Introduction to ARIMA Models and Estimation in Stata

In this video, Dr. Ngozi ADEYELE, PhD, Founder Crunch Econometrix, discusses how to estimate ARIMA models using Stata. The video covers step 2 of the ARIMA modeling process, which is estimation. The author first reviews the six tentative models that were selected in the previous video. Then, they demonstrate how to estimate these models using Stata. The author also provides guidance on how to select the most appropriate ARIMA model based on several criteria, including the number of significant coefficients, volatility, log likelihood, AIC, and SBIC. Finally, the author emphasizes the importance of parsimony in ARIMA modeling and encourages viewers to read textbooks and other resources to learn more about the topic.

Key points from the video:

• The video covers step 2 of the ARIMA modeling process, which is estimation.
• The author first reviews the six tentative models that were selected in the previous video.
• Then, they demonstrate how to estimate these models using Stata.
• The author also provides guidance on how to select the most appropriate ARIMA model based on several criteria, including the number of significant coefficients, volatility, log likelihood, AIC, and SBIC.
• Finally, the author emphasizes the importance of parsimony in ARIMA modeling and encourages viewers to read textbooks and other resources to learn more about the topic.

Conclusion

This video provides a helpful overview of how to estimate ARIMA models using Stata. The author covers all the key steps in the process and provides helpful tips for selecting the most appropriate model. I would recommend watching this video if you are interested in learning more about ARIMA modeling.

I hope this article is helpful! Please let me know if you have any questions.

Credit:

To Dr. Ngozi ADEYELE, PhD. Founder Crunch Econometrix

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