Footnotes
The vector estimates presented here represent the coefficients of a
Vector Autoregression (VAR) model. The model includes six variables:
Area, CO2 (Carbon Dioxide), Precip (Precipitation), Temp (Temperature),
Water, and Wheat. The subscript numbers (-1) and (1) denote the lag and
lead of one period, respectively. Coefficients in the vector represent
the impact of a one-unit change in the respective variable on the
dependent variable. For example, a positive coefficient for CO2 (-1)
indicates that a one-unit increase in the lagged CO2 variable leads to
an increase in the dependent variable. S.D. Dependent represents the
standard deviation of the dependent variable. Lower values indicate less
variability. All coefficient estimates are statistically significant at
conventional levels, confirming the relevance of the predictors in
explaining the variation in the dependent variables.
Figure 1 Lag chart of Model Coefficient vector estimates