QMUL - The Econometrics of Time Series Data
- Offered byCoursera
The Econometrics of Time Series Data at Coursera Overview
Duration | 31 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
The Econometrics of Time Series Data at Coursera Highlights
- Earn a Certificate upon completion from Queen Mary University of London
The Econometrics of Time Series Data at Coursera Course details
- 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
- 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
The Econometrics of Time Series Data at Coursera Curriculum
Time Series Data
Welcome to Use and Applications of Econometrics
Time Series Observations
Detrending Time Series Data
Memory and the Autocorrelation Function
Holding Constant the Intermediate Coefficients
Time Series Data and Correlation through Time
The Problem of Detrending
Theory of Autocorrelation Function
Building the Autocorrelation Function
Testing for Statistical Significance of the ACF
The Theory Behind Partial Autocorrelation Function
Understanding the the Role of History
Understanding Difference in Time Series Data
Understanding Detrending
Understanding ACG and PACF for Different Economies
Knowledge Check: Time Series Data
Stationary Time Series Models
Models for Time Series
Examples of Stationary Time Series Processes
How Can We Use Econometrics for Forecasting?
The Impact of Shocks on the Dynamics of the Series
Modelling Variables Over Time
From the Theoretical ACF to the Specification of the Empirical Model
Forecasting with Econometrics
Producing Forecasts with Econometrics
Are Our Forecasts Accurate?
A Special Case in Time Series Modelling
MA(2) Process
Understanding the Information Criteria to Use
Autocorrelated Residuals
Understanding the Limits of Forecasting
Understanding the Impact of Shocks on the Behaviour of the Series
Knowledge Check: Stationary Time Series Models
Non-Stationary Time Series Models
The Issue of Memory in the Data
Test for Unit Root
Spurious Regressions
Cointegrated Variables
Why is it Important to Deal with Non-Stationary Data?
The Dickey and Fuller Test
Why are Spurious Regression Problematic?
Cointegration as Long Run Relationship
Understanding Non-Stationary Data
The Dickey and Fuller Test at Work
Understanding the Problems with Spurious Regressions
Understanding Cointegration
Knowledge Check: Non-Stationary Time Series Models
Models for Changing Volatility
Changing Volatility
Modelling Volatility ARCH Models
Modelling Volatility GARCH Models
Modelling Volatility Extensions of the GARCH Models
Some Characteristics of Financial Markets
Modelling and Testing for the ARCH Effect
From ARCH models to Generalised ARCH model: GARCH
Non-Linear Models and GARCH
Understanding the Value of Modelling the Variance
Understanding Problems with ARCH(q) Models
Understanding GARCH and ARCH
Understanding the Characteristics of the Data
Knowledge Check: Models for Changing Volatility
The Econometrics of Time Series Data at Coursera Admission Process
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