Time series Analysis

Stata Course

About the Stata Course

This course provides an introduction to the principles and practice of time series analysis and forecasting using Stata. Time series data, which is collected over time, is commonly encountered in fields such as finance, economics, engineering, and social sciences. The course is designed to teach students how to model and analyze time series data and how to make forecasts based on these models. The course is tailored for economics students (undergraduates, graduates, postgraduates) as the examples are based on real-world economic data. Throughout the course, we will see examples of monetary policy, fiscal policy, economic growth, money supply and demand, inflation, and other macroeconomic variables such as the consumer price index.

The course covers the following topics:

Introduction to time series data and its properties
Stationarity and non-stationarity
Mean Forecasting with univariate models (AR, MA, ARMA, ARIMA)
Models for the Variance (Arch and Garch)
Model selection and diagnostic checking
Multivariate Forecasting Models: Vector Autoregressive (VAR) Models
Evaluation and forecast of models
Advanced topics in time series analysis, including Dynamic Stochastic General Equilibrium (DSGE) Models

The course is taught using Stata software, a popular statistical package widely used in academic research and industry. Students will learn to use Stata to conduct data analysis, model estimation, diagnostic checking, and forecasting.

Prerequisites:

Students are expected to have basic knowledge of statistics, econometrics. Familiarity with the Stata software is NOT required as I will walk you through the necessary steps.

Learning Outcomes:

Upon completion of this course, students will be able to:

Understand the properties and characteristics of time series data
Choose appropriate models for time series data and make inferences based on these models
Use Stata software to estimate and diagnose time series models
Generate accurate forecasts and evaluate their performance
Apply advanced time series techniques to real-world data problems.

Final Notes and Big Thanks

The course is offered free of charge and aims to provide students with the necessary skills and knowledge to succeed in their academic and professional careers. However, students also have the option to purchase the material of the course: including complete Stata DO files, slides used in the videos, and datasets.

Purchasing these materials is entirely optional, but it provides learners with the opportunity to study at their own pace and support the creation of further content. By investing in these materials, learners can also demonstrate their appreciation for the instructor's efforts and contribute to the continued development of the course.

I am confident that the course content will provide you with valuable insights and practical skills that will help you succeed in your economic assignments, exams, thesis, and projects. I hope you find the course and material engaging and informative and that it assists you in achieving your educational goals.

Best Regards,

Juan D'Amico - JDEconomics

Tutorials Available in Stata

I there any tutorial you don't see in the list but would like to see in the future? Feel free to send me your suggestions!

Import & Generate Time Series Variables

In this first tutorial, I'll show how to import and tsset time series data in STATA.

2. Edit Economics Graphs

Produce professional economics graphs in Stata. Learn step by step how to edit your graphs!

3. AR Model Forecast

Learn how to produce out of sample forecasts using Auto-regressive models (AR).

4. Arima Models

Discover how to apply ARIMA models to real-world data in STATA, and master the skills of fitting and interpreting these models with ease.

5. ARCH & GARCH Models

Having estimated the mean equation (ARIMA) in the previous tutorial, we'll now move on to estimating the variance using an ARCH/GARCH model. > COMING SOON

6. Cointegration and Error Correction Models

Two non stationary series can have a long run equilibrium. If so, we can estimate the long run model, and the short run dynamics. How? With the error correction model.

7. Vector Autoregressive (VAR) Models

Learn how to estimate multivariate time series models. VAR models allows us to analyze how different variables interact with each other.

8. VAR Model Forecast with Confidence Band

Learn how to produce out of sample forecasts with confidence bands with VAR models in Stata. We use a money supply example.

9. Dynamic Stochastic General Equilibrium (DSGE) Models

This is a full free course where I teach you how to estimate DSGE models in Stata. DSGE models focus on economic growth and business cycles. Learn how to estimate the models used by monetary entities.

10. Calculate Inflation using the CPI Series

Learn how to calculate the 12 month and 1 month inflation rate change using the CPI series.

Purchase the full Stata course material

Are you looking to take your learning experience to the next level? Do you want to delve deeper into the concepts and techniques covered in the video? Then buying the material is the perfect choice for you!

With the slides, datasets, and complete DO files with detailed codes, you'll have everything you need to fully immerse yourself in the material. You'll be able to review the content at your own pace, reinforce your understanding of key concepts, and even apply the techniques to your own projects.

By purchasing the material, you'll also be showing your appreciation to the instructor for creating such valuable content. And, you'll be supporting further content creation, ensuring that you and other learners have access to quality educational resources in the future.

Don't miss out on this opportunity to take your learning to the next level and support the creator. Buy the material today and see the difference it can make in your understanding and skills!