I am playing with some marketing data. My response variable is
market share and predictors are
time, brand, retail price, marketing activity.
brand is categorical, but would be a random factor.
retail price is numerical and
marketing activity is binary. 1 indicates activity and 0 indicates no activity.
The question that I am trying to answer is the influence of
retail price and
market share for specific
gls model in R:
# Generalized least square nav_gls <- gls(market_share~display_indicator*retail_price_avg*week_number, data=market_share_df) nav_gls_corr <- update(nav_gls, correlation=corAR1()) AIC(nav_gls, nav_gls_corr)
The results don't improve.
df AIC nav_gls 9 -2373.980 nav_gls_corr 10 -2604.405
Shouldn't my AIC improve by considering even a single period AR? Also, how do you account for autocorrelation when the lag period is more than 1. What is the intuition for using a lag period more than 1?