Jun-23-2024, 08:09 AM
(This post was last modified: Jun-23-2024, 08:14 AM by Gribouillis.)
It appears that the get_prediction method in PanelOLS from statsmodels is not directly supporting prediction intervals for panel data models like it does for OLS (Ordinary Least Squares). In your case, since PanelOLS is designed for panel data with entity and time effects, the method might not directly provide prediction intervals as with simple OLS.
To predict bounds (upper and lower) for 'Y' using your model results, you may need to calculate them manually based on the model's coefficients and standard errors. Here's a general approach:
Extract Coefficients and Standard Errors: Retrieve the coefficients (result.params) and their standard errors (result.std_errors).
Calculate Prediction Interval: Use these coefficients and standard errors to compute the prediction interval manually. For instance, for a given confidence level (e.g., 95%), the upper and lower bounds can be calculated as:
makefile
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lower_bound = result.predict(exog) - 1.96 * result.std_errors
upper_bound = result.predict(exog) + 1.96 * result.std_errors
Adjust the multiplier (1.96 for 95% confidence) based on your desired confidence level.
Stata Comparison: In Stata, the equivalent of predict provides this directly, but in Python's statsmodels, you'll typically need to compute these intervals explicitly.
Here’s an example assuming result is your PanelOLS model result:
python
Copy code
If you need further assistance or clarification on implementing this, feel free to ask!
Best regards,
To predict bounds (upper and lower) for 'Y' using your model results, you may need to calculate them manually based on the model's coefficients and standard errors. Here's a general approach:
Extract Coefficients and Standard Errors: Retrieve the coefficients (result.params) and their standard errors (result.std_errors).
Calculate Prediction Interval: Use these coefficients and standard errors to compute the prediction interval manually. For instance, for a given confidence level (e.g., 95%), the upper and lower bounds can be calculated as:
makefile
Copy code
lower_bound = result.predict(exog) - 1.96 * result.std_errors
upper_bound = result.predict(exog) + 1.96 * result.std_errors
Adjust the multiplier (1.96 for 95% confidence) based on your desired confidence level.
Stata Comparison: In Stata, the equivalent of predict provides this directly, but in Python's statsmodels, you'll typically need to compute these intervals explicitly.
Here’s an example assuming result is your PanelOLS model result:
python
Copy code
# Assuming 'result' is already fitted PanelOLS model predicted_values = result.predict(exog) std_errors = result.std_errors # Calculate prediction interval (e.g., 95% confidence) confidence_level = 1.96 # For 95% confidence interval lower_bound = predicted_values - confidence_level * std_errors upper_bound = predicted_values + confidence_level * std_errors # Print or use the bounds as needed print("Lower Bound:", lower_bound) print("Upper Bound:", upper_bound)This approach manually computes the prediction bounds based on the model’s predictions and standard errors. Ensure that exog contains the correct variables and constants required for prediction.
If you need further assistance or clarification on implementing this, feel free to ask!
Best regards,
Gribouillis write Jun-23-2024, 08:14 AM:
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Please post all code, output and errors (it it's entirety) between their respective tags. Refer to BBCode help topic on how to post. Use the "Preview Post" button to make sure the code is presented as you expect before hitting the "Post Reply/Thread" button.