Currently I'm working on a project to do forecasting of a time series data (monthly data). I am using R to do the forecasting. I have 1 dependent variable (y) and 3 independent variables (x1, x2, x3). The y variable has 73 observations, and so does the other 3 variables (alos 73). From January 2009 to January 2015. I have checked correlations and p-value, and it's all significant to put it in a model. My question is: How can I make a good prediction using all the independent variables? I don't have future values for these variables. Let's say that I would want to predict what my y variable in over 2 years (in 2017). How can I do this?
I tried the following code:
model = arima(y, order(0,2,0), xreg = externaldata)
Can I do a prediction of the y value over 2 years with this code?
I also tried a regression code:
reg = lm(y ~ x1 + x2 + x3)
But how do I take the time in this code? How can I forecast what my y value will be over lets say 2 years? I am new to statistics and forecasting. I have done some reading and cam across the lag value, but how can I use a lag value in the model to do forecasting?
Actually my overall question is how can I forecast a time series data with external variables with no future value?