I have a distributed lag model of the form:
lm(wellbeing ~ temperature + temperature_lag1 + temperature_lag2 + time + individual, df)
where I'm interested in finding out the long-run multiplier for the effect of daily temperatures on an individual's wellbeing.
I understand that the coefficient for the multiplier would simply be the sum of the beta coefficients for temperature, temperature_lag1 and temperature_lag2.
However, I'm not entirely sure how to go about calculating the standard error for the multiplier.
I've seen suggestions for doing so by regressing:
dif1 <- temperature_lag1 - temperature dif2 <- temperature_lag2 - temperature lm(wellbeing ~ temperature + dif1 + dif2 + time + individual, df)
With the beta for temperature acting as the SE for the long-run multiplier. However, I'm not sure how valid this approach is?
Any guidance would be much appreciated.