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