**4. Empirical results**

This section presents the estimated results and their implications on how the demand for natural gas responds to demographic and non-demographic factors in OECD countries. The estimation results for the static model utilizing the fixed effect to adjust for unobserved heterogeneity are shown in **Table 3**. This indicates that the majority of the coefficients are statistically significant and are nearly identical to previous research findings. In terms of the overall picture, per capita residential demand is statistically significant and positively correlated with urbanization rate, electricity prices, heating and cooling degree days, while elderly, population density, and natural gas prices are negatively correlated with per capita natural gas consumption.

However, because static models aren't ideal for observing economic trends over time, a dynamic panel model was employed to estimate energy demand, which may be more accurate than a static model. In addition, using a lagged dependent variable as a regressor to investigate residential natural gas demand violates the rigorous exogeneity constraint in static models; thus, the lagged dependent variable is included in the explanatory variables in the dynamic model in this work. The dynamic estimating model for residential natural gas demand is shown in **Table 4**.

The regression results for the dynamic model generated using the two-step system GMM estimator are shown in **Table 4**. First, the findings suggest that the lagged value of natural gas has a beneficial impact on demand. Furthermore, the


*Note: Figures in () are the standard error.*

*\*Significant at the10% level.*

*\*\*Significant at the5% level. \*\*\*Significant atthe1%level.*

#### **Table 3.**

*Estimation results: static model—FE.*


*\*\*Significant at the5% level.*

*\*\*\*Significant atthe1%level.*

**Table 4.** *Estimation results: dynamic GMM estimation.*
