Hello, I am fairly new to linearr regression and i want to see the relationship between two time series variables.I have two data variables: Mill rates and Increment values from 2014-2018 (i know it's not a lot of data). As increment value is created via TIF districts, the mill rate goes up. Incremental value is dollar amounts (i.e $112,309,600) and mill rate is in decimals but represent percentages (i.e .024578). I know there are problems with autocorrelation when it comes to timeseries, but i used the tslm() which I hope solves that problem ?
Here is my code:
millratets <- ts(Millrate, start=2014)
incrementts <- ts(Increment Value Generated From Previous year, start=2014)
regts <- tslm(millratets~log(incrementts))
summary(regts)
(i chose level-log model because it would be easier to interpret)
Here are my results:
Call: tslm(formula = millratets ~ log(incrementts))
Residuals:
Time Series:
Start = 2014
End = 2018
Frequency = 1
1 2 3 4 5
-0.00018196 -0.00012986 0.00007215 0.00040960 -0.00016993
Coefficients:
----------------------- Estimate --Std. Error-- t value-- Pr(>|t|)
(Intercept) --------0.0250244 -- 0.0082064--3.049---0.05545 .
log(incrementts) 0.0026857-- 0.0004363 -- 6.155 --0.00863 **
Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.0002897 on 3 degrees of freedom
Multiple R-squared: 0.9266, Adjusted R-squared: 0.9022
F-statistic: 37.88 on 1 and 3 DF, p-value: 0.008629
At first glance, seems like good results to me and the coefficients make sense. For everyone 1% increase in incremental value the mill rate goes up .0026. I just wanted to double check with the geniuses on this forum and make sure everything was legit 