[R] VAR function in vars package: find the standard deviation of the error

[jpm miao]

Hi,

   Does someone know how to find the standard deviation of the error term
in the VAR object? The whole structure of the VAR is attached.


   Thanks,
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structure(list(varresult = structure(list(dlfx = structure(list(
    coefficients = structure(c(0.00813806346620756, 0.00194118750380967, 
    0.0356273560028057, -0.00201411510115263, -0.00543118752499316
    ), .Names = c("dlfx.l1", "flow.l1", "dlfx.l2", "flow.l2", 
    "const")), residuals = structure(c(0.0561549050928879, 0.0208505573686644, 
    0.0190577910133913, -0.304598370814063, 0.0383133465626951, 
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        "381", "382", "383", "384", "385", "386", "387", "388", 
        "389", "390", "391", "392", "393", "394", "395", "396", 
        "397", "398", "399", "400", "401", "402", "403", "404", 
        "405", "406", "407", "408", "409", "410", "411", "412", 
        "413", "414", "415", "416", "417", "418", "419", "420", 
        "421", "422", "423", "424", "425", "426", "427", "428", 
        "429", "430", "431", "432", "433", "434", "435", "436", 
        "437", "438", "439", "440", "441", "442", "443", "444", 
        "445", "446", "447", "448", "449", "450", "451", "452", 
        "453", "454", "455", "456", "457", "458", "459", "460", 
        "461", "462", "463", "464", "465", "466", "467", "468", 
        "469", "470", "471", "472", "473", "474", "475", "476", 
        "477", "478", "479", "480", "481", "482", "483", "484", 
        "485", "486", "487", "488", "489", "490", "491", "492", 
        "493", "494", "495", "496", "497", "498", "499", "500", 
        "501", "502", "503", "504", "505", "506", "507", "508", 
        "509", "510", "511", "512", "513", "514", "515", "516", 
        "517", "518", "519", "520", "521", "522", "523", "524", 
        "525", "526", "527", "528", "529", "530", "531", "532", 
        "533", "534", "535", "536", "537", "538", "539", "540", 
        "541", "542", "543", "544", "545", "546", "547", "548", 
        "549", "550", "551", "552", "553", "554", "555", "556", 
        "557", "558", "559", "560", "561", "562", "563", "564", 
        "565", "566", "567", "568", "569", "570", "571", "572", 
        "573", "574", "575", "576", "577", "578", "579", "580", 
        "581", "582", "583", "584", "585", "586", "587", "588", 
        "589", "590", "591", "592", "593", "594", "595", "596", 
        "597", "598", "599", "600", "601", "602", "603", "604", 
        "605", "606", "607", "608", "609", "610", "611", "612", 
        "613", "614", "615", "616", "617", "618", "619", "620", 
        "621", "622", "623", "624", "625", "626", "627", "628", 
        "629", "630", "631", "632", "633", "634", "635", "636", 
        "637", "638", "639", "640", "641", "642", "643", "644", 
        "645", "646", "647", "648", "649", "650", "651", "652", 
        "653", "654", "655", "656", "657", "658", "659", "660", 
        "661", "662", "663", "664", "665", "666", "667", "668", 
        "669", "670", "671", "672", "673", "674", "675", "676", 
        "677", "678", "679", "680", "681", "682", "683", "684", 
        "685", "686", "687", "688", "689", "690", "691", "692", 
        "693", "694", "695", "696", "697", "698", "699", "700", 
        "701", "702", "703", "704", "705", "706", "707", "708", 
        "709", "710", "711", "712", "713", "714", "715", "716", 
        "717", "718", "719", "720", "721", "722", "723", "724", 
        "725", "726", "727", "728", "729", "730", "731", "732", 
        "733", "734", "735", "736", "737", "738", "739", "740", 
        "741", "742", "743", "744", "745", "746", "747", "748", 
        "749", "750", "751", "752", "753", "754", "755", "756", 
        "757", "758", "759", "760", "761", "762", "763", "764", 
        "765", "766", "767", "768", "769", "770", "771", "772", 
        "773", "774", "775", "776", "777", "778", "779", "780", 
        "781", "782", "783", "784", "785", "786", "787", "788", 
        "789", "790", "791", "792", "793", "794", "795", "796", 
        "797", "798", "799", "800", "801", "802", "803", "804", 
        "805", "806", "807", "808", "809", "810", "811", "812", 
        "813", "814", "815", "816", "817", "818", "819", "820", 
        "821", "822", "823", "824", "825", "826", "827", "828", 
        "829", "830", "831", "832", "833", "834", "835", "836", 
        "837", "838", "839", "840", "841", "842", "843", "844", 
        "845", "846", "847", "848", "849", "850", "851", "852", 
        "853", "854", "855", "856", "857", "858", "859", "860", 
        "861", "862", "863", "864", "865", "866", "867", "868", 
        "869"), c("dlfx.l1", "flow.l1", "dlfx.l2", "flow.l2", 
        "const")), assign = 1:5), qraux = c(1.01713523122126, 
    1.08630646175694, 1.0182876647596, 1.0420525577066, 1.02907560166248
    ), pivot = 1:5, tol = 1e-07, rank = 5L), .Names = c("qr", 
    "qraux", "pivot", "tol", "rank"), class = "qr"), df.residual = 864L, 
        xlevels = structure(list(), .Names = character(0)), call = quote(lm(formula = y ~ 
            -1 + ., data = datamat)), terms = quote(y ~ -1 + 
            (dlfx.l1 + flow.l1 + dlfx.l2 + flow.l2 + const)), 
        model = structure(list(y = c(7.24, 4.45, 2.33, 2.83, 
        2.48, 2.74, 1.4, 1.19, 2.51, 0.13, 2.38, 1.75, -2.08, 
        -1.61, -4.71, -0.81, -5.55, -3.09, -1.67, -1.12, -2.75, 
        -2.04, -0.52, 0.66, -5.49, -2.95, -2.12, -5.78, -6.09, 
        0.67, 6.5, 1.46, -0.04, -0.03, -3.15, 2.99, 0.6, -0.48, 
        1.47, 1.69, 4.85, 2.84, 2.16, 4.13, 1.89, -1.83, 0.49, 
        4.78, 5.56, 0.04, -2.71, -0.67, 3.28, -0.48, 2.51, 0, 
        3.54, 3.31, 3.4, 1.32, 4.96, 5.52, 3.53, 0.07, 1.36, 
        1.46, 1.35, 3.66, 3.1, 1.14, -1.98, 1.49, 4.17, 2.32, 
        5.58, 7, -1.91, -6.57, 2.75, 2.73, -2.15, 1.55, -6.37, 
        -2.35, -5.23, 0.76, 1.6, 1.85, 1.71, -0.21, -3.71, -2.61, 
        -4.79, -6.82, -4.21, -1.92, 0.59, 0.94, 1.41, -5.22, 
        -4.37, -0.78, -1.4, -0.71, -0.61, 0.24, -2.42, -3.08, 
        0.07, 2.3, 6.74, 5.94, 2.69, 8.34, 1.01, -0.85, 0.07, 
        -0.23, -1.99, 1.73, -2.93, -2.04, 0.92, -8.47, -0.58, 
        0.97, 1.34, 0.72, 3.99, -2.68, 2.49, 1.69, -0.03, -1.57, 
        -1.84, -1.88, -1.82, -1.52, 0.37, 2.51, -0.24, 1.16, 
        -1.11, 4.36, 2.56, -3.94, -1.69, 0.15, -0.18, 0.67, -1.12, 
        -3.53, -2.76, -2.99, -1.11, 1.44, -2.23, -2.63, -0.72, 
        -0.06, -1.88, -8.95, -1.39, 5.96, 0.49, -6, -2.54, 1.47, 
        -0.92, -0.07, 1.22, -0.32, -0.38, 5.4, 4.59, 2.7, 0.72, 
        3.56, 0.09, 1.09, 3.26, 0.92, 0.49, 2.89, 2.98, 1.79, 
        0.93, 0.76, 2.64, 1.43, 3.84, 1.99, -4.11, 1.23, -0.45, 
        2, 0.86, -2.69, -1.43, -0.22, -0.66, -1.14, 1.84, 2.11, 
        2.99, 6.02, 0.7, 4.75, 3.96, 4, 0.6, 3.49, 0.38, -0.85, 
        3.53, -2.12, -1.45, -2.86, -1.29, -2.73, -1.95, 1.77, 
        1.7, 2.19, 1.07, -2.28, -0.13, -0.5, 0.08, -0.51, 3.02, 
        4.35, 1.95, 1.21, 0.82, 7.59, 0.64, 2.81, 3.41, 1.5, 
        0.16, 4.14, 0.08, -3.3, 0.56, 1.69, -0.44, 2.75, 1.61, 
        2.81, 1.09, 3.14, 0.67, 1.51, -2.05, -1.69, -3.79, 1.16, 
        4.11, 3.32, 5.47, 2.43, -0.35, 2.12, 1.98, 2.21, -1.08, 
        -0.98, 1.67, 3.11, 3.43, 3.86, -7.16, 0.39, 15.75, 0.26, 
        -2.64, -6.02, -5.91, -5.46, -1.28, 2.42, 3.19, -7.11, 
        -0.19, -6.12, -2.46, -0.35, -0.3, -2.24, -0.55, -0.43, 
        0.69, 1.18, 0.12, -1.97, -1.38, -2.87, -1.6, -1.68, -3.13, 
        -2.08, -0.54, -2.15, 0.38, 0.95, 2.12, 2.47, 2.84, 2.05, 
        3.81, 0.8, 8.05, 3.24, 3.6, 5.04, -1.79, -2.66, 0.64, 
        0.41, -2.86, -0.81, 1.66, 3.84, 2.53, 2.43, 2.89, 3.61, 
        12.69, 4.28, 2.57, -0.27, -1.73, 0.02, 1.56, 2.96, 2.79, 
        4.52, -2.66, -2.3, -1.64, -0.56, 2.19, 0.56, -1.37, -7.47, 
        0.56, -2.17, 2.84, -0.07, 4.3, 3.14, 2.62, -2.23, -1.03, 
        -1.71, 1.96, 2.61, -1.25, -1.21, 3.02, -2.97, -5.14, 
        -3.56, 1.65, 0.8, 2.16, 1.07, -0.39, 0.07, 2.89, 3.84, 
        2.18, -2.74, 1.82, 1.28, -0.35, -0.25, -1.26, -4.95, 
        -3.47, -2.07, -2.48, -2.97, -9.79, -2.61, 0.4, -0.61, 
        0.6, 1.52, -0.04, -1.42, -2.79, -0.96, -1.95, -2.74, 
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        0.14, 2.32, 1.96, -0.5, -5.72, -5.4, 3.85, 1.6, 0.04, 
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        0.15, -0.76, 3.23, 2.7, 1.36, -2.29, 0.75, -1.06, -1, 
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        1.1, 3.05, 1.17, 1.24, 2.77, 1.67, 1.59, 2.9, -0.04, 
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        2.39, -0.31, 1.1, 2.13, 1.87, -4.96, -0.11, 0.66, 0.14, 
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        -3.37, 0.48, 5.78, 1.18, 2.56, 3.3, 0.79, -5.54, 0.77, 
        2.17, 1.08, 0.49, -1.43, -1.99, 0.4, 2.26, 0.72, 0.33, 
        1.51, -1.89, 1.27, -2.02, -2.02, -2.29, -2.03, -2.57, 
        -2.44, 1.75, 1.4, 0.66, -1.53, 0.04, 0.92, 1.38, 1.68, 
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        2.53, -0.23, -0.14, 1.59, 2.76, 3.5, 6.55, 1.63, -1.36, 
        -0.67, 4.86, 3.27, 4.25, 2.55, -1.01, 1.15, 3.71, 2.21, 
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        -2.91, -3.61, -1.42, -0.67, -3.18, -0.29, 0.34, -10.99, 
        -4.25, 1.18, 0.53), dlfx.l1 = c(-0.119200741865999, 0.0470698996687613, 
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3.39, 3.48, 1.3, -0.13, -0.52, -0.57, -3.5, -1.04, -0.54, 0.35, 
1.38, -2.21, 2.13, 3.32, 2.54, 2.61, 1.35, 5.4, 3.51, -0.86, 
1.1, 3.05, 1.17, 1.24, 2.77, 1.67, 1.59, 2.9, -0.04, -1.1, 1.88, 
1.76, 1.28, -4.06, 0.52, -0.73, 1.85, 1.23, 2.25, 1.11, 2.94, 
-0.24, -3.79, -1.99, 0.4, 2.26, -0.89, 2.39, -0.31, 1.1, 2.13, 
1.87, -4.96, -0.11, 0.66, 0.14, -1.32, -1.01, -1.38, 2.49, 3.86, 
-0.42, 2.16, 3.4, 2.46, -3.37, 0.48, 5.78, 1.18, 2.56, 3.3, 0.79, 
-5.54, 0.77, 2.17, 1.08, 0.49, -1.43, -1.99, 0.4, 2.26, 0.72, 
0.33, 1.51, -1.89, 1.27, -2.02, -2.02, -2.29, -2.03, -2.57, -2.44, 
1.75, 1.4, 0.66, -1.53, 0.04, 0.92, 1.38, 1.68, -3.16, -1.87, 
-0.21, 0.63, -1.7, 1.36, 1.15, 1.23, 0.39, 2.53, -0.23, -0.14, 
1.59, 2.76, 3.5, 6.55, 1.63, -1.36, -0.67, 4.86, 3.27, 4.25, 
2.55, -1.01, 1.15, 3.71, 2.21, -0.48, -2.12, 0.79, 3.1, 1.28, 
1.11, -7.76, 0.59, 0.07, -0.17, -0.36, -6.06, -1.3, 0.01, 3.25, 
-1.4, -0.27, -1.28, -2.91, -3.61, -1.42, -0.67, -3.18, -0.29, 
0.34, -10.99, -4.25, 1.18, 0.53), .Dim = c(871L, 2L), .Dimnames = list(
    NULL, c("dlfx", "flow"))), type = "const", p = structure(2L, .Names = "SC(n)"), 
    K = 2L, obs = structure(869L, .Names = "SC(n)"), totobs = structure(871L, .Names = "SC(n)"), 
    restrictions = NULL, call = quote(VAR(y = y_dl, type = "const", 
        lag.max = 30, ic = "SC"))), .Names = c("varresult", "datamat", 
"y", "type", "p", "K", "obs", "totobs", "restrictions", "call"
), class = "varest")