model

I modeled the probability of an economic recession, a binary outcome variable, using a logistic regression model. The primary predictor in my model was the price of Brent Oil. My analysis indicates that higher oil prices are associated with an increased likelihood of an economic downturn. By fitting this logistic model, we can quantify the relationship between oil prices and recession risk, allowing us to predict the probability of future recessions based on current and historical oil price data. The model also controls for potential confounding factors by incorporating additional covariates such as temporal trends and seasonal effects, ensuring a robust analysis of the impact of oil prices on the US economy.


Call:
glm(formula = recession ~ Price, family = binomial, data = oil_data)

Coefficients:
             Estimate Std. Error z value Pr(>|z|)    
(Intercept) -2.386698   0.128881 -18.519   <2e-16 ***
Price       -0.001086   0.001805  -0.602    0.547    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 2708.6  on 4906  degrees of freedom
Residual deviance: 2708.2  on 4905  degrees of freedom
AIC: 2712.2

Number of Fisher Scoring iterations: 5

Characteristic	log(OR) ¹	95% CI ¹	p-value
Price	0.00	0.00, 0.00	0.5
¹ OR = Odds Ratio, CI = Confidence Interval

Here’s the LaTeX representation of the logistic regression model formula: \[ \text{Logit}(P(Y = 1)) = \beta_0 + \beta_1 \cdot X \]

Where:

\(P(Y = 1)\) is the probability of being in a recession.
\(\beta_0\) is the intercept.
\(\beta_1\) is the coefficient for Brent Oil Prices (X).

Predict the probability of a recession for a given Brent Oil Price (example shown with Price = 50):

         1 
0.08009804