm10 <- plm(formula = f10, data=factors.all[LEARNset,], model='within', effect='individual',index=c('state','year'))

plm11 <- plm(formula = f11, data=factors.all[LEARNset,], model='within', effect='individual',index=c('state','year'))

plm12 <- plm(formula = f12, data=factors.all[LEARNset,], model='within', effect='individual',index=c('state','year'))

plm13 <- plm(formula = f13, data=factors.all[LEARNset,], model='within', effect='individual',index=c('state','year'))

plm14 <- plm(formula = f14, data=factors.all[LEARNset,], model='within', effect='individual',index=c('state','year'))

plm15 <- plm(formula = f15, data=factors.all[LEARNset,], model='within', effect='individual',index=c('state','year'))

plm16 <- plm(formula = f16, data=factors.all[LEARNset,], model='within', effect='individual',index=c('state','year'))

plm17 <- plm(formula = f17, data=factors.all[LEARNset,], model='within', effect='individual',index=c('state','year'))

plm18 <- plm(formula = f18, data=factors.all[LEARNset,], model='within', effect='individual',index=c('state','year'))

plm19 <- plm(formula = f19, data=factors.all[LEARNset,], model='within', effect='individual',index=c('state','year'))

y.learn <- as.matrix(gdp.r[LEARNset])

y.actual <- as.matrix(gdp.r[TESTset])

x01.learn <- as.matrix(x01[LEARNset,])

x01.predict <- as.matrix(x01[TESTset,])

x02.learn <- as.matrix(x02[LEARNset,])

x02.predict <- as.matrix(x02[TESTset,])

x03.learn <- as.matrix(x03[LEARNset,])

x03.predict <-

as.matrix(x03[TESTset,])

x04.learn <- as.matrix(x04[LEARNset,])

x04.predict <- as.matrix(x04[TESTset,])

x05.learn <- as.matrix(x05[LEARNset,])

x05.predict <- as.matrix(x05[TESTset,])

x06.learn <- as.matrix(x06[LEARNset,])

x06.predict <- as.matrix(x06[TESTset,])

x07.learn <- as.matrix(x07[LEARNset,])

x07.predict <- as.matrix(x07[TESTset,])

x08.learn <- as.matrix(x08[LEARNset,])

x08.predict <- as.matrix(x08[TESTset,])

x09.learn <- as.matrix(x09[LEARNset,])

x09.predict <- as.matrix(x09[TESTset,])

x10.learn <- as.matrix(x10[LEARNset,])

x10.predict <- as.matrix(x10[TESTset,])

x11.learn <- as.matrix(x11[LEARNset,])

x11.predict <- as.matrix(x11[TESTset,])

x12.learn <- as.matrix(x12[LEARNset,])

x12.predict <- as.matrix(x12[TESTset,])

x13.learn <- as.matrix(x13[LEARNset,])

x13.predict <- as.matrix(x13[TESTset,])

x14.learn <- as.matrix(x14[LEARNset,])

x14.predict <- as.matrix(x14[TESTset,])

x15.learn <- as.matrix(x15[LEARNset,])

x15.predict <- as.matrix(x15[TESTset,])

x16.learn <- as.matrix(x16[LEARNset,])

x16.predict <- as.matrix(x16[TESTset,])

x17.learn <- as.matrix(x17[LEARNset,])

x17.predict <- as.matrix(x17[TESTset,])

x18.learn <- as.matrix(x18[LEARNset,])

x18.predict <- as.matrix(x18[TESTset,])

x19.learn <- as.matrix(x19[LEARNset,])

x19.predict <- as.matrix(x19[TESTset,])

x20.learn <- as.matrix(x20[LEARNset,])

x20.predict <- as.matrix(x20[TESTset,])

x21.learn <- as.matrix(x21[LEARNset,])

x21.predict <- as.matrix(x21[TESTset,])

x22.learn <- as.matrix(x22[LEARNset,])

x22.predict <- as.matrix(x22[TESTset,])

x23.learn <- as.matrix(x23[LEARNset,])

x23.predict <- as.matrix(x23[TESTset,])

x24.learn <- as.matrix(x24[LEARNset,])

x24.predict <- as.matrix(x24[TESTset,])

x25.learn <- as.matrix(x25[LEARNset,])

x25.predict <- as.matrix(x25[TESTset,])

x26.learn <- as.matrix(x26[LEARNset,])

x26.predict <- as.matrix(x26[TESTset,])

x27.learn <- as.matrix(x27[LEARNset,])

x27.predict <- as.matrix(x27[TESTset,])

x28.learn <- as.matrix(x28[LEARNset,])

x28.predict <- as.matrix(x28[TESTset,])

x29.learn <- as.matrix(x29[LEARNset,])

x29.predict <- as.matrix(x29[TESTset,])

x30.learn <- as.matrix(x30[LEARNset,])

x30.predict <- as.matrix(x30[TESTset,])

x31.learn <- as.matrix(x31[LEARNset,])

x31.predict <- as.matrix(x31[TESTset,])

x32.learn <- as.matrix(x32[LEARNset,])

x32.predict <- as.matrix(x32[TESTset,])

x33.learn <- as.matrix(x33[LEARNset,])

x33.predict <- as.matrix(x33[TESTset,])

x34.learn <- as.matrix(x34[LEARNset,])

x34.predict <- as.matrix(x34[TESTset,])

x35.learn <- as.matrix(x35[LEARNset,])

x35.predict <- as.matrix(x35[TESTset,])

x36.learn <- as.matrix(x36[LEARNset,])

x36.predict <- as.matrix(x36[TESTset,])

x37.learn <- as.matrix(x37[LEARNset,])

x37.predict <- as.matrix(x37[TESTset,])

x38.learn <- as.matrix(x38[LEARNset,])

x38.predict <- as.matrix(x38[TESTset,])

x39.learn <- as.matrix(x39[LEARNset,])

x39.predict <- as.matrix(x39[TESTset,])

x40.learn <- as.matrix(x40[LEARNset,])

x40.predict <- as.matrix(x40[TESTset,])

x41.learn <- as.matrix(x41[LEARNset,])

x41.predict <- as.matrix(x41[TESTset,])

x42.learn <- as.matrix(x42[LEARNset,])

x42.predict <- as.matrix(x42[TESTset,])

x43.learn <- as.matrix(x43[LEARNset,])

x43.predict <- as.matrix(x43[TESTset,])

x44.learn <- as.matrix(x44[LEARNset,])

x44.predict <- as.matrix(x44[TESTset,])

x45.learn <- as.matrix(x45[LEARNset,])

x45.predict <- as.matrix(x45[TESTset,])

x46.learn <- as.matrix(x46[LEARNset,])

x46.predict <- as.matrix(x46[TESTset,])

x47.learn <- as.matrix(x47[LEARNset,])

x47.predict <- as.matrix(x47[TESTset,])

x48.learn <- as.matrix(x48[LEARNset,])

x48.predict <- as.matrix(x48[TESTset,])

x49.learn <- as.matrix(x49[LEARNset,])

x49.predict <- as.matrix(x49[TESTset,])

x50.learn <- as.matrix(x50[LEARNset,])

x50.predict <- as.matrix(x50[TESTset,])

x51.learn <- as.matrix(x51[LEARNset,])

x51.predict <- as.matrix(x51[TESTset,])

x52.learn <- as.matrix(x52[LEARNset,])

x52.predict <- as.matrix(x52[TESTset,])

x53.learn <- as.matrix(x53[LEARNset,])

x53.predict <- as.matrix(x53[TESTset,])

x54.learn <- as.matrix(x54[LEARNset,])

x54.predict <- as.matrix(x54[TESTset,])

x55.learn <- as.matrix(x55[LEARNset,])

x55.predict <- as.matrix(x55[TESTset,])

x56.learn <- as.matrix(x56[LEARNset,])

x56.predict <- as.matrix(x56[TESTset,])

x57.learn <- as.matrix(x57[LEARNset,])

x57.predict <- as.matrix(x57[TESTset,])

x58.learn <- as.matrix(x58[LEARNset,])

x58.predict <- as.matrix(x58[TESTset,])

x59.learn <- as.matrix(x59[LEARNset,])

x59.predict <- as.matrix(x59[TESTset,])

x60.learn <- as.matrix(x60[LEARNset,])

x60.predict <- as.matrix(x60[TESTset,])

x61.learn <- as.matrix(x61[LEARNset,])

x61.predict <- as.matrix(x61[TESTset,])

x62.learn <- as.matrix(x62[LEARNset,])

x62.predict <- as.matrix(x62[TESTset,])

x63.learn <- as.matrix(x63[LEARNset,])

x63.predict <- as.matrix(x63[TESTset,])

x64.learn <- as.matrix(x64[LEARNset,])

x64.predict <- as.matrix(x64[TESTset,])

x65.learn <- as.matrix(x65[LEARNset,])

x65.predict <- as.matrix(x65[TESTset,])

x66.learn <- as.matrix(x66[LEARNset,])

x66.predict <- as.matrix(x66[TESTset,])

x67.learn <- as.matrix(x67[LEARNset,])

x67.predict <- as.matrix(x67[TESTset,])

x68.learn <- as.matrix(x68[LEARNset,])

x68.predict <- as.matrix(x68[TESTset,])

x69.learn <- as.matrix(x69[LEARNset,])

x69.predict <- as.matrix(x69[TESTset,])

x70.learn <- as.matrix(x70[LEARNset,])

x70.predict <- as.matrix(x70[TESTset,])

x71.learn <- as.matrix(x71[LEARNset,])

x71.predict <- as.matrix(x71[TESTset,])

x72.learn <- as.matrix(x72[LEARNset,])

x72.predict <- as.matrix(x72[TESTset,])

x73.learn <- as.matrix(x73[LEARNset,])

x73.predict <- as.matrix(x73[TESTset,])

x74.learn <- as.matrix(x74[LEARNset,])

x74.predict <- as.matrix(x74[TESTset,])

x75.learn <- as.matrix(x75[LEARNset,])

x75.predict <- as.matrix(x75[TESTset,])

x76.learn <- as.matrix(x76[LEARNset,])

x76.predict <- as.matrix(x76[TESTset,])

x101.predict <- as.matrix(x101[TESTset,])

x102.predict <- as.matrix(x102[TESTset,])

x103.predict <- as.matrix(x103[TESTset,])

x104.predict <- as.matrix(x104[TESTset,])

x105.predict <- as.matrix(x105[TESTset,])

x106.predict <- as.matrix(x106[TESTset,])

x107.predict <- as.matrix(x107[TESTset,])

x108.predict <- as.matrix(x108[TESTset,])

x109.predict <- as.matrix(x109[TESTset,])

x110.predict <- as.matrix(x110[TESTset,])

x111.predict <- as.matrix(x111[TESTset,])

x112.predict <- as.matrix(x112[TESTset,])

x113.predict <- as.matrix(x113[TESTset,])

x114.predict <- as.matrix(x114[TESTset,])

x115.predict <- as.matrix(x115[TESTset,])

x116.predict <- as.matrix(x116[TESTset,])

x117.predict <- as.matrix(x117[TESTset,])

x118.predict <- as.matrix(x118[TESTset,])

x119.predict <- as.matrix(x119[TESTset,])

mlp01 <- monmlp.fit(x=x01.learn, y=y.learn, hidden1=10, n.ensemble=2, n.trials=5, bag=TRUE, monotone=c(1), silent=TRUE, iter.max=5000)

mlp02 <- monmlp.fit(x=x02.learn, y=y.learn, hidden1=10, n.ensemble=2, n.trials=5, bag=TRUE, monotone=c(1,2), silent=TRUE, iter.max=5000)

mlp03 <- monmlp.fit(x=x03.learn, y=y.learn, hidden1=10, n.ensemble=2, n.trials=5, bag=TRUE, monotone=c(1,2), silent=TRUE, iter.max=5000)

mlp04 <- monmlp.fit(x=x04.learn, y=y.learn, hidden1=10, n.ensemble=2, n.trials=5, bag=TRUE, monotone=c(1,2,3), silent=TRUE, iter.max=5000)

mlp05 <- monmlp.fit(x=x05.learn, y=y.learn, hidden1=10, n.ensemble=2, n.trials=5, bag=TRUE, monotone=c(1), silent=TRUE, iter.max=5000)

mlp06 <- monmlp.fit(x=x06.learn, y=y.learn, hidden1=10, n.ensemble=2, n.trials=5, bag=TRUE, monotone=c(1), silent=TRUE, iter.max=5000)

mlp07 <- monmlp.fit(x=x07.learn, y=y.learn, hidden1=10, n.ensemble=2, n.trials=5, bag=TRUE, monotone=c(1), silent=TRUE, iter.max=5000)

mlp08 <- monmlp.fit(x=x08.learn, y=y.learn, hidden1=10, n.ensemble=2, n.trials=5, bag=TRUE, monotone=c(1,2), silent=TRUE, iter.max=5000)

mlp09 <- monmlp.fit(x=x09.learn, y=y.learn, hidden1=10, n.ensemble=2, n.trials=5, bag=TRUE, monotone=c(1,2), silent=TRUE, iter.max=5000)

mlp10 <- monmlp.fit(x=x10.learn, y=y.learn, hidden1=10, n.ensemble=2, n.trials=5, bag=TRUE, monotone=c(1,2,3), silent=TRUE, iter.max=5000)

mlp11 <- monmlp.fit(x=x11.learn, y=y.learn, hidden1=10, n.ensemble=2, n.trials=5, bag=TRUE, monotone=c(1,2), silent=TRUE, iter.max=5000)

mlp12 <- monmlp.fit(x=x12.learn, y=y.learn, hidden1=10, n.ensemble=2, n.trials=5, bag=TRUE, monotone=c(1,2), silent=TRUE, iter.max=5000)

mlp13 <- monmlp.fit(x=x13.learn, y=y.learn, hidden1=10, n.ensemble=2, n.trials=5, bag=TRUE, monotone=c(1,2,3), silent=TRUE, iter.max=5000)

mlp14 <- monmlp.fit(x=x14.learn, y=y.learn, hidden1=10, n.ensemble=2, n.trials=5, bag=TRUE, monotone=c(1,2), silent=TRUE, iter.max=5000)

mlp15 <- monmlp.fit(x=x15.learn, y=y.learn, hidden1=10, n.ensemble=2, n.trials=5, bag=TRUE, monotone=c(1,2), silent=TRUE, iter.max=5000)

mlp16 <- monmlp.fit(x=x16.learn, y=y.learn, hidden1=10, n.ensemble=2, n.trials=5, bag=TRUE, monotone=c(1,2,3), silent=TRUE, iter.max=5000)

mlp17 <- monmlp.fit(x=x17.learn, y=y.learn, hidden1=10, n.ensemble=2, n.trials=5, bag=TRUE, monotone=c(1,2,3), silent=TRUE, iter.max=5000)

mlp18 <- monmlp.fit(x=x18.learn, y=y.learn, hidden1=10, n.ensemble=2, n.trials=5, bag=TRUE, monotone=c(1,2), silent=TRUE, iter.max=5000)

mlp19 <- monmlp.fit(x=x19.learn, y=y.learn, hidden1=10, n.ensemble=2, n.trials=5, bag=TRUE, monotone=c(1,2), silent=TRUE, iter.max=5000)

mlp20 <- monmlp.fit(x=x20.learn, y=y.learn, hidden1=5, n.ensemble=2, n.trials=5, bag=TRUE, monotone=c(1), silent=TRUE, iter.max=5000)

mlp21 <- monmlp.fit(x=x21.learn, y=y.learn, hidden1=5, n.ensemble=2, n.trials=5, bag=TRUE, monotone=c(1,2), silent=TRUE, iter.max=5000)

mlp22 <- monmlp.fit(x=x22.learn, y=y.learn, hidden1=5, n.ensemble=2, n.trials=5, bag=TRUE, monotone=c(1,2), silent=TRUE, iter.max=5000)

mlp23 <- monmlp.fit(x=x23.learn, y=y.learn, hidden1=5, n.ensemble=2, n.trials=5, bag=TRUE, monotone=c(1,2,3), silent=TRUE, iter.max=5000)

mlp24 <- monmlp.fit(x=x24.learn, y=y.learn, hidden1=5, n.ensemble=2, n.trials=5, bag=TRUE, monotone=c(1), silent=TRUE, iter.max=5000)

mlp25 <- monmlp.fit(x=x25.learn, y=y.learn, hidden1=5, n.ensemble=2, n.trials=5, bag=TRUE, monotone=c(1), silent=TRUE, iter.max=5000)

mlp26 <- monmlp.fit(x=x26.learn, y=y.learn, hidden1=5, n.ensemble=2, n.trials=5, bag=TRUE, monotone=c(1), silent=TRUE, iter.max=5000)

mlp27 <- monmlp.fit(x=x27.learn, y=y.learn, hidden1=5, n.ensemble=2, n.trials=5, bag=TRUE, monotone=c(1,2), silent=TRUE, iter.max=5000)

mlp28 <- monmlp.fit(x=x28.learn, y=y.learn, hidden1=5, n.ensemble=2, n.trials=5, bag=TRUE, monotone=c(1,2), silent=TRUE, iter.max=5000)

mlp29 <- monmlp.fit(x=x29.learn, y=y.learn, hidden1=5, n.ensemble=2, n.trials=5, bag=TRUE, monotone=c(1,2,3), silent=TRUE, iter.max=5000)

mlp30 <- monmlp.fit(x=x30.learn, y=y.learn, hidden1=5, n.ensemble=2, n.trials=5, bag=TRUE, monotone=c(1,2), silent=TRUE, iter.max=5000)

mlp31 <- monmlp.fit(x=x31.learn, y=y.learn, hidden1=5, n.ensemble=2, n.trials=5, bag=TRUE, monotone=c(1,2), silent=TRUE, iter.max=5000)

mlp32 <- monmlp.fit(x=x32.learn, y=y.learn, hidden1=5, n.ensemble=2, n.trials=5, bag=TRUE, monotone=c(1,2,3), silent=TRUE, iter.max=5000)

mlp33 <- monmlp.fit(x=x33.learn, y=y.learn, hidden1=5, n.ensemble=2, n.trials=5, bag=TRUE, monotone=c(1,2), silent=TRUE, iter.max=5000)

mlp34 <- monmlp.fit(x=x34.learn, y=y.learn, hidden1=5, n.ensemble=2, n.trials=5, bag=TRUE, monotone=c(1,2), silent=TRUE, iter.max=5000)

mlp35 <- monmlp.fit(x=x35.learn, y=y.learn, hidden1=5, n.ensemble=2, n.trials=5, bag=TRUE, monotone=c(1,2,3), silent=TRUE, iter.max=5000)

mlp36 <- monmlp.fit(x=x36.learn, y=y.learn, hidden1=5, n.ensemble=2, n.trials=5, bag=TRUE, monotone=c(1,2,3), silent=TRUE, iter.max=5000)

mlp37 <- monmlp.fit(x=x37.learn, y=y.learn, hidden1=5, n.ensemble=2, n.trials=5, bag=TRUE, monotone=c(1,2), silent=TRUE, iter.max=5000)

mlp38 <- monmlp.fit(x=x38.learn, y=y.learn, hidden1=5, n.ensemble=2, n.trials=5, bag=TRUE, monotone=c(1,2), silent=TRUE, iter.max=5000)

mlp39 <- monmlp.fit(x=x39.learn, y=y.learn, hidden1=10, n.ensemble=4, n.trials=5, bag=TRUE, monotone=c(1), silent=TRUE, iter.max=5000)

mlp40 <- monmlp.fit(x=x40.learn, y=y.learn, hidden1=10, n.ensemble=4, n.trials=5, bag=TRUE, monotone=c(1,2), silent=TRUE, iter.max=5000)

mlp41 <- monmlp.fit(x=x41.learn, y=y.learn, hidden1=10, n.ensemble=4, n.trials=5, bag=TRUE, monotone=c(1,2), silent=TRUE, iter.max=5000)

mlp42 <- monmlp.fit(x=x42.learn, y=y.learn, hidden1=10, n.ensemble=4, n.trials=5, bag=TRUE, monotone=c(1,2,3), silent=TRUE, iter.max=5000)

mlp43 <- monmlp.fit(x=x43.learn, y=y.learn, hidden1=10, n.ensemble=4, n.trials=5, bag=TRUE, monotone=c(1), silent=TRUE, iter.max=5000)

mlp44 <- monmlp.fit(x=x44.learn, y=y.learn, hidden1=10, n.ensemble=4, n.trials=5, bag=TRUE, monotone=c(1), silent=TRUE, iter.max=5000)

mlp45 <- monmlp.fit(x=x45.learn, y=y.learn, hidden1=10, n.ensemble=4, n.trials=5, bag=TRUE, monotone=c(1), silent=TRUE, iter.max=5000)

mlp46 <- monmlp.fit(x=x46.learn, y=y.learn, hidden1=10, n.ensemble=4, n.trials=5, bag=TRUE, monotone=c(1,2), silent=TRUE, iter.max=5000)

mlp47 <- monmlp.fit(x=x47.learn, y=y.learn, hidden1=10, n.ensemble=4, n.trials=5, bag=TRUE, monotone=c(1,2), silent=TRUE, iter.max=5000)

mlp48 <- monmlp.fit(x=x48.learn, y=y.learn, hidden1=10, n.ensemble=4, n.trials=5, bag=TRUE, monotone=c(1,2,3), silent=TRUE, iter.max=5000)

mlp49 <- monmlp.fit(x=x49.learn, y=y.learn, hidden1=10, n.ensemble=4, n.trials=5, bag=TRUE, monotone=c(1,2), silent=TRUE, iter.max=5000)

mlp50 <- monmlp.fit(x=x50.learn, y=y.learn, hidden1=10, n.ensemble=4, n.trials=5, bag=TRUE, monotone=c(1,2), silent=TRUE, iter.max=5000)

mlp51 <- monmlp.fit(x=x51.learn, y=y.learn, hidden1=10, n.ensemble=4, n.trials=5, bag=TRUE, monotone=c(1,2,3), silent=TRUE, iter.max=5000)

mlp52 <- monmlp.fit(x=x52.learn, y=y.learn, hidden1=10, n.ensemble=4, n.trials=5, bag=TRUE, monotone=c(1,2), silent=TRUE, iter.max=5000)

mlp53 <- monmlp.fit(x=x53.learn, y=y.learn, hidden1=10, n.ensemble=4, n.trials=5, bag=TRUE, monotone=c(1,2), silent=TRUE, iter.max=5000)

mlp54 <- monmlp.fit(x=x54.learn, y=y.learn, hidden1=10, n.ensemble=4, n.trials=5, bag=TRUE, monotone=c(1,2,3), silent=TRUE, iter.max=5000)

mlp55 <- monmlp.fit(x=x55.learn, y=y.learn, hidden1=10, n.ensemble=4, n.trials=5, bag=TRUE, monotone=c(1,2,3), silent=TRUE, iter.max=5000)

mlp56 <- monmlp.fit(x=x56.learn, y=y.learn, hidden1=10, n.ensemble=4, n.trials=5, bag=TRUE, monotone=c(1,2), silent=TRUE, iter.max=5000)

mlp57 <- monmlp.fit(x=x57.learn, y=y.learn, hidden1=10, n.ensemble=4, n.trials=5, bag=TRUE, monotone=c(1,2), silent=TRUE, iter.max=5000)

mlp58 <- monmlp.fit(x=x58.learn, y=y.learn, hidden1=5, n.ensemble=4, n.trials=5, bag=TRUE, monotone=c(1), silent=TRUE, iter.max=5000)

mlp59 <- monmlp.fit(x=x59.learn, y=y.learn, hidden1=5, n.ensemble=4, n.trials=5, bag=TRUE, monotone=c(1,2), silent=TRUE, iter.max=5000)

mlp60 <- monmlp.fit(x=x60.learn, y=y.learn, hidden1=5, n.ensemble=4, n.trials=5, bag=TRUE, monotone=c(1,2), silent=TRUE, iter.max=5000)

mlp61 <- monmlp.fit(x=x61.learn, y=y.learn, hidden1=5, n.ensemble=4, n.trials=5, bag=TRUE, monotone=c(1,2,3), silent=TRUE, iter.max=5000)

mlp62 <- monmlp.fit(x=x62.learn, y=y.learn, hidden1=5, n.ensemble=4, n.trials=5, bag=TRUE, monotone=c(1), silent=TRUE, iter.max=5000)

mlp63 <- monmlp.fit(x=x63.learn, y=y.learn, hidden1=5, n.ensemble=4, n.trials=5, bag=TRUE, monotone=c(1), silent=TRUE, iter.max=5000)

mlp64 <- monmlp.fit(x=x64.learn, y=y.learn, hidden1=5, n.ensemble=4, n.trials=5, bag=TRUE, monotone=c(1), silent=TRUE, iter.max=5000)

mlp65 <- monmlp.fit(x=x65.learn, y=y.learn, hidden1=5, n.ensemble=4, n.trials=5, bag=TRUE, monotone=c(1,2), silent=TRUE, iter.max=5000)

mlp66 <- monmlp.fit(x=x66.learn, y=y.learn, hidden1=5, n.ensemble=4, n.trials=5, bag=TRUE, monotone=c(1,2), silent=TRUE, iter.max=5000)

mlp67 <- monmlp.fit(x=x67.learn, y=y.learn, hidden1=5, n.ensemble=4, n.trials=5, bag=TRUE, monotone=c(1,2,3), silent=TRUE, iter.max=5000)

mlp68 <- monmlp.fit(x=x68.learn, y=y.learn, hidden1=5, n.ensemble=4, n.trials=5, bag=TRUE, monotone=c(1,2), silent=TRUE, iter.max=5000)

mlp69 <- monmlp.fit(x=x69.learn, y=y.learn, hidden1=5, n.ensemble=4, n.trials=5, bag=TRUE, monotone=c(1,2), silent=TRUE, iter.max=5000)

mlp70 <- monmlp.fit(x=x70.learn, y=y.learn, hidden1=5, n.ensemble=4, n.trials=5, bag=TRUE, monotone=c(1,2,3), silent=TRUE, iter.max=5000)

mlp71 <- monmlp.fit(x=x71.learn, y=y.learn, hidden1=5, n.ensemble=4, n.trials=5, bag=TRUE, monotone=c(1,2), silent=TRUE, iter.max=5000)

mlp72 <- monmlp.fit(x=x72.learn, y=y.learn, hidden1=5, n.ensemble=4, n.trials=5, bag=TRUE, monotone=c(1,2), silent=TRUE, iter.max=5000)

mlp73 <- monmlp.fit(x=x73.learn, y=y.learn, hidden1=5, n.ensemble=4, n.trials=5, bag=TRUE, monotone=c(1,2,3), silent=TRUE, iter.max=5000)

mlp74 <- monmlp.fit(x=x74.learn, y=y.learn, hidden1=5, n.ensemble=4, n.trials=5, bag=TRUE, monotone=c(1,2,3), silent=TRUE, iter.max=5000)

mlp75 <- monmlp.fit(x=x75.learn, y=y.learn, hidden1=5, n.ensemble=4, n.trials=5, bag=TRUE, monotone=c(1,2), silent=TRUE, iter.max=5000)

mlp76 <- monmlp.fit(x=x76.learn, y=y.learn, hidden1=5, n.ensemble=4, n.trials=5, bag=TRUE, monotone=c(1,2), silent=TRUE, iter.max=5000)

fix01 <- as.matrix(fixef(plm01))

fix02 <- as.matrix(fixef(plm02))

fix03 <- as.matrix(fixef(plm03))

fix04 <- as.matrix(fixef(plm04))

fix05 <- as.matrix(fixef(plm05))

fix06 <- as.matrix(fixef(plm06))

fix07 <- as.matrix(fixef(plm07))

fix08 <- as.matrix(fixef(plm08))

fix09 <- as.matrix(fixef(plm09))

fix10 <- as.matrix(fixef(plm10))

fix11 <- as.matrix(fixef(plm11))

fix12 <- as.matrix(fixef(plm12))

fix13 <- as.matrix(fixef(plm13))

fix14 <- as.matrix(fixef(plm14))

fix15 <- as.matrix(fixef(plm15))

fix16 <- as.matrix(fixef(plm16))

fix17 <- as.matrix(fixef(plm17))

fix18 <- as.matrix(fixef(plm18))

fix19 <- as.matrix(fixef(plm19))

c01 <- as.matrix(coef(plm01))

c02 <- as.matrix(coef(plm02))

c03 <- as.matrix(coef(plm03))

c04 <- as.matrix(coef(plm04))

c05 <- as.matrix(coef(plm05))

c06 <- as.matrix(coef(plm06))

c07 <- as.matrix(coef(plm07))

c08 <- as.matrix(coef(plm08))

c09 <- as.matrix(coef(plm09))

c10 <- as.matrix(coef(plm10))

c11 <- as.matrix(coef(plm11))

c12 <- as.matrix(coef(plm12))

c13 <- as.matrix(coef(plm13))

c14 <- as.matrix(coef(plm14))

c15 <- as.matrix(coef(plm15))

c16 <- as.matrix(coef(plm16))

c17 <- as.matrix(coef(plm17))

c18 <- as.matrix(coef(plm18))

c19 <- as.matrix(coef(plm19))

y01.predict <- monmlp.predict(x = x01.predict, weights = mlp01)

y02.predict <- monmlp.predict(x = x02.predict, weights = mlp02)

y03.predict <- monmlp.predict(x = x03.predict, weights = mlp03)

y04.predict <- monmlp.predict(x = x04.predict, weights = mlp04)

y05.predict <- monmlp.predict(x = x05.predict, weights = mlp05)

y06.predict <- monmlp.predict(x = x06.predict, weights = mlp06)

y07.predict <- monmlp.predict(x = x07.predict, weights = mlp07)

y08.predict <- monmlp.predict(x = x08.predict, weights = mlp08)

y09.predict <- monmlp.predict(x = x09.predict, weights = mlp09)

y10.predict <- monmlp.predict(x = x10.predict, weights = mlp10)

y11.predict <- monmlp.predict(x = x11.predict, weights = mlp11)

y12.predict <- monmlp.predict(x = x12.predict, weights = mlp12)

y13.predict <- monmlp.predict(x = x13.predict, weights = mlp13)

y14.predict <- monmlp.predict(x = x14.predict, weights = mlp14)

y15.predict <- monmlp.predict(x = x15.predict, weights = mlp15)

y16.predict <- monmlp.predict(x = x16.predict, weights = mlp16)

y17.predict <- monmlp.predict(x = x17.predict, weights = mlp17)

y18.predict <- monmlp.predict(x = x18.predict, weights = mlp18)

y19.predict <- monmlp.predict(x = x19.predict, weights = mlp19)

y20.predict <- monmlp.predict(x = x20.predict, weights = mlp20)

y21.predict <- monmlp.predict(x = x21.predict, weights = mlp21)

y22.predict <- monmlp.predict(x = x22.predict, weights = mlp22)

y23.predict <- monmlp.predict(x = x23.predict, weights = mlp23)

y24.predict <- monmlp.predict(x = x24.predict, weights = mlp24)

y25.predict <- monmlp.predict(x = x25.predict, weights = mlp25)

y26.predict <- monmlp.predict(x = x26.predict, weights = mlp26)

y27.predict <- monmlp.predict(x = x27.predict, weights = mlp27)

y28.predict <- monmlp.predict(x = x28.predict, weights = mlp28)

y29.predict <- monmlp.predict(x = x29.predict, weights = mlp29)

y30.predict <- monmlp.predict(x = x30.predict, weights = mlp30)

y31.predict <- monmlp.predict(x = x31.predict, weights = mlp31)

y32.predict <- monmlp.predict(x = x32.predict, weights = mlp32)

y33.predict <- monmlp.predict(x = x33.predict, weights = mlp33)

y34.predict <- monmlp.predict(x = x34.predict, weights = mlp34)

y35.predict <- monmlp.predict(x = x35.predict, weights = mlp35)

y36.predict <- monmlp.predict(x = x36.predict, weights = mlp36)

y37.predict <- monmlp.predict(x = x37.predict, weights = mlp37)

y38.predict <- monmlp.predict(x = x38.predict, weights = mlp38)

y39.predict <- monmlp.predict(x = x39.predict, weights = mlp39)

y40.predict <- monmlp.predict(x = x40.predict, weights = mlp40)

y41.predict <- monmlp.predict(x = x41.predict, weights = mlp41)

y42.predict <- monmlp.predict(x = x42.predict, weights = mlp42)

y43.predict <- monmlp.predict(x = x43.predict, weights = mlp43)

y44.predict <- monmlp.predict(x = x44.predict, weights = mlp44)

y45.predict <- monmlp.predict(x = x45.predict, weights = mlp45)

y46.predict <- monmlp.predict(x = x46.predict, weights = mlp46)

y47.predict <- monmlp.predict(x = x47.predict, weights = mlp47)

y48.predict <- monmlp.predict(x = x48.predict, weights = mlp48)

y49.predict <- monmlp.predict(x = x49.predict, weights = mlp49)

y50.predict <- monmlp.predict(x = x50.predict, weights = mlp50)

y51.predict <- monmlp.predict(x = x51.predict, weights = mlp51)

y52.predict <- monmlp.predict(x = x52.predict, weights = mlp52)

y53.predict <- monmlp.predict(x = x53.predict, weights = mlp53)

y54.predict <- monmlp.predict(x = x54.predict, weights = mlp54)

y55.predict <- monmlp.predict(x = x55.predict, weights = mlp55)

y56.predict <- monmlp.predict(x = x56.predict, weights = mlp56)

y57.predict <- monmlp.predict(x = x57.predict, weights = mlp57)

y58.predict <- monmlp.predict(x = x58.predict, weights = mlp58)

y59.predict <- monmlp.predict(x = x59.predict, weights = mlp59)

y60.predict <- monmlp.predict(x = x60.predict, weights = mlp60)

y61.predict <- monmlp.predict(x = x61.predict, weights = mlp61)

y62.predict <- monmlp.predict(x = x62.predict, weights = mlp62)

y63.predict <- monmlp.predict(x = x63.predict, weights = mlp63)

y64.predict <- monmlp.predict(x = x64.predict, weights = mlp64)

y65.predict <- monmlp.predict(x = x65.predict, weights = mlp65)

y66.predict <- monmlp.predict(x = x66.predict, weights = mlp66)

y67.predict <- monmlp.predict(x = x67.predict, weights = mlp67)

y68.predict <- monmlp.predict(x = x68.predict, weights = mlp68)

y69.predict <- monmlp.predict(x = x69.predict, weights = mlp69)

y70.predict <- monmlp.predict(x = x70.predict, weights = mlp70)

y71.predict <- monmlp.predict(x = x71.predict, weights = mlp71)

y72.predict <- monmlp.predict(x = x72.predict, weights = mlp72)

y73.predict <- monmlp.predict(x = x73.predict, weights = mlp73)

y74.predict <- monmlp.predict(x = x74.predict, weights = mlp74)

y75.predict <- monmlp.predict(x = x75.predict, weights = mlp75)

y76.predict <- monmlp.predict(x = x76.predict, weights = mlp76)

y101.predict <- fix01 + x101.predict %*% c01

y102.predict <- fix02 + x102.predict %*% c02

y103.predict <- fix03 + x103.predict %*% c03

y104.predict <- fix04 + x104.predict %*% c04

y105.predict <- fix05 + x105.predict %*% c05

y106.predict <- fix06 + x106.predict %*% c06

y107.predict <- fix07 + x107.predict %*% c07

y108.predict <- fix08 + x108.predict %*% c08

y109.predict <- fix09 + x109.predict %*% c09

y110.predict <- fix10 + x110.predict %*% c10

y111.predict <- fix11 + x111.predict %*% c11

y112.predict <- fix12 + x112.predict %*% c12

y113.predict <- fix13 + x113.predict %*% c13

y114.predict <- fix14 + x114.predict %*% c14

y115.predict <- fix15 + x115.predict %*% c15

y116.predict <- fix16 + x116.predict %*% c16

y117.predict <- fix17 + x117.predict %*% c17

y118.predict <- fix18 + x118.predict %*% c18

y119.predict <- fix19 + x119.predict %*% c19

gdp01.predict <- gdp[TESTset2]*(1+y01.predict/100)

gdp02.predict <- gdp[TESTset2]*(1+y02.predict/100)

gdp03.predict <- gdp[TESTset2]*(1+y03.predict/100)

gdp04.predict <- gdp[TESTset2]*(1+y04.predict/100)

gdp05.predict <- gdp[TESTset2]*(1+y05.predict/100)

gdp06.predict <- gdp[TESTset2]*(1+y06.predict/100)

gdp07.predict <- gdp[TESTset2]*(1+y07.predict/100)

gdp08.predict <- gdp[TESTset2]*(1+y08.predict/100)

gdp09.predict <- gdp[TESTset2]*(1+y09.predict/100)

gdp10.predict <- gdp[TESTset2]*(1+y10.predict/100)

gdp11.predict <- gdp[TESTset2]*(1+y11.predict/100)

gdp12.predict <- gdp[TESTset2]*(1+y12.predict/100)

gdp13.predict <- gdp[TESTset2]*(1+y13.predict/100)

gdp14.predict <- gdp[TESTset2]*(1+y14.predict/100)

gdp15.predict <- gdp[TESTset2]*(1+y15.predict/100)

gdp16.predict <- gdp[TESTset2]*(1+y16.predict/100)

gdp17.predict <- gdp[TESTset2]*(1+y17.predict/100)

gdp18.predict <- gdp[TESTset2]*(1+y18.predict/100)

gdp19.predict <- gdp[TESTset2]*(1+y19.predict/100)

gdp20.predict <- gdp[TESTset2]*(1+y20.predict/100)

gdp21.predict <- gdp[TESTset2]*(1+y21.predict/100)

gdp22.predict <- gdp[TESTset2]*(1+y22.predict/100)

gdp23.predict <- gdp[TESTset2]*(1+y23.predict/100)

gdp24.predict <- gdp[TESTset2]*(1+y24.predict/100)

gdp25.predict <- gdp[TESTset2]*(1+y25.predict/100)

gdp26.predict <- gdp[TESTset2]*(1+y26.predict/100)

gdp27.predict <- gdp[TESTset2]*(1+y27.predict/100)

gdp28.predict <- gdp[TESTset2]*(1+y28.predict/100)

gdp29.predict <- gdp[TESTset2]*(1+y29.predict/100)

gdp30.predict <- gdp[TESTset2]*(1+y30.predict/100)

gdp31.predict <- gdp[TESTset2]*(1+y31.predict/100)

gdp32.predict <- gdp[TESTset2]*(1+y32.predict/100)

gdp33.predict <- gdp[TESTset2]*(1+y33.predict/100)

gdp34.predict <- gdp[TESTset2]*(1+y34.predict/100)

gdp35.predict <- gdp[TESTset2]*(1+y35.predict/100)

gdp36.predict <- gdp[TESTset2]*(1+y36.predict/100)

gdp37.predict <- gdp[TESTset2]*(1+y37.predict/100)

gdp38.predict <- gdp[TESTset2]*(1+y38.predict/100)

gdp39.predict <- gdp[TESTset2]*(1+y39.predict/100)

gdp40.predict <- gdp[TESTset2]*(1+y40.predict/100)

gdp41.predict <- gdp[TESTset2]*(1+y41.predict/100)

gdp42.predict <- gdp[TESTset2]*(1+y42.predict/100)

gdp43.predict <- gdp[TESTset2]*(1+y43.predict/100)

gdp44.predict <- gdp[TESTset2]*(1+y44.predict/100)

gdp45.predict <- gdp[TESTset2]*(1+y45.predict/100)

gdp46.predict <- gdp[TESTset2]*(1+y46.predict/100)

gdp47.predict <- gdp[TESTset2]*(1+y47.predict/100)

gdp48.predict <- gdp[TESTset2]*(1+y48.predict/100)

gdp49.predict <- gdp[TESTset2]*(1+y49.predict/100)

gdp50.predict <- gdp[TESTset2]*(1+y50.predict/100)

gdp51.predict <- gdp[TESTset2]*(1+y51.predict/100)

gdp52.predict <- gdp[TESTset2]*(1+y52.predict/100)

gdp53.predict <- gdp[TESTset2]*(1+y53.predict/100)

gdp54.predict <- gdp[TESTset2]*(1+y54.predict/100)

gdp55.predict <- gdp[TESTset2]*(1+y55.predict/100)

gdp56.predict <- gdp[TESTset2]*(1+y56.predict/100)

gdp57.predict <- gdp[TESTset2]*(1+y57.predict/100)

gdp58.predict <- gdp[TESTset2]*(1+y58.predict/100)

gdp59.predict <- gdp[TESTset2]*(1+y59.predict/100)

gdp60.predict <- gdp[TESTset2]*(1+y60.predict/100)

gdp61.predict <- gdp[TESTset2]*(1+y61.predict/100)

gdp62.predict <- gdp[TESTset2]*(1+y62.predict/100)

gdp63.predict <- gdp[TESTset2]*(1+y63.predict/100)

gdp64.predict <- gdp[TESTset2]*(1+y64.predict/100)

gdp65.predict <- gdp[TESTset2]*(1+y65.predict/100)

gdp66.predict <- gdp[TESTset2]*(1+y66.predict/100)

gdp67.predict <- gdp[TESTset2]*(1+y67.predict/100)

gdp68.predict <- gdp[TESTset2]*(1+y68.predict/100)

gdp69.predict <- gdp[TESTset2]*(1+y69.predict/100)

gdp70.predict <- gdp[TESTset2]*(1+y70.predict/100)

gdp71.predict <- gdp[TESTset2]*(1+y71.predict/100)

gdp72.predict <- gdp[TESTset2]*(1+y72.predict/100)

gdp73.predict <- gdp[TESTset2]*(1+y73.predict/100)

gdp74.predict <- gdp[TESTset2]*(1+y74.predict/100)

gdp75.predict <- gdp[TESTset2]*(1+y75.predict/100)

gdp76.predict <- gdp[TESTset2]*(1+y76.predict/100)

gdp101.predict <- gdp[TESTset2]*(1+y101.predict/100)

gdp102.predict <- gdp[TESTset2]*(1+y102.predict/100)

gdp103.predict <- gdp[TESTset2]*(1+y103.predict/100)

gdp104.predict <- gdp[TESTset2]*(1+y104.predict/100)

gdp105.predict <- gdp[TESTset2]*(1+y105.predict/100)

gdp106.predict <- gdp[TESTset2]*(1+y106.predict/100)

gdp107.predict <- gdp[TESTset2]*(1+y107.predict/100)

gdp108.predict <- gdp[TESTset2]*(1+y108.predict/100)

gdp109.predict <- gdp[TESTset2]*(1+y109.predict/100)

gdp110.predict <- gdp[TESTset2]*(1+y110.predict/100)

gdp111.predict <- gdp[TESTset2]*(1+y111.predict/100)

gdp112.predict <- gdp[TESTset2]*(1+y112.predict/100)

gdp113.predict <- gdp[TESTset2]*(1+y113.predict/100)

gdp114.predict <- gdp[TESTset2]*(1+y114.predict/100)

gdp115.predict <- gdp[TESTset2]*(1+y115.predict/100)

gdp116.predict <- gdp[TESTset2]*(1+y116.predict/100)

gdp117.predict <- gdp[TESTset2]*(1+y117.predict/100)

gdp118.predict <- gdp[TESTset2]*(1+y118.predict/100)

gdp119.predict <- gdp[TESTset2]*(1+y119.predict/100)

gdp.01[TESTset] <- gdp01.predict

gdp.02[TESTset] <- gdp02.predict

gdp.03[TESTset] <- gdp03.predict

gdp.04[TESTset] <- gdp04.predict

gdp.05[TESTset] <- gdp05.predict

gdp.06[TESTset] <- gdp06.predict

gdp.07[TESTset] <- gdp07.predict

gdp.08[TESTset] <- gdp08.predict

gdp.09[TESTset] <- gdp09.predict

gdp.10[TESTset] <- gdp10.predict

gdp.11[TESTset] <- gdp11.predict

gdp.12[TESTset] <- gdp12.predict

gdp.13[TESTset] <- gdp13.predict

gdp.14[TESTset] <- gdp14.predict

gdp.15[TESTset] <- gdp15.predict

gdp.16[TESTset] <- gdp16.predict

gdp.17[TESTset] <- gdp17.predict

gdp.18[TESTset] <- gdp18.predict

gdp.19[TESTset] <- gdp19.predict

gdp.20[TESTset] <- gdp20.predict

gdp.21[TESTset] <- gdp21.predict

gdp.22[TESTset] <- gdp22.predict

gdp.23[TESTset] <- gdp23.predict

gdp.24[TESTset] <- gdp24.predict

gdp.25[TESTset] <- gdp25.predict

gdp.26[TESTset] <- gdp26.predict

gdp.27[TESTset] <- gdp27.predict

gdp.28[TESTset] <- gdp28.predict

gdp.29[TESTset] <- gdp29.predict

gdp.30[TESTset] <- gdp30.predict

gdp.31[TESTset] <- gdp31.predict

gdp.32[TESTset] <- gdp32.predict

gdp.33[TESTset] <- gdp33.predict

gdp.34[TESTset] <- gdp34.predict

gdp.35[TESTset] <- gdp35.predict

gdp.36[TESTset] <- gdp36.predict

gdp.37[TESTset] <- gdp37.predict

gdp.38[TESTset] <- gdp38.predict

gdp.39[TESTset] <- gdp39.predict

gdp.40[TESTset] <- gdp40.predict

gdp.41[TESTset] <- gdp41.predict

gdp.42[TESTset] <- gdp42.predict

gdp.43[TESTset] <- gdp43.predict

gdp.44[TESTset] <- gdp44.predict

gdp.45[TESTset] <- gdp45.predict

gdp.46[TESTset] <- gdp46.predict

gdp.47[TESTset] <- gdp47.predict

gdp.48[TESTset] <- gdp48.predict

gdp.49[TESTset] <- gdp49.predict

gdp.50[TESTset] <- gdp50.predict

gdp.51[TESTset] <- gdp51.predict

gdp.52[TESTset] <- gdp52.predict

gdp.53[TESTset] <- gdp53.predict

gdp.54[TESTset] <- gdp54.predict

gdp.55[TESTset] <- gdp55.predict

gdp.56[TESTset] <- gdp56.predict

gdp.57[TESTset] <- gdp57.predict

gdp.58[TESTset] <- gdp58.predict

gdp.59[TESTset] <- gdp59.predict

gdp.60[TESTset] <- gdp60.predict

gdp.61[TESTset] <- gdp61.predict

gdp.62[TESTset] <- gdp62.predict

gdp.63[TESTset] <- gdp63.predict

gdp.64[TESTset] <- gdp64.predict

gdp.65[TESTset] <- gdp65.predict

gdp.66[TESTset] <- gdp66.predict

gdp.67[TESTset] <- gdp67.predict

gdp.68[TESTset] <- gdp68.predict

gdp.69[TESTset] <- gdp69.predict

gdp.70[TESTset] <- gdp70.predict

gdp.71[TESTset] <- gdp71.predict

gdp.72[TESTset] <- gdp72.predict

gdp.73[TESTset] <- gdp73.predict

gdp.74[TESTset] <- gdp74.predict

gdp.75[TESTset] <- gdp75.predict

gdp.76[TESTset] <- gdp76.predict

gdp.101[TESTset] <- gdp101.predict

gdp.102[TESTset] <- gdp102.predict

gdp.103[TESTset] <- gdp103.predict

gdp.104[TESTset] <- gdp104.predict

gdp.105[TESTset] <- gdp105.predict

gdp.106[TESTset] <- gdp106.predict

gdp.107[TESTset] <- gdp107.predict

gdp.108[TESTset] <- gdp108.predict

gdp.109[TESTset] <- gdp109.predict

gdp.110[TESTset] <- gdp110.predict

gdp.111[TESTset] <- gdp111.predict

gdp.112[TESTset] <- gdp112.predict

gdp.113[TESTset] <- gdp113.predict

gdp.114[TESTset] <- gdp114.predict

gdp.115[TESTset] <- gdp115.predict

gdp.116[TESTset] <- gdp116.predict

gdp.117[TESTset] <- gdp117.predict

gdp.118[TESTset] <- gdp118.predict

gdp.119[TESTset] <- gdp119.predict

MAPPE[1,(j+1)] <- mean(abs(1-gdp01.predict/gdp.actual))

MAPPE[2,(j+1)] <- mean(abs(1-gdp02.predict/gdp.actual))

MAPPE[3,(j+1)] <- mean(abs(1-gdp03.predict/gdp.actual))

MAPPE[4,(j+1)] <- mean(abs(1-gdp04.predict/gdp.actual))

MAPPE[5,(j+1)] <- mean(abs(1-gdp05.predict/gdp.actual))

MAPPE[6,(j+1)] <- mean(abs(1-gdp06.predict/gdp.actual))

MAPPE[7,(j+1)] <- mean(abs(1-gdp07.predict/gdp.actual))

MAPPE[8,(j+1)] <- mean(abs(1-gdp08.predict/gdp.actual))

MAPPE[9,(j+1)] <- mean(abs(1-gdp09.predict/gdp.actual))

MAPPE[10,(j+1)] <- mean(abs(1-gdp10.predict/gdp.actual))

MAPPE[11,(j+1)] <- mean(abs(1-gdp11.predict/gdp.actual))

MAPPE[12,(j+1)] <- mean(abs(1-gdp12.predict/gdp.actual))

MAPPE[13,(j+1)] <- mean(abs(1-gdp13.predict/gdp.actual))

MAPPE[14,(j+1)] <- mean(abs(1-gdp14.predict/gdp.actual))

MAPPE[15,(j+1)] <- mean(abs(1-gdp15.predict/gdp.actual))

MAPPE[16,(j+1)] <- mean(abs(1-gdp16.predict/gdp.actual))

MAPPE[17,(j+1)] <- mean(abs(1-gdp17.predict/gdp.actual))

MAPPE[18,(j+1)] <- mean(abs(1-gdp18.predict/gdp.actual))

MAPPE[19,(j+1)] <- mean(abs(1-gdp19.predict/gdp.actual))

MAPPE[20,(j+1)] <- mean(abs(1-gdp20.predict/gdp.actual))

MAPPE[21,(j+1)] <- mean(abs(1-gdp21.predict/gdp.actual))

MAPPE[22,(j+1)] <- mean(abs(1-gdp22.predict/gdp.actual))

MAPPE[23,(j+1)] <- mean(abs(1-gdp23.predict/gdp.actual))

MAPPE[24,(j+1)] <- mean(abs(1-gdp24.predict/gdp.actual))

MAPPE[25,(j+1)] <- mean(abs(1-gdp25.predict/gdp.actual))

MAPPE[26,(j+1)] <- mean(abs(1-gdp26.predict/gdp.actual))

MAPPE[27,(j+1)] <- mean(abs(1-gdp27.predict/gdp.actual))

MAPPE[28,(j+1)] <- mean(abs(1-gdp28.predict/gdp.actual))

MAPPE[29,(j+1)] <- mean(abs(1-gdp29.predict/gdp.actual))

MAPPE[30,(j+1)] <- mean(abs(1-gdp30.predict/gdp.actual))

MAPPE[31,(j+1)] <- mean(abs(1-gdp31.predict/gdp.actual))

MAPPE[32,(j+1)] <- mean(abs(1-gdp32.predict/gdp.actual))

MAPPE[33,(j+1)] <- mean(abs(1-gdp33.predict/gdp.actual))

MAPPE[34,(j+1)] <- mean(abs(1-gdp34.predict/gdp.actual))

MAPPE[35,(j+1)] <- mean(abs(1-gdp35.predict/gdp.actual))

MAPPE[36,(j+1)] <- mean(abs(1-gdp36.predict/gdp.actual))

MAPPE[37,(j+1)] <- mean(abs(1-gdp37.predict/gdp.actual))

MAPPE[38,(j+1)] <- mean(abs(1-gdp38.predict/gdp.actual))

MAPPE[39,(j+1)] <- mean(abs(1-gdp39.predict/gdp.actual))

MAPPE[40,(j+1)] <- mean(abs(1-gdp40.predict/gdp.actual))

MAPPE[41,(j+1)] <- mean(abs(1-gdp41.predict/gdp.actual))

MAPPE[42,(j+1)] <- mean(abs(1-gdp42.predict/gdp.actual))

MAPPE[43,(j+1)] <- mean(abs(1-gdp43.predict/gdp.actual))

MAPPE[44,(j+1)] <- mean(abs(1-gdp44.predict/gdp.actual))

MAPPE[45,(j+1)] <- mean(abs(1-gdp45.predict/gdp.actual))

MAPPE[46,(j+1)] <- mean(abs(1-gdp46.predict/gdp.actual))

MAPPE[47,(j+1)] <- mean(abs(1-gdp47.predict/gdp.actual))

MAPPE[48,(j+1)] <- mean(abs(1-gdp48.predict/gdp.actual))

MAPPE[49,(j+1)] <- mean(abs(1-gdp49.predict/gdp.actual))

MAPPE[50,(j+1)] <- mean(abs(1-gdp50.predict/gdp.actual))

MAPPE[51,(j+1)] <- mean(abs(1-gdp51.predict/gdp.actual))

MAPPE[52,(j+1)] <- mean(abs(1-gdp52.predict/gdp.actual))

MAPPE[53,(j+1)] <- mean(abs(1-gdp53.predict/gdp.actual))

MAPPE[54,(j+1)] <- mean(abs(1-gdp54.predict/gdp.actual))

MAPPE[55,(j+1)] <- mean(abs(1-gdp55.predict/gdp.actual))

MAPPE[56,(j+1)] <- mean(abs(1-gdp56.predict/gdp.actual))

MAPPE[57,(j+1)] <- mean(abs(1-gdp57.predict/gdp.actual))

MAPPE[58,(j+1)] <- mean(abs(1-gdp58.predict/gdp.actual))

MAPPE[59,(j+1)] <- mean(abs(1-gdp59.predict/gdp.actual))

MAPPE[60,(j+1)] <- mean(abs(1-gdp60.predict/gdp.actual))

MAPPE[61,(j+1)] <- mean(abs(1-gdp61.predict/gdp.actual))

MAPPE[62,(j+1)] <- mean(abs(1-gdp62.predict/gdp.actual))

MAPPE[63,(j+1)] <- mean(abs(1-gdp63.predict/gdp.actual))

MAPPE[64,(j+1)] <- mean(abs(1-gdp64.predict/gdp.actual))

MAPPE[65,(j+1)] <- mean(abs(1-gdp65.predict/gdp.actual))

MAPPE[66,(j+1)] <- mean(abs(1-gdp66.predict/gdp.actual))

MAPPE[67,(j+1)] <- mean(abs(1-gdp67.predict/gdp.actual))

MAPPE[68,(j+1)] <- mean(abs(1-gdp68.predict/gdp.actual))

MAPPE[69,(j+1)] <- mean(abs(1-gdp69.predict/gdp.actual))

MAPPE[70,(j+1)] <- mean(abs(1-gdp70.predict/gdp.actual))

MAPPE[71,(j+1)] <- mean(abs(1-gdp71.predict/gdp.actual))

MAPPE[72,(j+1)] <- mean(abs(1-gdp72.predict/gdp.actual))

MAPPE[73,(j+1)] <- mean(abs(1-gdp73.predict/gdp.actual))

MAPPE[74,(j+1)] <- mean(abs(1-gdp74.predict/gdp.actual))

MAPPE[75,(j+1)] <- mean(abs(1-gdp75.predict/gdp.actual))

MAPPE[76,(j+1)] <- mean(abs(1-gdp76.predict/gdp.actual))

MAPPE[101,(j+1)] <- mean(abs(1-gdp101.predict/gdp.actual))

MAPPE[102,(j+1)] <- mean(abs(1-gdp102.predict/gdp.actual))

MAPPE[103,(j+1)] <- mean(abs(1-gdp103.predict/gdp.actual))

MAPPE[104,(j+1)] <- mean(abs(1-gdp104.predict/gdp.actual))

MAPPE[105,(j+1)] <- mean(abs(1-gdp105.predict/gdp.actual))

MAPPE[106,(j+1)] <- mean(abs(1-gdp106.predict/gdp.actual))

MAPPE[107,(j+1)] <- mean(abs(1-gdp107.predict/gdp.actual))

MAPPE[108,(j+1)] <- mean(abs(1-gdp108.predict/gdp.actual))

MAPPE[109,(j+1)] <- mean(abs(1-gdp109.predict/gdp.actual))

MAPPE[110,(j+1)] <- mean(abs(1-gdp110.predict/gdp.actual))

MAPPE[111,(j+1)] <- mean(abs(1-gdp111.predict/gdp.actual))

MAPPE[112,(j+1)] <- mean(abs(1-gdp112.predict/gdp.actual))

MAPPE[113,(j+1)] <- mean(abs(1-gdp113.predict/gdp.actual))

MAPPE[114,(j+1)] <- mean(abs(1-gdp114.predict/gdp.actual))

MAPPE[115,(j+1)] <- mean(abs(1-gdp115.predict/gdp.actual))

MAPPE[116,(j+1)] <- mean(abs(1-gdp116.predict/gdp.actual))

MAPPE[117,(j+1)] <- mean(abs(1-gdp117.predict/gdp.actual))

MAPPE[118,(j+1)] <- mean(abs(1-gdp118.predict/gdp.actual))

MAPPE[119,(j+1)] <- mean(abs(1-gdp119.predict/gdp.actual))

sPPE[1,(j+1)] <- 1-sum(gdp01.predict)/sum(gdp.actual)

sPPE[2,(j+1)] <- 1-sum(gdp02.predict)/sum(gdp.actual)

sPPE[3,(j+1)] <- 1-sum(gdp03.predict)/sum(gdp.actual)

sPPE[4,(j+1)] <- 1-sum(gdp04.predict)/sum(gdp.actual)

sPPE[5,(j+1)] <- 1-sum(gdp05.predict)/sum(gdp.actual)

sPPE[6,(j+1)] <- 1-sum(gdp06.predict)/sum(gdp.actual)

sPPE[7,(j+1)] <- 1-sum(gdp07.predict)/sum(gdp.actual)

sPPE[8,(j+1)] <- 1-sum(gdp08.predict)/sum(gdp.actual)

sPPE[9,(j+1)] <- 1-sum(gdp09.predict)/sum(gdp.actual)

sPPE[10,(j+1)] <- 1-sum(gdp10.predict)/sum(gdp.actual)

sPPE[11,(j+1)] <- 1-sum(gdp11.predict)/sum(gdp.actual)

sPPE[12,(j+1)] <- 1-sum(gdp12.predict)/sum(gdp.actual)

...