# Baseline Model

The baseline population forecast model is updated annually, reflecting the latest data releases from our primary sources, such as the US Census.

### **Main Data Sources**

* Population Estimates from US Census (County and ZCTA Level)
* Fertility Rates from Centers from Disease Control and Prevent (CDC) WONDER database.
* Mortality Rates from Centers from Disease Control and Prevent (CDC) WONDER database.
* U.S. County Population Data Age/Gender from National Cancer Institute (NCI)

### Baseline Forecast Methodology: Age-Gender Cohort Component Model

For this baseline approach, the cohort component model is applied to age and gender groups using the following main formula:

$$
P\_{s,a+1,t+1} = P\_{s,a,t} \cdot (1 - M\_{s,a,t}) + N\_{s,a+1,t+1}
$$

**Where:**

* P<sub>s,a,t</sub>: Population of sex s, age a, in year t
* M<sub>s,a,t</sub>: Mortality rate for sex s, age a, in year t
* N<sub>s,a,t</sub>: Net migration for sex s, age a, in year t

Of course, new births (age 0) require special treatment, calculated based on the fertility rates of women aged 15 to 45 years:

$$
P\_{s,0,t+1} = \left( \sum\_{a=15}^{49} P\_{f,a,t} \cdot F\_{a,t} \cdot S\_s \right) \cdot (1 - M\_{s,0,t}) + N\_{s,0,t+1}
$$

**Where**:

* F<sub>a,t</sub>: Fertility rate for females aged a in year t
* S<sub>s</sub>: Proportion of births of sex s (e.g., 0.512 for male, 0.488 for female)

For migration patterns, we incorporate annual indicators that help guide the direction of population movement (inflows and outflows), such as:

* Housing market trends
* Employability
* And others

### State-Level Calibration

After building our cohort component model, we saw a chance to improve our projections by comparing them with state-level data from respected research groups. This comparison gave us important insights into regional population differences and helped validate the strength of our initial model.

Using these insights, we added a calibration step that incorporated annual projection data from key states like California and Texas. By adjusting our model to match these expert projections, we enhanced the accuracy and reliability of our forecasts, providing results that are both data-driven and backed by professional expertise.


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