Introduction to ARIMA Models Identification in Stata

In this video, Dr. Ngozi ADEYELE, PhD, Founder Crunch Econometrix, discusses how to identify ARIMA models using Stata. The video covers the first step of the Box-Jenkins methodology for time series analysis. The author discusses how to identify the appropriate ARIMA model for a given dataset using the correlogram, which is the plot of the ACF and PACF against their respective lag lengths. 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 the first step of the ARIMA modeling process, which is identification.
• The author discusses how to identify the appropriate ARIMA model for a given dataset using the correlogram, which is the plot of the ACF and PACF against their respective lag lengths.
• 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 identify 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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