this is my first post so sorry if this is clunky or not formatted well.
period texas u3 national u3
1976 5.758333333 7.716666667
1977 5.333333333 7.066666667
1978 4.825 6.066666667
1979 4.308333333 5.833333333
1980 5.141666667 7.141666667
1981 5.291666667 7.6
1982 6.875 9.708333333
1983 7.916666667 9.616666667
1984 6.125 7.525
1985 7.033333333 7.191666667
1986 8.75 6.991666667
1987 8.441666667 6.191666667
1988 7.358333333 5.491666667
1989 6.658333333 5.266666667
1990 6.333333333 5.616666667
1991 6.908333333 6.816666667
1992 7.633333333 7.508333333
1993 7.158333333 6.9
1994 6.491666667 6.083333333
1995 6.066666667 5.608333333
1996 5.708333333 5.416666667
1997 5.308333333 4.95
1998 4.883333333 4.508333333
1999 4.666666667 4.216666667
2000 4.291666667 3.991666667
2001 4.941666667 4.733333333
2002 6.341666667 5.775
2003 6.683333333 5.991666667
2004 5.941666667 5.533333333
2005 5.408333333 5.066666667
2006 4.891666667 4.616666667
2007 4.291666667 4.616666667
2008 4.808333333 5.775
2009 7.558333333 9.266666667
2010 8.15 9.616666667
2011 7.758333333 8.95
2012 6.725 8.066666667
2013 6.283333333 7.375
2014 5.1 6.166666667
2015 4.45 5.291666667
2016 4.633333333 4.866666667
2017 4.258333333 4.35
2018 3.858333333 3.9
2019 ____ 3.5114
2020 ____ 3.477
2021 ____ 3.7921
2022 ____ 4.0433
2023 ____ 4.1339
2024 ____ 4.2269
2025 ____ 4.2738
How can one use auto.arima in R with an external regressor to make a forecast but only plot the out-of-sample values? I believe the forecast values are correct but the years do not match up correctly. So if I have annual data from 1976-2018 and I forecast the dependent variable (column 2) (I want to forecast through 2025), it plots the "forecast" for the time period 2019-2068. Weirdly enough, the figures match up well with the sample data (the "forecast" for 2019 seems to be the model prediction for 1980 and so on, all the way through 2068 matching 2025.
I would like to be able to eliminate that and have it so "2062-2068" results are instead 2019-2025. I'll try and include a picture of the plot so it might be easier to visualize my plight.
Below is the R script:
#Download the CVS file, the dependent variable in the second column, xreg in the third, and years in the first. All columns have headers.
library(forecast)
library(DataCombine)
library(tseries)
library(MASS)
library(TSA)
ts(TXB102[,2], frequency = 1, start = c(1976, 1),end = c(2018, 1)) -> TXB102ts
ts(TXB102[,3], frequency = 1, start = c(1976, 1), end = c(2018,1)) -> TXB102xregtest
ts(TXB102[,3], frequency = 1, start = c(1976, 1), end = c(2025,1)) -> TXB102xreg
as.vector(t(TXB102ts)) -> y
as.vector(t(TXB102xregtest)) -> xregtest
as.vector(t(TXB102xreg)) -> xreg
y <- ts(y,frequency = 1, start = c(1976,1),end = c(2018,1))
xregtest <- ts(xregtest, frequency = 1, start = c(1976,1), end=c(2018,1))
xreg <- ts(xreg, frequency = 1, start = c(1976,1), end=c(2025,1))
summary(y)
plot(y)
ndiffs(y)
ARIMA <- auto.arima(y, trace = TRUE, stepwise = FALSE, approximation = FALSE, xreg=xregtest)
ARIMA
forecast(ARIMA,xreg=xreg)
plot(forecast(ARIMA,xreg=xreg))
The following is a plot of what I get after running the script.
TLDR: How do I get the real out-of-sample forecast to plot for 2019-2025 as opposed to the in-sample model fit it is passing along as 2019-2068.
