Statistical models to Predict Recession

MSVAR_DCC_models.py

#Markov-Switching Vector Autoregression (MSVAR) model
import numpy as np
import statsmodels.tsa.api as smt

# Set the number of lags to consider
nlags = 4

# Create the model
model = smt.MSVAR(data, lags=nlags)

# Fit the model
results = model.fit()

# Print the predicted probabilities of a recession
print(results.predicted_probabilities[:, 1])

############################################
#Dynamic Conditional Correlation (DCC)
import numpy as np
import statsmodels.tsa.api as smt

# Set the number of lags to consider
nlags = 4

# Create the model
model = smt.DCC(data, lags=nlags)

# Fit the model
results = model.fit()

# Print the predicted probabilities of a recession
print(results.predicted_probabilities[:, 1])

###########################################
#Here is an example of Python code for the Dynamic Conditional Correlation (DCC) model
#using unemployment rate, consumer sentiment, stock market volatility (S&P 500), and corporate interest rates as variables. 
#This code assumes that the data is stored in a CSV file called "recession_data.csv" 
import numpy as np
import pandas as pd
import statsmodels.tsa.api as smt

# Load the data
data = pd.read_csv("recession_data.csv")

# Set the number of lags to consider
nlags = 4

# Create the model
model = smt.DCC(data[['unemployment_rate', 'consumer_sentiment', 'sp500_volatility', 'corporate_interest_rates']], lags=nlags)

# Fit the model
results = model.fit()

# Print the predicted probabilities of a recession
print(results.predicted_probabilities[:, 1])