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The Econometrics of Time Series Data
  • Level Professional
  • Duration 31 hours
  • Course by Queen Mary University of London
  • Offered by

About

In this course, you will look at models and approaches that are designed to deal with challenges raised by time series data. The discussion covers the motivation for the use of particular models and the description of the characteristics of time series data, with a special attention raised to the potential memory. You will: – Discuss time series models, that refer to data that have been collected over a period on one or more variables for the same individual. – Explore both on stationary and non-stationary time series models, as well as the difference between the non-stationary data and the trend-stationary processes – Consider the problems that may occur with non-stationarity data. – Discover the applications of time series models that are of use when we want to model the GDP growth of an economy, and to test for the Purchasing Power Parity Hypothesis. – Explore the idea of forecasting using econometric models. – Discuss different criteria to decide how good your in-sample and out-of-sample forecasts are. – Explore the problem raised by data where the variance is non-constant, and models for volatility forecasting. – Estimate ARCH(p) and GARCH(p,q) models for volatility with real financial market data and present how to extend these models to the mean of the time series via Garch-in-mean. It is recommended that you have completed and understood the previous three courses in this Specialisation: The Classical Linear Regression Model, Hypothesis Testing in Econometrics and Topics in Applied Econometrics. By the end of this course, you will be able to: – Manipulate and plot the different types of data – Estimate and interpret the empirical autocorrelation function – Estimate and compare models for stationary series – Test for non-stationarity of time series data – Estimate and interpret cointegration equations – Perform in-sample and out-of-sample forecasting exercises – Estimate and compare models for changing volatility

Modules

Time Series Data

    • 2 Videos
    • 1 Readings
    • 2 Discussion
    • 1 Lab
    • 2 Assignment
Show info about module content

2 Assignment

  • Understanding the the Role of History
  • Understanding Difference in Time Series Data

2 Discussions

  • The Role of History
  • Difference in Time Series Data

1 Labs

  • Generate Different Time Series Data Using R

2 Videos

  • Welcome to Use and Applications of Econometrics
  • Time Series Observations

1 Readings

  • Time Series Data and Correlation through Time

Detrending

    • 1 Videos
    • 1 Readings
    • 1 Discussion
    • 1 Lab
    • 1 Assignment
Show info about module content

1 Assignment

  • Understanding Detrending

1 Discussions

  • Wrong Detrending of a Random Walk and of a Deterministic Trend Series

1 Labs

  • Detrending with R

1 Videos

  • Detrending Time Series Data

1 Readings

  • The Problem of Detrending

Autocorrelation Function

    • 1 Videos
    • 3 Readings
Show info about module content

1 Videos

  • Memory and the Autocorrelation Function

3 Readings

  • Theory of Autocorrelation Function
  • Building the Autocorrelation Function
  • Testing for Statistical Significance of the ACF

Partial Autocorrelation Function

    • 1 Videos
    • 1 Readings
    • 1 Discussion
    • 1 Lab
    • 2 Assignment
Show info about module content

2 Assignment

  • Understanding ACG and PACF for Different Economies
  • Knowledge Check: Time Series Data

1 Discussions

  • ACG and PACF for Different Economies, and Memory

1 Labs

  • Estimate the Autocorrelation Function with R

1 Videos

  • Holding Constant the Intermediate Coefficients

1 Readings

  • The Theory Behind Partial Autocorrelation Function

Stationary Time Series Models

    • 1 Videos
    • 1 Readings
    • 1 Lab
    • 1 Assignment
Show info about module content

1 Assignment

  • MA(2) Process

1 Labs

  • Using R for Stationary Time Series Models

1 Videos

  • Models for Time Series

1 Readings

  • Modelling Variables Over Time

Examples of Time Series Processes

    • 1 Videos
    • 1 Readings
    • 1 Discussion
    • 1 Lab
    • 2 Assignment
Show info about module content

2 Assignment

  • Understanding the Information Criteria to Use
  • Autocorrelated Residuals

1 Discussions

  • What Information Criteria to Use?

1 Labs

  • Using R for Time Series Processes

1 Videos

  • Examples of Stationary Time Series Processes

1 Readings

  • From the Theoretical ACF to the Specification of the Empirical Model

Forecasting

    • 1 Videos
    • 3 Readings
    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Understanding the Limits of Forecasting

1 Discussions

  • Limits of Forecasting

1 Videos

  • How Can We Use Econometrics for Forecasting?

3 Readings

  • Forecasting with Econometrics
  • Producing Forecasts with Econometrics
  • Are Our Forecasts Accurate?

The Impact of Shocks on the Behaviour of the Series

    • 1 Videos
    • 1 Readings
    • 1 Lab
    • 2 Assignment
Show info about module content

2 Assignment

  • Understanding the Impact of Shocks on the Behaviour of the Series
  • Knowledge Check: Stationary Time Series Models

1 Labs

  • Generating Data Using R

1 Videos

  • The Impact of Shocks on the Dynamics of the Series

1 Readings

  • A Special Case in Time Series Modelling

Non-Stationary Time Series

    • 1 Videos
    • 1 Readings
    • 1 Discussion
    • 1 Lab
    • 1 Assignment
Show info about module content

1 Assignment

  • Understanding Non-Stationary Data

1 Discussions

  • Non-Stationary and Stationary

1 Labs

  • Non-Stationary Time Series with R

1 Videos

  • The Issue of Memory in the Data

1 Readings

  • Why is it Important to Deal with Non-Stationary Data?

Testing for Unit Root in Time Series Data

    • 1 Videos
    • 1 Readings
    • 1 Lab
    • 1 Assignment
Show info about module content

1 Assignment

  • The Dickey and Fuller Test at Work

1 Labs

  • Testing for Stationarity with R

1 Videos

  • Test for Unit Root

1 Readings

  • The Dickey and Fuller Test

Spurious Regressions

    • 1 Videos
    • 1 Readings
    • 1 Discussion
    • 1 Lab
    • 1 Assignment
Show info about module content

1 Assignment

  • Understanding the Problems with Spurious Regressions

1 Discussions

  • Problems with Spurious Regressions

1 Labs

  • Estimating a Regression Model with R

1 Videos

  • Spurious Regressions

1 Readings

  • Why are Spurious Regression Problematic?

Cointegration

    • 1 Videos
    • 1 Readings
    • 1 Lab
    • 2 Assignment
Show info about module content

2 Assignment

  • Understanding Cointegration
  • Knowledge Check: Non-Stationary Time Series Models

1 Labs

  • Hypothesis Testing with R

1 Videos

  • Cointegrated Variables

1 Readings

  • Cointegration as Long Run Relationship

Stylised Facts in Financial Markets

    • 1 Videos
    • 1 Readings
    • 1 Discussion
    • 1 Lab
    • 1 Assignment
Show info about module content

1 Assignment

  • Understanding the Value of Modelling the Variance

1 Discussions

  • Modelling the Variance

1 Labs

  • An Exercise with R

1 Videos

  • Changing Volatility

1 Readings

  • Some Characteristics of Financial Markets

Modelling the Variance and Testing for the ARCH Effect

    • 1 Videos
    • 1 Readings
    • 1 Discussion
    • 1 Lab
    • 1 Assignment
Show info about module content

1 Assignment

  • Understanding Problems with ARCH(q) Models

1 Discussions

  • Problems with ARCH(q) Models

1 Labs

  • Estimating the ARCH with R

1 Videos

  • Modelling Volatility ARCH Models

1 Readings

  • Modelling and Testing for the ARCH Effect

Generalised ARCH (GARCH) Models

    • 1 Videos
    • 1 Readings
    • 1 Discussion
    • 1 Lab
    • 1 Assignment
Show info about module content

1 Assignment

  • Understanding GARCH and ARCH

1 Discussions

  • GARCH versus ARCH

1 Labs

  • Estimating the GARCH with R

1 Videos

  • Modelling Volatility GARCH Models

1 Readings

  • From ARCH models to Generalised ARCH model: GARCH

Extensions of the GARCH Models

    • 1 Videos
    • 1 Readings
    • 1 PeerReview
    • 1 Lab
    • 2 Assignment
Show info about module content

2 Assignment

  • Understanding the Characteristics of the Data
  • Knowledge Check: Models for Changing Volatility

1 Peer Review

  • Drawing Conclusions on the Fit ARCH(1), ARCH(2) and GARCH(1,1) Models

1 Labs

  • Assessing the Fit of ARCH(1), ARCH(2) and GARCH(1,1) Models

1 Videos

  • Modelling Volatility Extensions of the GARCH Models

1 Readings

  • Non-Linear Models and GARCH

Auto Summary

"The Econometrics of Time Series Data" is a comprehensive course in the Business & Management domain, offered by Coursera. Taught by an expert instructor, it delves into models and approaches for time series data, focusing on stationary and non-stationary models, forecasting, and volatility. This professional-level course spans 1860 minutes and offers both Starter and Professional subscription options. Ideal for those with prior knowledge in econometrics, it equips learners with skills to manipulate, estimate, and forecast time series data, making it perfect for professionals aiming to enhance their analytical capabilities in economics and finance.

Dr Leone Leonida
Instructor

Dr Leone Leonida