我是R编程初学者。
我需要在估算函数的两个不同参数上按以下顺序重复以下后答案Adding EGARCH flavor into a loop over fGARCH-flavor models的代码。
重申的第一个论点是错误分布。这是应该在模型估计命令中改变的向量（参见链接的帖子）：
error_distribution=c("norm","std","ged")
model_2012=ugarchspec(variance.model = list(model="fGARCH", submodel = j, garchOrder = c(1, i)),mean.model = list(armaOrder = c(0, 0)),distribution.model=z)
modelfit_2012=ugarchfit(spec=model_2012, data=bchain_2012_logreturns, solver="hybrid")
重申的第二个论点是输入数据，它由包含2012 - 2016年的对数回报的五个向量给出，如下所示：
bchain_2012_logreturns
bchain_2013_logreturns
bchain_2014_logreturns
bchain_2015_logreturns
bchain_2016_logreturns
预期结果是单个数据框架，根据各自的AIC，每年对45个GARCH模型进行排名。请参阅以下输出：
model_name error_distribution order AIC model_name error_distribution order AIC model_name error_distribution order AIC
31 eGARCH ged (1,1) -4.75865287 TGARCH ged (1,1) -3.08102463 eGARCH ged (1,1) -4.05490216
33 GARCH ged (1,1) -4.75652725 AVGARCH ged (1,1) -3.08045023 TGARCH ged (1,1) -4.05374702
43 NAGARCH ged (1,1) -4.75490561 AVGARCH std (1,1) -3.07788929 AVGARCH ged (1,1) -4.05331759
39 GJRGARCH ged (1,1) -4.75278902 TGARCH std (1,1) -3.07725803 GARCH ged (1,1) -4.05294280
41 NGARCH ged (1,1) -4.75108028 NGARCH ged (1,1) -3.07641387 NGARCH ged (1,1) -4.05211362
37 TGARCH ged (1,1) -4.74863010 APARCH ged (1,1) -3.07511457 NAGARCH ged (1,1) -4.05184210
45 APARCH ged (1,1) -4.74737629 GJRGARCH ged (1,1) -3.07409228 GJRGARCH ged (1,1) -4.05104571
35 AVGARCH ged (1,1) -4.74587528 GARCH ged (1,1) -3.07400130 APARCH ged (1,1) -4.05064538
16 eGARCH std (1,1) -4.69989618 NAGARCH ged (1,1) -3.07318299 AVGARCH std (1,1) -4.04762280
18 GARCH std (1,1) -4.69719283 APARCH std (1,1) -3.07315401 TGARCH std (1,1) -4.04601307
28 NAGARCH std (1,1) -4.69405444 eGARCH std (1,1) -3.07269706 eGARCH std (1,1) -4.04406967
26 NGARCH std (1,1) -4.69404219 NGARCH std (1,1) -3.06768570 NAGARCH std (1,1) -4.04226303
24 GJRGARCH std (1,1) -4.69226266 eGARCH ged (1,1) -3.06481282 APARCH std (1,1) -4.04141302
30 APARCH std (1,1) -4.68892186 NAGARCH std (1,1) -3.05214076 GJRGARCH std (1,1) -4.03922662
20 AVGARCH std (1,1) -4.68635636 GJRGARCH std (1,1) -3.04994480 NGARCH std (1,1) -4.03902956
22 TGARCH std (1,1) -4.68545602 GARCH std (1,1) -3.04671032 GARCH std (1,1) -4.03587846
17 GARCH std (1,0) -4.63367371 NGARCH std (1,0) -2.96723963 NGARCH std (1,0) -4.02141342
27 NAGARCH std (1,0) -4.63086175 TGARCH std (1,0) -2.96703818 APARCH std (1,0) -4.01926563
25 NGARCH std (1,0) -4.62854627 GARCH std (1,0) -2.96419867 TGARCH std (1,0) -4.01739401
23 GJRGARCH std (1,0) -4.62836367 APARCH std (1,0) -2.96180909 NAGARCH std (1,0) -4.00275498
21 TGARCH std (1,0) -4.62233634 NAGARCH std (1,0) -2.96011709 GARCH std (1,0) -4.00197866
19 AVGARCH std (1,0) -4.60732059 GJRGARCH std (1,0) -2.95925904 GJRGARCH std (1,0) -3.99942924
29 APARCH std (1,0) -4.60040491 AVGARCH std (1,0) -2.93447870 AVGARCH std (1,0) -3.97651737
1 eGARCH norm (1,1) -4.37707925 AVGARCH norm (1,1) -2.84142521 eGARCH norm (1,1) -3.89143994
5 AVGARCH norm (1,1) -4.36877118 GJRGARCH norm (1,1) -2.83307523 GARCH norm (1,1) -3.88777006
7 TGARCH norm (1,1) -4.35992439 GARCH norm (1,1) -2.83172032 NGARCH norm (1,1) -3.88227550
9 GJRGARCH norm (1,1) -4.35675975 NAGARCH norm (1,1) -2.83096966 NAGARCH norm (1,1) -3.88075879
13 NAGARCH norm (1,1) -4.35669056 NGARCH norm (1,1) -2.82936979 TGARCH norm (1,1) -3.87732087
15 APARCH norm (1,1) -4.35639445 APARCH norm (1,1) -2.82908704 APARCH norm (1,1) -3.87128476
3 GARCH norm (1,1) -4.34457950 TGARCH norm (1,1) -2.82664751 NGARCH norm (1,0) -3.86962700
11 NGARCH norm (1,1) -4.34333420 eGARCH norm (1,1) -2.79347854 GJRGARCH norm (1,1) -3.86727511
8 GJRGARCH norm (1,0) -4.09031551 NGARCH norm (1,0) -2.71997549 AVGARCH norm (1,1) -3.86704031
2 GARCH norm (1,0) -4.08846458 GARCH norm (1,0) -2.71897732 GARCH norm (1,0) -3.79428684
14 APARCH norm (1,0) -4.08213105 APARCH norm (1,0) -2.71487076 NAGARCH norm (1,0) -3.79161679
10 NGARCH norm (1,0) -4.06826972 NAGARCH norm (1,0) -0.87217161 AVGARCH norm (1,0) -3.73878504
4 AVGARCH norm (1,0) -3.97389330 AVGARCH norm (1,0) -0.30823178 APARCH norm (1,0) -3.73584478
12 NAGARCH norm (1,0) -3.95954524 TGARCH ged (1,0) 0.03351648 TGARCH ged (1,0) 0.03351648
6 TGARCH norm (1,0) -1.08711502 AVGARCH ged (1,0) 0.03901099 AVGARCH ged (1,0) 0.03901099
36 TGARCH ged (1,0) 0.03342466 APARCH ged (1,0) 0.03901099 APARCH ged (1,0) 0.03901099
34 AVGARCH ged (1,0) 0.03890411 GJRGARCH norm (1,0) 1.05494307 GJRGARCH norm (1,0) 3.06619678
44 APARCH ged (1,0) 0.03890411 TGARCH norm (1,0) 1.66969764 TGARCH norm (1,0) 13.23452126
42 NAGARCH ged (1,0) 8.03028586 NAGARCH ged (1,0) 30.06384244 GJRGARCH ged (1,0) 32.95929809
32 GARCH ged (1,0) 8.13606127 GARCH ged (1,0) 30.29489274 NAGARCH ged (1,0) 33.19740991
40 NGARCH ged (1,0) 8.14154072 NGARCH ged (1,0) 30.30038725 GARCH ged (1,0) 33.20214247
38 GJRGARCH ged (1,0) 8.16366093 GJRGARCH ged (1,0) 30.34334891 NGARCH ged (1,0) 33.20763698
model_name error_distribution order AIC model_name error_distribution order AIC
31 APARCH ged (1,1) -4.43376898 GARCH ged (1,1) -5.23989609
33 NGARCH ged (1,1) -4.43125597 NAGARCH ged (1,1) -5.23846855
43 GARCH ged (1,1) -4.43024900 GJRGARCH ged (1,1) -5.23694535
39 GJRGARCH ged (1,1) -4.42919245 NGARCH ged (1,1) -5.23441634
41 NAGARCH std (1,1) -4.42526122 APARCH ged (1,1) -5.23278501
37 AVGARCH ged (1,1) -4.41838285 eGARCH std (1,1) -5.23184947
45 eGARCH ged (1,1) -4.41674667 AVGARCH std (1,1) -5.23141871
35 GJRGARCH std (1,1) -4.41582574 NGARCH std (1,1) -5.23124651
16 APARCH std (1,1) -4.41051921 eGARCH ged (1,1) -5.23097095
18 GARCH std (1,1) -4.40684699 NAGARCH std (1,1) -5.22847769
28 eGARCH std (1,1) -4.40379536 TGARCH ged (1,1) -5.22758361
26 NGARCH std (1,1) -4.40146116 GARCH std (1,1) -5.22727192
24 TGARCH ged (1,1) -4.40045970 TGARCH std (1,1) -5.22670653
30 TGARCH std (1,1) -4.39302792 GJRGARCH std (1,1) -5.22562117
20 AVGARCH std (1,1) -4.38983868 AVGARCH ged (1,1) -5.22542480
22 GARCH std (1,0) -4.38731276 AVGARCH std (1,0) -5.21402359
17 NAGARCH std (1,0) -4.38699935 NAGARCH std (1,0) -5.20394522
27 NAGARCH ged (1,1) -4.38672794 APARCH std (1,1) -5.19630862
25 GJRGARCH std (1,0) -4.38216336 GARCH std (1,0) -5.18779640
23 NGARCH std (1,0) -4.38182166 NGARCH std (1,0) -5.18468472
21 AVGARCH std (1,0) -4.37746757 GJRGARCH std (1,0) -5.18242298
19 APARCH std (1,0) -4.37668704 APARCH std (1,0) -5.17696075
29 TGARCH std (1,0) -4.36889276 TGARCH std (1,0) -5.16984529
1 APARCH norm (1,1) -4.22868663 GARCH norm (1,1) -4.86762096
5 NGARCH norm (1,1) -4.22662065 GJRGARCH norm (1,1) -4.86355228
7 GARCH norm (1,1) -4.18924653 NGARCH norm (1,1) -4.86339583
9 GJRGARCH norm (1,1) -4.18544590 NAGARCH norm (1,1) -4.86270926
13 NAGARCH norm (1,1) -4.18480391 eGARCH norm (1,1) -4.85324513
15 eGARCH norm (1,1) -4.16017862 AVGARCH norm (1,1) -4.84896561
3 NGARCH norm (1,0) -4.13398631 TGARCH norm (1,1) -4.84311948
11 AVGARCH norm (1,1) -4.12548973 NAGARCH norm (1,0) -4.76807819
8 APARCH norm (1,0) -4.12331541 APARCH norm (1,1) -4.75358590
2 NAGARCH norm (1,0) -4.11927591 GARCH norm (1,0) -4.73585352
14 TGARCH norm (1,1) -4.09717462 NGARCH norm (1,0) -4.72947512
10 GJRGARCH norm (1,0) -0.03847664 APARCH norm (1,0) -4.72645043
4 TGARCH ged (1,0) 0.03351648 AVGARCH norm (1,0) -3.71073192
12 AVGARCH ged (1,0) 0.03901099 GJRGARCH norm (1,0) -0.85666042
6 APARCH ged (1,0) 0.03901099 TGARCH norm (1,0) -0.40795391
36 GARCH norm (1,0) 5.28913383 TGARCH ged (1,0) 0.03342466
34 TGARCH norm (1,0) 22.84463294 AVGARCH ged (1,0) 0.03890411
44 GJRGARCH ged (1,0) 31.04458936 APARCH ged (1,0) 0.03890411
42 GARCH ged (1,0) 31.04836775 GARCH ged (1,0) 15.92143895
32 NAGARCH ged (1,0) 31.04957524 NGARCH ged (1,0) 15.92691840
40 NGARCH ged (1,0) 31.05386225 GJRGARCH ged (1,0) 16.00220144
38 AVGARCH norm (1,0) 128.26641562 NAGARCH ged (1,0) 16.00801808
实现预期结果的一种天真方法是在每次迭代时手动更改两个必需参数，对五个AIC数据帧进行排序，最后将它们连接成一个数据帧。
实现预期结果的更好方法可能是使用两个＆＃34; for＆＃34;在继续订购和连接AIC数据帧之前进行循环。但是，我无法将这两个循环插入到lapply函数中以产生预期结果。那么，为此目的的任何建议？你能指出任何其他更智能的解决方案给R初学者吗？
答案 0 :(得分：1)
使用expand.grid，我们可以创建诸如（“bchain_2012_logreturns”，“norm”），（“bchain_2012_logreturns”，“std”）等的组合。
对于每个组合，我们调用自定义函数来计算所有模型的AIC。此功能是我之前解决方案的修改版本。
由于缺乏数据，我无法验证结果，但这应该有效。
创建组合：
error_distribution=c("norm","std","ged")
dataList = c("bchain_2012_logreturns","bchain_2013_logreturns","bchain_2014_logreturns","bchain_2015_logreturns","bchain_2016_logreturns")
paramCombinations = expand.grid(dataList,error_distribution)
paramCombinations
# Var1 Var2
#1 bchain_2012_logreturns norm
#2 bchain_2013_logreturns norm
#3 bchain_2014_logreturns norm
#4 bchain_2015_logreturns norm
#5 bchain_2016_logreturns norm
#6 bchain_2012_logreturns std
AIC计算：
fn_AIC_Calc = function(yearlyReturns = "bchain_2012_logreturns" ,errorDistrib = "norm" ) {
cat(paste0("Begin AIC calc with data =",deparse(substitute(yearlyReturns)),":","errorDistrib=",deparse(substitute(errorDistrib)),"\n"))
yearlyReturns = eval(parse(text="yearlyReturns"))
AIC_DF = do.call(rbind,lapply(model_names,function( x ) {
if ( x=="eGARCH" ){
model_year_0 = ugarchspec(variance.model = list(model= x,garchOrder = c(1, 1)),mean.model = list(armaOrder = c(0, 0)),distribution.model = errorDistrib)
modelfit_year_0 =ugarchfit(spec=model_year_0 , data = yearlyReturns, solver="hybrid")
DF = data.frame(model_name = x,error_distribution = errorDistrib ,order= "1,1" ,AIC = infocriteria(modelfit_year_0)[1])
}else {
model_year_0 = ugarchspec(variance.model = list(model="fGARCH", submodel = x, garchOrder = c(1, 0)),mean.model = list(armaOrder = c(0, 0)),,distribution.model = errorDistrib)
model_year_1 = ugarchspec(variance.model = list(model="fGARCH", submodel = x, garchOrder = c(1, 1)),mean.model = list(armaOrder = c(0, 0)),,distribution.model = errorDistrib)
modelfit_year_0 =ugarchfit(spec=model_year_0 , data = yearlyReturns, solver="hybrid")
modelfit_year_1 =ugarchfit(spec=model_year_1, data = yearlyReturns, solver="hybrid")
DF_0 = data.frame(model_name = x,error_distribution = errorDistrib, order= "1,0" , AIC = infocriteria(modelfit_year_0)[1])
DF_1 = data.frame(model_name = x,error_distribution = errorDistrib, order= "1,1" , AIC = infocriteria(modelfit_year_1)[1])
DF = rbind(DF_0,DF_1)
}
return(DF)
}))
cat(paste0("End AIC calc with data =",deparse(substitute(yearlyReturns)),":","errorDistrib=",deparse(substitute(errorDistrib)),"\n"))
}
通话功能：
AIC_Aggregate = do.call(rbind,lapply(1:nrow(paramCombinations),function(x) {
#For each combination of yearly returns dataset and error distribution compute AIC for all models
yearErrorDistribDF = fn_AIC_Calc(yearlyReturns = as.character(paramCombinations[x,"Var1"]) ,errorDistrib = as.character(paramCombinations[x,"Var2"]))
return(yearErrorDistribDF)
}))