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
Figure 1 Lag chart of Model Coefficient vector estimates