I have this data frame on which I want to apply the ARDL model but every time I run it, it gives me an error. If anyone could please help me or point out what I am doing wrong would be highly appreciated. Error:'list' object cannot be coerced to type 'double' If I remove the prediction part of the code in the loop out then it runs otherwise no.
Data:
structure(list(Industrialproduction = c(1.65801981343852, 1.79541527049647,
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2.39425379090184, 2.98519095869059, 4.36691137516082, 3.57868020304568,
1.66275772744776, 3.79450451070863, 4.52162951167727, 2.28203256419209,
4.17054552224914, 3.2439678284182, 4.76643873164257, 0.955633279171614,
2.91614381581101, 0.848198902642676, 5.02010671012167), OUTERSEAST = c(6.7110371602884,
7.53638253638255, 9.47317544707589, 8.56512141280351, 3.82269215128102,
2.11515863689776, 1.64940544687381, -1.73584905660378, 1.34408602150539,
1.78097764304659, 0.446760982874161, -1.26019273535953, 0.150150150150159,
3.11094452773611, 1.4176663031625, 2.54480286738352, 5.56448794127927,
4.89371564797033, 3.88257575757575, 1.85961713764815, 5.54859495256845,
4.29879599796508, 2.00525702517411, 3.63679834232127, 3.44509381728699,
3.46664684309643, 1.93988743863012, 2.50440502760482, 2.96578121060713,
4.47634947134114), OUTERMET = c(4.54545454545458, 6.58505698607005,
7.36633663366336, 7.08225746956843, 4.3747847054771, 1.68316831683168,
1.00616682895164, -1.28534704370181, 2.01822916666665, 0.797702616464613,
0.949667616334271, -0.940733772342415, 1.10794555238999, 2.19160926737633,
2.84926470588237, 2.62138814417631, 5.02467343976781, 5.65213786241397,
3.22555328833776, 3.73552294786995, 5.05948745510956, 4.28797321179426,
2.86300392436674, 2.60339894216597, 4.28031183318191, 3.43199821714381,
3.34554286721641, 3.04770569170409, 1.65167650683293, 4.62120252591965
), LONDON = c(8.11719500480309, 10.3065304309196, 6.32299637535239,
7.65151515151515, 1.30190007037299, 2.1535255296978, -0.204012240734436,
-0.306643952299836, 0.786056049213951, 1.18684299762631, 1.00536193029493,
-2.85335102853352, 2.76639344262296, 2.06048521103356, 1.23738196027352,
2.70183338694115, 3.30410272471031, 5.76322570865546, 4.73255747291176,
1.98428989791171, 6.03563952552197, 4.88977753030802, 2.12581135535556,
4.43247330120026, 5.42986425339366, 3.96781115879828, 3.43247538648888,
4.0668901660281, 4.09587727708534, 4.81707991010573), SOUTHWEST = c(6.17577197149644,
7.71812080536912, 7.63239875389407, 9.45489628557649, 2.46804759806079,
2.19354838709679, 1.72558922558922, 0.248241621845247, 1.48576145274456,
2.03334688897925, -0.677560781187733, -2.3274478330658, 1.80772391125718,
2.42130750605327, 1.85185185185186, 0.928433268858785, 5.95247221157533,
4.38447346525341, 3.30272049904696, 2.25107353730542, 3.86823714688802,
2.04371722787289, 3.04596811639065, 4.19057346270538, 2.45646407565451,
2.17525889239081, 2.83400809716597, 1.58015962290428, 2.77894958869438,
4.08650146221331), WALES = c(6.09418282548476, 8.35509138381203,
7.40963855421687, 7.01065619742007, 1.15303983228513, 3.47150259067357,
-0.150225338007013, 0.852557673019058, 0.944803580308295, -1.13300492610835,
0.946686596910786, -2.17176702862782, 3.98587285570131, 0.485201358563789,
3.62143891839691, 1.63094128611373, 1.61852361302152, 4.32251951450617,
1.28887158859911, 0.68747598104105, 3.71925360474978, 4.66941979801284,
1.44927536231884, 1.05121293800539, 1.67663757954501, 2.9419480568152,
-0.422309596621509, 2.67987715706347, 0.0249243368346056, 2.03260714794249
), SCOTLAND = c(5.15222482435597, 4.12026726057908, 5.40106951871658,
8.67579908675796, -0.280112044817908, 2.94943820224719, 1.04592996816735,
1.21512151215122, 1.33392618941751, 3.59806932865292, 0.974163490046604,
0.125838926174496, 1.46627565982404, 3.42691990090835, -0.838323353293421,
1.97262479871176, 3.40702724042636, 4.30649410147751, 2.44866586142527,
1.93997856377279, 2.09581887638873, 4.22573890357352, 0.833278440155458,
4.15155969296095, 2.01655899140689, 1.93980755633434, 0.325693606755129,
0.796561260069754, -0.381713535919834, 2.90974405029185), NIRELAND = c(4.54545454545454,
4.94752623688156, 4.42857142857145, 2.96397628818967, 6.06731620903454,
0.0835073068893502, -1.66875260742594, -2.96987696224015, -1.18058592041975,
-0.884955752212393, -1.74107142857143, -0.545206724216265, 1.96436729100047,
-0.224014336917564, -1.84104176021554, 1.6010978956999, 1.42278253039172,
1.97993429814437, 1.29287828660979, 1.61158623060724, 2.28387751649466,
1.84005954349984, 1.79057208981284, 2.22177901874749, 2.88757950598978,
-0.731975575530031, 3.07939176281808, -0.0593031875463392, -1.05696484201158,
3.40717418194087), UK = c(5.76890543055322, 7.20302836425676,
7.39543442582184, 7.22885986848197, 3.23472252213347, 2.95766398929048,
1.20271423347285, -0.554061107319231, 0.98913965036942, 1.55113136643479,
0.373986300291293, -1.61195434757029, 1.59052858167903, 2.07573082205217,
1.17628969016684, 2.44680851063832, 2.84453345201007, 4.10010457610617,
2.88208396840793, 1.58922558922557, 3.67559326527908, 3.90013106997858,
1.36611181194425, 4.12505691303686, 2.02017257462689, 2.93167985827357,
1.54068234183715, 2.12149379408387, 0.594313861969269, 3.83755588673622
)), row.names = c(NA, 30L), class = "data.frame")
Code:
library(tidyverse)
library(GGally)
library(Amelia)
library(inspectdf)
library(ggcorrplot)
library(ggplot2)
library(reshape2)
library(tseries)
library(dplyr)
library(caret)
library(tidyverse)
library(ARDL)
library(dLagM)
library(forecast)
in_sampleARDL <- data %>%
dplyr::filter(Date < '2020-03-01')
out_sampleARDL <-data %>%
dplyr::filter(Date >= '2020-03-01')
# Model Building
# Create the formulas
indep_vars <- expression(Industrialproduction, Householdconsumption, Investmentgrowth, ConsumerPriceIndex, Employment, Unemploymentrate,
Stockmarketindex, Economicgrowth, Consumptiongrowth, Governmentexpenditure, Longtermgovernmentbondyield,
BankRate, ConsumerConfidenceIndex, RealPersonalDisposableIncome, PersonalDisposableIncome, SPPricechange,
HouseStarts, HouseCompleted, TermSpread, BuildingPermits)
dep_vars <- expression(NORTH, YORKSANDTHEHUMBER, NORTHWEST, EASTMIDS, WESTMIDS, EASTANGLIA, OUTERSEAST, OUTERMET, LONDON,
SOUTHWEST, WALES, SCOTLAND, NIRELAND, UK)
# Formulae with diff()
formulae <- unlist(lapply(dep_vars, \(x) lapply(indep_vars, \(y) bquote(.(x)~diff(.(y))))))
length(formulae)
# Without diff()
formulae2 <- unlist(lapply(dep_vars, \(x) lapply(indep_vars, \(y) bquote(.(x)~.(y)))))
length(formulae2)
result <- vector('list', length = length(formulae))
names(result) <- formulae2
# Loop for H = 4
for (i in seq_along(formulae)){
# auto_ardl
result[[i]][[1]] <- auto_ardl(formula(formulae2[[i]]),
data = in_sampleARDL, max_order = 4, selection = 'BIC')
# prediction
result[[i]][[2]] <- forecast(ardlDlm(formula = formula(formulae[[i]]), data = in_sampleARDL, p = 3),
x = out_sampleARDL |> select(sub("\\s~.*", "", formula(formulae[[i]]))) |> pull(), h = 4)
# error
result[[i]][[3]] <- mean((out_sampleARDL |> select(sub("\\s~.*", "", formula(formulae[[i]]))) |> pull() |> (\(x) x[1:4])() - result[[i]][[2]][["forecasts"]])^2)
# set names
names(result[[i]]) <- c('auto_ardl','forecast','error')
}
print(result[[i]])
CodePudding user response：
traceback() shows that this error is coming from the forecast call, and that it occurs with i==1, so look at the first parameter to forecast :: ardlDlm(formula = formula(formulae[[i]]), data = in_sampleARDL, p = 3) and realize that it is not something that forecast is designed to work with. forecast was expecting an atomic numeric vector.
Looking at the output of ardlDlm(formula = formula(formulae[[1]]), data = in_sampleARDL, p = 3), it appears that you really want numeric vectors contained in the $data leaf of that much longer list and in particular probably want only the i-th column, so try this:
for (i in seq_along(formulae)){
# auto_ardl
result[[i]][[1]] <- auto_ardl(formula(formulae2[[i]]),
data = in_sampleARDL,
max_order = 4, selection = 'BIC')
# prediction
#
result[[i]][[2]] <- forecast(ardlDlm(formula = formula(formulae[[i]]),
#---------------extract one col------------------\/-\/-\/-
data = in_sampleARDL, p = 3)$data[[i]],
x = out_sampleARDL |>
select(sub("\\s~.*", "", formula(formulae[[i]]))) |>
pull(), h = 4)
# error
result[[i]][[3]] <- mean((out_sampleARDL |> select(sub("\\s~.*",
"", formula(formulae[[i]]))) |>
pull() |> (\(x) x[1:4])() -
result[[i]][[2]][["forecasts"]])^2)
# set names
names(result[[i]]) <- c('auto_ardl','forecast','error')
}
Note that you only printed the last value in the much longer result object. The last such value looks like:
print(result[[i]])
$auto_ardl
$auto_ardl$best_model
Time series regression with "ts" data:
Start = 5, End = 30
Call:
dynlm::dynlm(formula = full_formula, data = data, start = start,
end = end)
Coefficients:
(Intercept) L(UK, 1) BuildingPermits L(BuildingPermits, 1) L(BuildingPermits, 2)
1.59718 -0.04719 0.90441 -0.04269 0.19583
L(BuildingPermits, 3) L(BuildingPermits, 4)
0.63773 0.02544
$auto_ardl$best_order
[1] 1 4
$auto_ardl$top_orders
UK BuildingPermits BIC
1 1 4 90.18992
2 2 4 93.11884
3 3 4 96.02905
4 1 3 98.36867
5 4 4 99.15721
6 3 3 100.20359
7 2 3 100.53056
8 2 2 104.78506
9 1 2 104.85999
10 1 1 106.10666
$forecast
Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
1 NaN NaN NaN NaN NaN
2 NaN NaN NaN NaN NaN
3 NaN NaN NaN NaN NaN
4 NaN NaN NaN NaN NaN
$error
[1] NaN