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:
The Econometrics of Time Series Data
This course is part of Econometrics for Economists and Finance Practitioners Specialization
Taught in English
Some content may not be translated
Instructor: Dr Leone Leonida
Sponsored by Ukraine Response
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What you'll learn
How to estimate the various model with R
How to check that the models are statistically valid with R
How to use the various models for decision making
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There are 4 modules in this course
This week’s materials present a number of time series observations. We look at white noise, trend stationary and non-stationary time series. We explore both at real observation about the GDP and to financial markets observations, and to generated series of data. We introduce both the idea of autocorrelation function and that of partial autocorrelation function as tools to understand the degree of persistency in a series of data.
What's included
5 videos6 readings5 quizzes4 discussion prompts3 ungraded labs
This week we deal with stationary time series models. We present white noise, moving average, autoregression and autoregressive and moving average models. We describe the models and the different types of autocorrelation functions you have in each of these cases. We also discuss the problem of estimating the order of the autocorrelation and moving average models. We study the idea and the challenges raised by forecasting, and that’s raised by high persistency of the impact of shocks on the observed series.
What's included
4 videos6 readings6 quizzes2 discussion prompts3 ungraded labs
This week we consider the problems raised by non-stationarity of time series observations. We define non-stationarity of time series data, and present the tests for non-stationarity, including the challenges raised by near non-stationarity, and that of potential correlation of the estimating model when testing for non-stationarity. We present a full example to show what are the consequences in cases where we adopt the classical linear regression model when observations are non-stationary. We introduce the idea of cointegration and present introductory models to test whether the variables are cointegrated.
What's included
4 videos4 readings5 quizzes2 discussion prompts4 ungraded labs
This week’s materials discuss some stylised facts present across financial market returns, independent of the period, the financial tool and the market we study, that are volatility clustering and aggregational gaussianity. We discuss why these models, being nonlinear in nature, cannot be estimated via the classical linear regression model, and discuss and estimate some examples of autoregressive conditional heteroscedastic models. We discuss advantages and shortcomings of these models; building on the latter, we present some generalisation of the approach to generalised conditional heteroscedastic models (GARCH), GARCH-in-meena, TGARCH amd IGRACH models.
What's included
4 videos4 readings5 quizzes1 peer review3 discussion prompts4 ungraded labs
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Queen Mary University of London
Queen Mary University of London
Queen Mary University of London
Queen Mary University of London
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