I need help solving this error, I am not sure how to, but it seems as when I run the "dp_stat" to get the statistics, an error appears when I do my OLS model. Essentially I try to find the cumulative difference between a benchmark and a given individual predictive model. Here dp is my independent variable and sg is my dependent variable. datanu is my excel data. I'm not sure how to attach the data here, however here is a link to the excel and the code: https://drive.google.com/drive/folders/12BOuNBODURIP7CQIBZWMmHFc1d7zXHxN?usp=sharing If anyone has a fix it would mean the world!
"#Error in lag():! n must be a positive integer, not a double vector of length 1."
rm(list= ls()) # Clear global environment
invisible(gc()) # Free up unused R-occupied memory
cat("\014") # Clear console output: equivalent to ctrl + L
library("tseries")
library("readxl")
library("Metrics")
library("lubridate")
library("ggplot2")
library("data.table")
library("dyn")
library("reshape2")
#header TRUE fordi første row er navne.
datanu <- read_xlsx("~/Documents/6.semester/Bachelor/Data/datanu.xlsx",
na = "NaN",
sheet = "datax",
)
myts <- ts(datanu, start=c(1872, 1), end=c(2020, 12), frequency=12)
plot(myts[, c("dp", "dy", "ep", "de")])
get_statistics <- function(myts, dp, sg, h=1, start=1872, end=2020, est_periods_OOS = 20) {
#### IS ANALYSIS
#1. Historical mean model for en portefølje
avg <- mean(window(myts, start, end)[, sg], na.rm=TRUE)
IS_error_N <- (window(myts, start, end)[, sg] - avg)
#2. OLS model - installér dyn her
#reg <- dyn$lm(sg ~ lag(as.numeric(dp), 1), data=window(myts, start, end))
reg <- dyn$lm(eval(parse(text=sg)) ~ lag(eval(parse(text=dp)), -1), data=window(myts, start, end)) #Error in `lag()`:! `n` must be a positive integer, not a double vector of length 1.
IS_error_A <- reg$residuals
#OOS ANALYSIS
OOS_error_N <- numeric(end - start - est_periods_OOS)
OOS_error_A <- numeric(end - start - est_periods_OOS)
#anvender kun information op til forecasten er lavet.
j <- 0
for (i in (start + est_periods_OOS):(end-1)) {
j <- j + 1
#Get the actual ERP that you want to predict
actual_ERP <- as.numeric(window(myts, i+1, i+1)[, sg])
#1. Historical mean model
OOS_error_N[j] <- actual_ERP - mean(window(myts, start, i)[, sg], na.rm=TRUE)
#2. OLS model
reg_OOS <- dyn$lm(eval(parse(text=sg)) ~ lag(eval(parse(text=dp)), -1),
data=window(myts, start, i))
#Compute_error
df <- data.frame(x=as.numeric(window(myts, i, i)[, dp]))
names(df) <- dp
pred_ERP <- predict.lm(reg_OOS, newdata=df)
OOS_error_A[j] <- pred_ERP - actual_ERP
}
#Compute statistics
MSE_N <- mean(OOS_error_N^2)
MSE_A <- mean(OOS_error_A^2)
T <- length(!is.na(myts[, sg]))
OOS_R2 <- 1 - MSE_A/MSE_N
#Is the -1 enough (maybe -2 needed because of lag)?
OOS_oR2 <- OOS_R2 - (1-OOS_R2)*(reg$df.residual)/(T - 1)
dRMSE <- sqrt(MSE_N) - sqrt(MSE_A)
##
#### CREATE PLOT
IS <- cumsum(IS_error_N[2:length(IS_error_N)]^2)-cumsum(IS_error_A^2)
OOS <- cumsum(OOS_error_N^2)-cumsum(OOS_error_A^2)
df <- data.frame(x=seq.int(from=start + 1 + est_periods_OOS, to=end),
IS=IS[(1 + est_periods_OOS):length(IS)],
OOS=OOS) #Because you lose one observation due to the lag
#Shift IS errors vertically, so that the IS line begins
# at zero on the date of first OOS prediction. (se Goyal/Welch (2008, side 1465))
df$IS <- df$IS - df$IS[1]
df <- melt(df, id.var="x")
plotGG <- ggplot(df) +
geom_line(aes(x=x, y=value,color=variable)) +
geom_rect(data=data.frame(),#Needed by ggplot2, otherwise not transparent
aes(xmin=2008, xmax=2010,ymin=-0.2,ymax=0.2),
fill='red',
alpha=0.1) +
scale_y_continuous('Cumulative SSE Difference', limits=c(-0.2, 0.2)) +
scale_x_continuous('Year')
##
return(list(IS_error_N = IS_error_N,
IS_error_A = reg$residuals,
OOS_error_N = OOS_error_N,
OOS_error_A = OOS_error_A,
IS_R2 = summary(reg)$r.squared,
IS_aR2 = summary(reg)$adj.r.squared,
OOS_R2 = OOS_R2,
OOS_oR2 = OOS_oR2,
dRMSE = dRMSE,
plotGG = plotGG))
}
dp_stat <- get_statistics(myts, "dp", "sg", start=1872)
dp_stat$plotGG
