R rugarch：$运算符对原子向量无效？

Question

我正在尝试制作一个巨大的嵌套 for 循环（优化留待以后使用）以适应 rugarch.

中可用的所有 GARCH 模型

这是我重现错误的 MWE：

library(rugarch)

## Small parameter space to search over   
AR_terms = c(0,1,2)
I_terms = c(0,1)
MA_terms = c(0,1,2)

garch_p_terms = c(0,1,2)
garch_q_terms = c(0,1,2)

## Models to search over
var_models = c("sGARCH","eGARCH","gjrGARCH","apARCH","iGARCH","csGARCH")

for (x in var_models) {

    if (x == 'fGARCH') {

        for (y in sub_var_models) {

            for (AR in AR_terms) {
                for (MA in MA_terms) {
                    for (I in I_terms) {
                        for (p in garch_p_terms) {
                            for (q in garch_q_terms) {

                                cat(y)

                                spec = spec_creator('fGARCH', y, MA, AR, I, p, q)
                                garch = ugarchfit(spec = spec, data = apple['A'], solver = 'hybrid', solver.control = list(trace=0))

                                cat('Fit Success')

                            }
                        }
                    }
                }
            }

        }

    next ## To skip evaluating fGARCH as its own model with not submodel below.

    }    

    for (AR in AR_terms) {
        for (MA in MA_terms) {
            for (I in I_terms) {
                 for (p in garch_p_terms) {
                    for (q in garch_q_terms) {

                        cat(x)

                        spec = spec_creator(x, 'null', MA, AR, I, p, q)
                        garch = ugarchfit(spec = spec, data = apple['A'], solver = 'hybrid', solver.control = list(trace=0))

                        cat('Fit Success')    

                    }
                }
            }
        }
    }


}

)

这里定义了我的 spec_creator 函数：（fGARCH 模型允许子模型族，这是大部分冗余代码的原因）

## Function to create the specs, purely to make the for loop area more readable.
spec_creator = function(model, sub_model, AR_term, I_term, MA_term, garch_p_term, garch_q_term) {

    require(rugarch)

    if (sub_model == 'null') {   
        spec = ugarchspec(variance.model = list(model = model, 
                                        garchOrder = c(garch_p_term, garch_q_term), 
                                        submodel = NULL, 
                                        external.regressors = NULL, 
                                        variance.targeting = FALSE), 

                          mean.model = list(armaOrder = c(AR_term, I_term, MA_term)))
    }

    else {
        spec = ugarchspec(variance.model = list(model = 'fGARCH', 
                                        garchOrder = c(garch_p_term, garch_q_term), 
                                        submodel = sub_model, 
                                        external.regressors = sub_model, 
                                        variance.targeting = FALSE), 

                          mean.model = list(armaOrder = c(AR_term, I_term, MA_term)))
    }

}

当我运行以上操作时，我收到许多 sGARCH 模型的成功消息，但最终收到此错误：Error: $ operator is invalid for atomic vectors，回溯指向 ugarchfit()和一个 hessian() 函数。

我假设这是某种收敛问题，但不知道是哪种。

编辑：这是我的数据（尽管其他数据集也有同样的错误），

    A
    28.57223993
    28.30616607
    28.2447644
    28.29934366
    28.39485735
    28.80420177
    29.29541506
    29.42504079
    29.31588228
    29.51373208
    30.25737443
    28.94747231
    28.85195861
    28.72915529
    29.17943414
    29.12485489
    29.04298601
    28.96111712
    27.95822332
    28.5381279
    28.68822085
    28.12878349
    27.96504572
    29.32952709
    30.31877609
    30.1345711
    29.629713
    30.01859019
    30.71447569
    30.55756033
    29.09756526
    29.72522669
    29.96401093
    29.96401093
    28.98840675
    27.59663575
    28.07420423
    28.89971546
    28.70868807
    27.75355111
    28.28569885
    29.21354618
    31.89475207
    31.29438027
    31.36260434
    31.41718359

Answer 1

实际上错误出现在极少数模型之后。之后许多其他模型也抛出相同的错误。

这是也不是收敛问题。使用 trace = 1 你可以看到在那种情况下 hybrid 方法从 solnp 到 nlminb 再到 gosolnp 并且显然 gosolnp 也是无法获得解决方案，它无法无错退出。下一个求解器将是 nloptr，实际上工作正常。

就gosolnp而言，我们有

Trying gosolnp solver...
Calculating Random Initialization Parameters...ok!
Excluding Inequality Violations...
...Excluded 500/500 Random Sequences
Evaluating Objective Function with Random Sampled Parameters...ok!
Sorting and Choosing Best Candidates for starting Solver...ok!
Starting Parameters and Starting Objective Function:
     [,1]
par1   NA
par2   NA
par3   NA
objf   NA

表示所有500组随机初始参数都不满足不等式约束。由于其他一切似乎都工作正常，我怀疑那些初始参数非常不适合 GARCH。尝试多达 50000 组参数无济于事。您可能会尝试通过 solver.control 传递 gosolnp 的 distr，但这不是很好，因为其他模型也会出现同样的问题（因此，可能很难选择一组好的分布对于每种情况）。

所以，我们可以做的是仍然使用 hybrid 但要查找错误，如果有错误，则使用 nloptr:

spec <- spec_creator(x, 'null', MA, AR, I, p, q)
garch <- tryCatch(ugarchfit(spec = spec, data = apple['A'],
                            solver = 'hybrid', solver.control = list(trace = 0)),
                  error = function(e) e)
if(inherits(garch, "error")) {
  garch <- ugarchfit(spec = spec, data = apple['A'],
                     solver = 'nloptr', solver.control = list(trace = 0))
}

我没有用这个完成运行你的代码，但是 10 多分钟没问题。

R rugarch：$运算符对原子向量无效？

R rugarch: $ operator invalid for atomic vectors?

r

mathematical-optimization

convergence