ARIMA models in Stata - Part 1: Identification
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3 سال پیش
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ARIMA Models in Stata -
ARIMA Models in Stata - Part 1: Identification. Learn how to forecast in Stata step by step! In this video, I cover ARIMA models in Stata and use the Box-Jenkins model selection criteria to select the right model to forecast/predict future values.
This video provides a comprehensive guide on ARIMA models and Box-Jenkins model selection in Stata, divided into three parts. In this first part, the focus is on the identification process of ARIMA models. ARIMA is a popular method used for time-series forecasting, and in this video, we will be using the Consumer Price Index for the USA to forecast values for 2021 using an ARIMA model, and selecting the appropriate model with the Box-Jenkins model selection criteria.
The first step in this process is identifying whether the variable is stationary and, if not, the order of differencing needed to achieve stationarity. The next step is identifying the autoregressive and moving average components. The video demonstrates how to check for stationarity using various methods, such as a graph, correlogram, and formal tests.
Moreover, the video provides insights on how to import data and set the time variable. This video is perfect for anyone interested in mastering ARIMA models and Box-Jenkins model selection in Stata. By the end of this series, you will have a comprehensive understanding of how to forecast values and select the best model for your data. Don't miss out on this informative video!
📣 Get the complete package for learning ARIMA models in Stata! Purchase the slides used in the video, along with the complete Stata Do File and Dataset at: https://jdeconomicstore.com/b/arimastata.
📈 You can also download the dataset for free and replicate the content of the video at https://www.jdeconomics.com/stata-tut....
✅ This video is part of a FREE STATA Course. See the full course outline at:
https://www.jdeconomics.com/stata-tut...
✅ Visit my website for all the content available:
https://www.jdeconomics.com/
📣 Tutorial is also available in EViews:
ARIMA models and Box-Jenkins method i...
📺 For more videos likes this, please subscribe: @jdeconomics
✅ Is there any topic you would like me to cover? Do you have any research questions? Contact me at:
📧 [email protected]
📲 Follow me on social media for news, updates, discounts, tips and more!
https://juandamico.start.page/
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🕘 Timestamps:
👋 Introduction 0:00
📊Overview of ARIMA and Box-Jenkins: 0:49
📊 Box-Jenkins Stage 1-Identification: 2:05
📊 a) Stationarity: 2:44
📊 b) Identifying "p" and "q": 14:15
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There are three Videos : Ensure to watch them all to learn about time series forecasting.
Video 2: ARIMA models in STATA - Part 2: Estimation
🌐Link: ARIMA models in Stata - Part 2: Estim...
Video 3: ARIMA models in STATA - Part 3: Diagnostics and Forecasting.
🌐Link: ARIMA models in Stata - Part 1: Ident...
---------------------------------------------------------------------------------------------------------
✅ Interested in learning more?
🎬 Learn how to write your research paper in a fancy way in Latex with Overleaf: Latex with Overleaf Tutorial/Course
🎬 EViews related videos:
Applied Time Series Analysis: Free Ev...
---------------------------------------------------------------------------------------------------------
If you liked the video and would like more content, please support my channel subscribing!
👍Like and subscribe for more videos!
☕️ If you would like to show your appreciation and make a donation:
💳 https://paypal.me/JDEconomics?locale....
Thanks a lot!
This video provides a comprehensive guide on ARIMA models and Box-Jenkins model selection in Stata, divided into three parts. In this first part, the focus is on the identification process of ARIMA models. ARIMA is a popular method used for time-series forecasting, and in this video, we will be using the Consumer Price Index for the USA to forecast values for 2021 using an ARIMA model, and selecting the appropriate model with the Box-Jenkins model selection criteria.
The first step in this process is identifying whether the variable is stationary and, if not, the order of differencing needed to achieve stationarity. The next step is identifying the autoregressive and moving average components. The video demonstrates how to check for stationarity using various methods, such as a graph, correlogram, and formal tests.
Moreover, the video provides insights on how to import data and set the time variable. This video is perfect for anyone interested in mastering ARIMA models and Box-Jenkins model selection in Stata. By the end of this series, you will have a comprehensive understanding of how to forecast values and select the best model for your data. Don't miss out on this informative video!
📣 Get the complete package for learning ARIMA models in Stata! Purchase the slides used in the video, along with the complete Stata Do File and Dataset at: https://jdeconomicstore.com/b/arimastata.
📈 You can also download the dataset for free and replicate the content of the video at https://www.jdeconomics.com/stata-tut....
✅ This video is part of a FREE STATA Course. See the full course outline at:
https://www.jdeconomics.com/stata-tut...
✅ Visit my website for all the content available:
https://www.jdeconomics.com/
📣 Tutorial is also available in EViews:
ARIMA models and Box-Jenkins method i...
📺 For more videos likes this, please subscribe: @jdeconomics
✅ Is there any topic you would like me to cover? Do you have any research questions? Contact me at:
📧 [email protected]
📲 Follow me on social media for news, updates, discounts, tips and more!
https://juandamico.start.page/
---------------------------------------------------------------------------------------------------------
🕘 Timestamps:
👋 Introduction 0:00
📊Overview of ARIMA and Box-Jenkins: 0:49
📊 Box-Jenkins Stage 1-Identification: 2:05
📊 a) Stationarity: 2:44
📊 b) Identifying "p" and "q": 14:15
---------------------------------------------------------------------------------------------------------
There are three Videos : Ensure to watch them all to learn about time series forecasting.
Video 2: ARIMA models in STATA - Part 2: Estimation
🌐Link: ARIMA models in Stata - Part 2: Estim...
Video 3: ARIMA models in STATA - Part 3: Diagnostics and Forecasting.
🌐Link: ARIMA models in Stata - Part 1: Ident...
---------------------------------------------------------------------------------------------------------
✅ Interested in learning more?
🎬 Learn how to write your research paper in a fancy way in Latex with Overleaf: Latex with Overleaf Tutorial/Course
🎬 EViews related videos:
Applied Time Series Analysis: Free Ev...
---------------------------------------------------------------------------------------------------------
If you liked the video and would like more content, please support my channel subscribing!
👍Like and subscribe for more videos!
☕️ If you would like to show your appreciation and make a donation:
💳 https://paypal.me/JDEconomics?locale....
Thanks a lot!
3 سال پیش
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