GMM rolling estimation repeats same value to each windows

Question

With the following code structure, I estimate gmm on rolling basis. The code seems to work but at the end, results are same in each windows which shouldn't be.

library(zoo)
library(gmm)
    > dput(ALLX)
structure(c(" 0.0028923077", " 0.0099160000", "-0.0042440000", 
" 0.0795800000", "-0.0350041667", "-0.2480500000", " 0.0042880000", 
"-0.1620888889", " 0.0637518519", " 0.0928178571", "-0.0974379310", 
"-0.1012612903", "-0.0141133333", " 0.0373500000", " 0.0420323529", 
" 0.0406323529", "-0.0479314286", " 0.0393514286", " 0.0134594595", 
"-0.0042162162", " 0.0282142857", "-0.0467717949", " 0.0271487179", 
" 0.0484756757", " 0.0388105263", "-0.0232324324", "-0.1534487179", 
"-0.0553538462", "-0.0127976190", " 0.0248613636", "-0.0069093023", 
" 0.0016234043", "-0.0004617021", "-0.0747617021", " 0.0362914894", 
" 0.0773816327", "-0.0038936170", " 0.0876829787", "-0.0198040000", 
"-0.0078372549", " 0.0343978723", " 0.0350833333", "-0.0382480000", 
" 0.0824346939", "-0.0280615385", "-0.0312228070", "-0.1020928571", 
"-0.1011178571", " 0.0460186441", " 0.0407403509", "-0.0200607143", 
" 0.1692310345", " 0.1254927273", "-0.0012947368", " 0.0410393443", 
"-0.0060650000", " 0.1175457627", "-0.0219952381", " 0.0538709677", 
" 0.1302564516", " 0.0177306452", " 0.0465440678", "-0.0318777778", 
"-0.0027257576", "-0.0808714286", " 0.0753223881", " 0.0421338235", 
" 0.0192617647", " 0.0645835821", " 0.0178743243", " 0.0917106667", 
"-0.0504720000", "-0.0605917808", " 0.0670115942", " 0.0841644737", 
"-0.0862937500", " 0.0583458333", " 0.0341283951", " 0.0363376623", 
"-0.0072297619", " 0.0697188235", " 0.0708900000", " 0.0964263736", 
" 0.0265102273", "-0.0310302326", " 0.0419810000", "-0.0719272727", 
" 0.0243521739", "-0.0945943925", " 0.0339018868", " 0.0440350000", 
" 0.0503681818", " 0.0629381356", "-0.0510975000", " 0.0249760684", 
" 0.0827214876", " 0.0892224806", "-0.0168313043", " 0.0291585366", 
" 0.0273647541", "-0.0975674242", "-0.0746062016", " 0.0238595238", 
"-0.0278552000", "-0.0925223077", " 0.0659656934", " 0.0456731343", 
" 0.0466132450", "-0.0135568493", " 0.0414873016", " 0.0340628205", 
" 0.0267903846", "-0.0494906667", " 0.0180333333", " 0.0387062500", 
"-0.0283124183", " 0.0306898810", " 0.0886619048", "-0.0198162921", 
"-0.0154431818", "-0.0838285714", " 0.0658326531", "-0.0614076503", 
" 0.0424984211", "-0.0170115000", " 0.0205202970", " 0.0596765258", 
"-0.0538638298", "-0.0453000000", "-0.0493330317", " 0.0545885714", 
" 0.1193183168", "-0.0335423810", " 0.0599138393", " 0.0207066964", 
" 0.0554773756", " 0.0606587156", " 0.0448098039", "-0.0354082569", 
" 0.0057316038", " 0.0019272727", " 0.0214976303", "-0.0108309179", 
" 0.0166442211", " 0.0028019512", "-0.0548807910", " 0.0297556962", 
"-0.0161016854", " 0.0100739726", " 0.0538203704", " 0.0304168724", 
" 0.0246617647", " 0.0294436975", "-0.0192055300", "-0.0221397906", 
" 0.0429194215", "-0.1356025974", " 0.0155836364", " 0.0421339806", 
" 0.0193581152", "-0.0316510526", " 0.0171187192", " 0.0299633484", 
" 0.0136308017", "-0.0803899225", "-0.0462198413", "-0.0337478469", 
"-0.0044057018", " 0.0297085185", " 0.0273300000", "-0.0814074689", 
" 0.0194720183", "-0.0640064885", " 0.0194782979", " 0.0047418868", 
"-0.0393941634", " 0.0119372093", "-0.0092268116", "-0.0221629956", 
" 0.0803489130", "-0.0976939502", "-0.0391824701", "-0.0672646154", 
"-0.0124784247", "-0.1038085627", "-0.0105592476", "-0.0279538710", 
" 0.0196077181", " 0.0338205788", "-0.0189602996", "-0.0860247273", 
"-0.0478760714", "-0.0510344720", " 0.0275029586", " 0.0428622291", 
" 0.0166286232", "-0.0288036101", "-0.0180476923", " 0.0393909483", 
"-0.0641453901", "-0.0841982143", " 0.0047675900", "-0.0240106312", 
"-0.0700313793", " 0.0733418994", "-0.0533325905", "-0.0331509589", 
" 0.0489809399", "-0.0736352078", " 0.0240526646", "-0.0939243553", 
"-0.0042405995", "-0.0387828042", "-0.0470409756", "-0.0931115294", 
"-0.0910497738", " 0.0013111111", " 0.0348456633", " 0.0705459082", 
" 0.0758425000", "-0.1478202222", " 0.0717040179", " 0.0569382114", 
"-0.0945091623", " 0.1044561713", " 0.0293398396", "-0.1272769802", 
" 0.0325193750", " 0.0277184066", "-0.0341782369", "-0.0250598160", 
" 0.0189187500", "-0.0074535385", "-0.0359813953", " 0.0160150115", 
" 0.0476877551", "-0.1018837398", "-0.0427113689", " 0.0191961637", 
" 0.1059326403", "-0.169583118", "-0.116332577", "-0.129824912", 
"-0.124963142", "-0.126441587", "-0.225621177", "-0.168195296", 
"-0.338720204", "-0.317270673", "-0.273857581", " 0.035586249", 
"-0.092702051", "-0.064176332", "-0.153796722", "-0.325390751", 
"-0.274011376", "-0.227546299", "-0.365942549", " 0.085455103", 
"-0.010836766", "-0.217781081", " 0.003511111", "-0.096351701", 
"-0.036061785", "-0.082746305", "-0.058446435", "-0.029538576", 
"-0.061935687", "-0.099415678", "-0.159079368", " 0.005994670", 
"-0.120082806", " 0.014296796", " 0.015944490", "-0.178096580", 
"-0.196765343", "-0.149854935", "-0.182080042", "-0.047670592", 
"-0.064468455", "-0.087561840", "-0.090476667", "-0.181043278", 
"-0.085486903", "-0.090858829", "-0.066936975", "-0.015848709", 
"-0.110468333", "-0.153506248", "-0.044522802", "-0.080978652", 
"-0.159889171", "-0.154480172", "-0.205349921", "-0.103695059", 
"-0.138147377", "-0.145655249", "-0.040561855", "-0.126550923", 
"-0.108670753", "-0.072749279", " 0.007390436", "-0.158405424", 
"-0.194249538", "-0.249071040", "-0.063031677", "-0.137200770", 
"-0.005985255", "-0.061970616", "-0.001665978", "-0.125325668", 
"-0.135089529", "-0.030677383", "-0.022131183", "-0.027605680", 
"-0.048131926", "-0.009713796", "-0.024840051", "-0.003947993", 
"-0.028356052", " 0.008669359", "-0.065582513", "-0.018108408", 
"-0.089593204", " 0.016014273", "-0.222439074", "-0.002558520", 
"-0.003835706", "-0.084480896", "-0.011204725", "-0.009045822", 
" 0.039202318", "-0.017543767", "-0.044229046", "-0.064026845", 
" 0.019091399", "-0.146065442", "-0.082462079", "-0.076968274", 
" 0.010287979", " 0.048663646", "-0.052517617", "-0.078097749", 
"-0.058839773", " 0.020560811", " 0.022542800", "-0.049323346", 
"-0.136855394", " 0.051979118", "-0.049670745", "-0.269486125", 
"-0.016167840", "-0.073386011", "-0.138936808", "-0.053387474", 
" 0.010016301", "-0.068842589", "-0.074879318", "-0.072413842", 
"-0.101225355", "-0.003558611", "-0.041926566", "-0.148669593", 
"-0.009603558", "-0.233703786", "-0.041852318", " 0.041637969", 
"-0.135528966", "-0.107569064", "-0.209429496", "-0.196268700", 
"-0.046792696", "-0.087288872", "-0.034059584", "-0.174947640", 
"-0.138459757", "-0.122212061", "-0.124686515", "-0.391344971", 
"-0.216096655", " 0.052018432", "-0.019510655", "-0.139205691", 
"-0.125329712", " 0.141051207", " 0.049742832", " 0.026519711", 
"-0.110162918", " 0.055812668", " 0.068929681", "-0.039230413", 
"-0.041629101", "-0.055834264", "-0.006038571", "-0.159107391", 
" 0.070929013", " 0.004192821", "-0.120316632", "-0.130435927", 
"-0.026905200", " 0.003387477", "-0.011266511", " 0.057113748", 
" 0.000171649", "-0.046263896", "-0.020481623", "-0.105748694", 
"-0.051830004", " 0.015952694", "-0.104439047", "-0.048152805", 
"-0.043645953", "-0.160393736", "-0.074220347", "-0.059452923", 
"-0.025244806", "-0.092688259", "-0.003549789", " 0.023238113", 
"-0.022835595", "-0.089331999", "-0.063785474", "-0.099384857", 
"-0.053629441", " 0.056101995", "-0.075942152", "-0.056728587", 
"-0.036623971", "-0.003294702", "-0.022135444", "-0.059247766", 
"-0.068374619", "-0.078938524", "-0.031597308", "-0.156975052", 
"-0.026702652", " 0.001761469", "-0.031652647", "-0.057892629", 
"-0.061797364", "-0.112400246", "-0.031780546", "-0.066364666", 
"-0.027673000", " 0.005256987", "-0.025408318", "-0.059037925", 
"-0.049307603", "-0.039687409", "-0.091508111", "-0.144818522", 
" 0.002830555", "-0.148057711", "-0.039918341", "-0.001476170", 
"-0.109996768", "-0.014149004", "-0.064867451", "-0.134710810", 
"-0.074557220", "-0.116546886", "-0.160253490", " 0.038980675", 
"-0.156042254", "-0.247471967", "-0.099724931", "-0.025823112", 
" 0.034017819", "-0.021862462", "-0.105484344", "-0.043692340", 
"-0.032831220", " 0.000531434", "-0.138783395", "-0.080691278", 
"-0.101847518", "-0.011818825", "-0.021092363", "-0.063934848", 
"-0.083217974"), .Dim = c(240L, 2L), .Dimnames = list(c("1", 
"2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", 
"14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", 
"25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", 
"36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", 
"47", "48", "49", "50", "51", "52", "53", "54", "55", "56", "57", 
"58", "59", "60", "61", "62", "63", "64", "65", "66", "67", "68", 
"69", "70", "71", "72", "73", "74", "75", "76", "77", "78", "79", 
"80", "81", "82", "83", "84", "85", "86", "87", "88", "89", "90", 
"91", "92", "93", "94", "95", "96", "97", "98", "99", "100", 
"101", "102", "103", "104", "105", "106", "107", "108", "109", 
"110", "111", "112", "113", "114", "115", "116", "117", "118", 
"119", "120", "121", "122", "123", "124", "125", "126", "127", 
"128", "129", "130", "131", "132", "133", "134", "135", "136", 
"137", "138", "139", "140", "141", "142", "143", "144", "145", 
"146", "147", "148", "149", "150", "151", "152", "153", "154", 
"155", "156", "157", "158", "159", "160", "161", "162", "163", 
"164", "165", "166", "167", "168", "169", "170", "171", "172", 
"173", "174", "175", "176", "177", "178", "179", "180", "181", 
"182", "183", "184", "185", "186", "187", "188", "189", "190", 
"191", "192", "193", "194", "195", "196", "197", "198", "199", 
"200", "201", "202", "203", "204", "205", "206", "207", "208", 
"209", "210", "211", "212", "213", "214", "215", "216", "217", 
"218", "219", "220", "221", "222", "223", "224", "225", "226", 
"227", "228", "229", "230", "231", "232", "233", "234", "235", 
"236", "237", "238", "239", "240"), c("DE", "rmrf_local")), index = 1:240, class = c("zooreg", 
"zoo"), frequency = 1)

And my code is as follows:

DE <- as.matrix(ALLX$DE)
    p <- nrow(DE)
    rmrf_local <- as.matrix(ALLX$rmrf_local)
    h <- matrix(rmrf_local, p, 1)
    dolm <- function(x) gmm(DE ~ rmrf_local, x=h)
    #ALLX = as.zooreg(ALLX )

#rolling gmm estimate  

pp=rollapply(ALLX,width=24,by=1,FUN=dolm,  by.column = FALSE, align = "right")
pp=data.frame(pp)
pp$coefficients

At this point, i'm expecting different result for each 217 windows but i got a repeated same value to each windows as indicated in the last three rows of the output below, any help please?

.
.
.
[[215]]
 (Intercept)   rmrf_local 
-0.001226275 -0.032253645 

[[216]]
 (Intercept)   rmrf_local 
-0.001226275 -0.032253645 

[[217]]
 (Intercept)   rmrf_local 
-0.001226275 -0.032253645 

Answer 1

Apparently, zoo::rollapply runs across a 1-D vector or specifically for the data argument:

data: the data to be used (representing a series of observations).

Since you need to run model on a 2-D matrix, consider lapply across your matrix length indexing in blocks of your window length, 24. Then, with each iteration pass data into gmm. Also, you original ALLX maintained character values and hence needed conversion with as.numeric. Otherwise error would raise: non-numeric argument to binary operator.

gmm_list <- lapply(1:(length(ALLX$DE)-24), function(i) {
  tmp <- ALLX[i:(i+23)]
  DE <- as.matrix(as.numeric(tmp$DE))
  p <- nrow(DE)
  rmrf_local <- as.matrix(as.numeric(tmp$rmrf_local))
  h <- matrix(rmrf_local, p, 1)

  gmm(DE ~ rmrf_local, x=h)
})

# OUTPUTS ALL COEFFICIENTS             
lapply(gmm_list, "[[", "coefficients")

Answer 2

There are several problems:

• ALLX is character whereas clearly we need numeric data
• dolm does not depend on its argument so it cannot be expected to give different values for different inputs

Try this:

ALLX0 <- apply(ALLX, 2, as.numeric)

do_gmm <- function(X) {
  DE <- X[, "DE"]
  h <- rmrf_local <- X[, "rmrf_local"]
  coef(gmm(DE ~ rmrf_local, x = h))
}

r <- rollapplyr(ALLX0, 24, do_gmm, by.column = FALSE, fill = NA)

This gives a matrix r having the same dimensions as ALLX filling it with NAs at the beginning to achieve that but if you don't want the NAs then remove fill = NA.