在R中重現OxMetrics的ARFIMA模型

Question

我正在努力從R中重新生成OxMetrics的一些結果（Ox Professional version 7.10），但我很難弄清楚我是否正確地在R中獲得了正確的規範。我並不期望得到相同的結果估計，但應該有可能進行類似的估計（見OxMetrics和R的估計）。

這裏的任何人都可以幫助我搞清楚我如何做OxMetrics在R中做什麼？

我試過使用forecast::arfima,forecast::Arima,fracdiff::fracdiff和arfima::arfima到目前爲止，我與後者最接近。

下面是數據和代碼，

吹塑結果是從距離估計OxMetrics ARFIMA（2,0,2）模型使用利用arfima從arfima包最大似然和從R（代碼吹較長數據串）。

  OxMetrics   R (using arfima()] 
AR   1.41763   1.78547  
AR  -0.51606   -0.79782 
MA  -0.89892   -0.08406 
MA   0.30821   0.48083 
Constant -0.09382   -0.09423 

y <- c(-0.0527281830620101, -0.0483283435220523, 
-0.0761110069836706, -0.0425588714546148, 
-0.0629789511239869, -0.118944956578757, 
-0.156545103342326, -0.138106089421937, 
-0.107335059908618, -0.145013381825552, 
-0.100753517322066, -0.0987268545186417, 
-0.0454663306471916, -0.0404439816954447, 
-0.110574863632305, -0.0933955365797221, 
-0.0915045759209185, -0.110397691370645, 
-0.0944201704700927, -0.121257467376357, 
-0.109785472344257, -0.0890776818684245, 
-0.0554059943242384, -0.0700566531543618, 
-0.0366694695635905, -0.0687369752462432, 
-0.0651380598746858, -0.134224646388692, 
-0.0670924768348229, -0.0835771023087037, 
-0.0709997877276756, -0.116003735777656, 
-0.0794873243023737, -0.067057402058551, 
-0.0698663891865543, -0.0511133873895728, 
-0.0513203609998669, -0.0894001277309737, 
-0.0398284483421012, -0.0514468502511471, 
-0.0599700163953942, -0.0661889418696937, 
-0.079516218903545, -0.0685966077135509, 
-0.0861445337428064, -0.0923966209966709, 
-0.133444703431511, -0.131567692883267, 
-0.127157375630663, -0.136327904368355, 
-0.102133208996487, -0.109453799095327, 
-0.103333580486325, -0.0982528240902063, 
-0.139243862997714, -0.112067682286408, 
-0.0741501704478233, -0.0885574830826608, 
-0.0819203358523941, -0.0891168040724528, 
-0.0331415164887199, -0.038039022334333, 
0.000471320939768205, -0.0250547289467331, 
-0.0411983586070352, -0.0463752713008887, 
-0.0184870766950889, -0.0318185253129144, 
-0.0623828610377037, -0.0718563679309012, 
-0.0635702270765757, -0.0929728977267059, 
-0.0894248292570765, -0.0919046741661464, 
-0.0844700793317346, -0.112800098282505, 
-0.141344968548085, -0.127965917566584, 
-0.143980868315393, -0.154901662762077, 
-0.130634570152671, -0.150417664726561, 
-0.163723312802416, -0.146099566906346, 
-0.14837251795191, -0.144887288973472, 
-0.14232221415307, -0.142825446351853, 
-0.158838097005599, -0.14340614330986, 
-0.118935233992604, -0.109627188482776, 
-0.120889714109902, -0.119484146944083, 
-0.0950435556738212, -0.134667374330086, 
-0.155051119642286, -0.134094795193097, 
-0.128627607285988, -0.133954472488274, 
-0.119286541395138, -0.135714339904381, 
-0.0903767618937357, -0.109592987693797, 
-0.0770998518949151, -0.108375176935532, 
-0.1369, -0.0856673865524131, 
-0.108854388315838, -0.0708359081737591, 
-0.106961434062811, -0.0429126711978416, 
-0.0550592121225453, -0.0715845951018634, 
-0.0509376225313689, -0.0570175197192393, 
-0.0724229547086495, -0.0867303057832318, 
-0.089712447506396, -0.125158029708487, 
-0.122260116350003, -0.0905629436620448, 
-0.090357598491857, -0.097173095034008, 
-0.0674973361276239, -0.12411935716644, 
-0.0957789729967162, -0.088838044599159, 
-0.110065127067576, -0.108172925482296) 

# install.packages(c("arfima"), dependencies = TRUE) 
# library(arfima) 
arfima::arfima(y, order = c(2, 0, 2)) 

Answer 1

的解決方案是在numeach選項來設置所述第二參數爲0，即

arfima::arfima(y, order = c(2, 0, 2), numeach = c(2, 0)) 

此控制開始次數分數參數的數目。