Time series analysis: 

EViews Course

About the EViews Course

This course is designed for economics students who want to learn how to use EViews software for econometric analysis, time series analysis, and forecasting. The course covers a wide range of topics in applied econometrics, including model specification, estimation, hypothesis testing, and forecasting. Additionally, the course emphasizes hands-on experience with EViews, a popular software package used in econometric analysis.

The first part of the course introduces basic econometric concepts, such as regression analysis, hypothesis testing, and model specification. In this section, students will learn how to specify, estimate, and interpret econometric models using EViews. Additionally, this section covers techniques for testing and correcting autocorrelation and heteroscedasticity in time series data.

The second part of the course covers time series analysis, including stationary and non-stationary time series, unit root testing, and ARIMA models. In this section, students will learn how to use EViews to estimate and diagnose different types of time series models, such as autoregressive (AR), moving average (MA), and ARMA models. This section concludes with models to estimate the variance, such as GARCH and ARCH models.

After discussing stationary models, we will proceed to non-stationary models with long-run relationships. When two (or more) variables are non-stationary, we could encounter a spurious regression. However, if the residuals resulting from that regression are stationary, we are in the presence of two cointegrated variables. We will cover cointegration with the Engle and Granger method, estimate long-run and short-run models, and discuss the interpretation of the error correction term.

The final part of the course covers forecasting techniques, including out-of-sample forecasting, forecasting evaluation, and forecasting with multiple models. We will focus on Vector Autoregressive (VAR) models and structural vector autoregressive (SVAR) models. Students will learn how to use EViews to develop forecasting models and evaluate their performance using statistical metrics.

In addition to theoretical concepts and practical applications, this course will provide hands-on experience with real-world data. The examples used in the course will involve monetary policy, fiscal policy, money growth, GDP growth, and forecasting variables, such as the consumer price index.

Students will have the opportunity to work with actual datasets, allowing them to gain experience in data manipulation, cleaning, and analysis. They will also develop their research questions and hypotheses, giving them the chance to apply their knowledge meaningfully.

By the end of the course, students will have the skills necessary to conduct their econometric analysis and forecasting using EViews with real-world data. They will be able to critically evaluate existing literature and apply econometric techniques to answer significant economic questions. Overall, this course will prepare students for careers in economics, finance, and other fields that require advanced data analysis skills.

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 EViews Workfiles, 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 EViews

Is 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!

Download EViews for FREE

Looking for EViews Student Lite, the free version for students? You can download it directly from the official EViews website. Click here to access the official download page and get started with EViews for your academic needs.

EViews Student Lite offers essential econometric and statistical analysis tools, making it a valuable resource for students in economics, finance, and related fields. Download it today and unlock the power of EViews for your academic projects.

Note: Ensure that you're using the official EViews website to download the software to guarantee its authenticity and legality.


New to EViews? Learn how to download real economic data from online sources, and upload it to EViews.

2. Linear Regression

In this tutorial I teach you how to estimate a simple linear regression and understand the output. We fit a real example using Argentina and Brazil GDP. Finally, I explain why the regression is spurious and what are the signs to detect it.

3. Stationarity : Unit Roots

Learn about stationarity in EViews. We will do a graph and correlogram analysis. Next, we finish with some formal tests: Augmented Dickey Fuller, Phillips Perron and KPSS test.

4. Unit Root Tests with Breakpoints

We replicate Perron's paper and learn how to identify structural breaks in our data.

5. HP Filter

Learn how to use the Hodrick-Prescott (HP) filter to  decompose a time series into cyclical and trend components. The cyclical component is stationary and will show the percentage deviation from the long run trend. 

6. ARIMA Models

Learn how to forecast ARIMA models. ARIMA are univariate models, where past information of the variable will help us model how it will behave in the future. We cover the Box-Jenkins 3 step methodology.

7. ARIMA Model Forecast With Confidence Bands

Now that you know how to forecast ARIMA models, let's add confidence bands to the out of sample forecast! Finally, I teach you how to edit the graph to make it look professional.

8. ARCH Models

Traditional Econometric models assume that the variance is constant, however, there are periods of high volatility that can influence our variable. Learn about volatility clustering, arch terms, and model the variance of a series. We use Toronto Stock Exchange (TSX) as example.

9. ARCH Models Misspecifications

Learn what are the three things you should check when estimating ARCH models. 

10. GARCH Models

GARCH models are an extension of ARCH models. GARCH models tend to be more parsimonious and are a good alternative to high ARCH models. We use Microsoft stock as example.

11. Cointegration & Error Correction Model

Two non-stationary variables can have a long run equilibrium. Learn how to verify so using the Engle and Granger method. Next, lets estimate the short run and long run model. We discuss the error correction term.

12. Vector Autoregressive (VAR) Models

Vector Autoregression (VAR) models, are one of the most popular models for multivariate time series analysis. Learn how to estimate them! In our example, we will replicate Stock and Watson (2001) paper. 

13. Structural Vector Autoregressive (SVAR) Models

SVAR stand for structural vector autoregression models and they imply imposing a restriction on the response matrix based on economic theory. In this case, we will use long run restrictions based on long run money neutrality. We replicate Ender &Lee (1997) paper.

Sign up to my newsletter