The Econometrics of Time Series Data from Coursera

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

What's inside

Syllabus

Time Series Data

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.

Stationary Time Series Models

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.

Non-Stationary Time Series Models

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.

Models for Changing Volatility

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.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Strengthens existing foundation for intermediate learners who want to delve deeper into time series data analysis

Taught by Dr Leone Leonida, a recognized expert in econometrics

Develops core skills for those seeking professional skills or deep expertise in econometrics, specifically time series data analysis

Covers unique perspectives on time series data, such as the potential memory in the data and the challenges raised by non-stationary data

Requires completion of three previous courses in the specialization, which may be a barrier for some learners

Assumes learners have a strong background in econometrics

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in The Econometrics of Time Series Data with these activities:

Time Series Analysis Resources Collection

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Organizes and reviews course materials, promoting retention and understanding of key concepts.

Browse courses on Time Series Analysis

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Compile lecture notes, readings, and other course materials into a central repository.
Review materials regularly to reinforce learning and identify areas for further study.

Connect with Time Series Experts

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Establishes connections with experienced professionals, providing guidance and support throughout the learning journey.

Browse courses on Time Series Analysis

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Attend industry events and conferences related to time series analysis.
Reach out to professors, researchers, or practitioners in the field via email or LinkedIn.
Request informational interviews to gain insights and advice.

Understanding of Time Series Data

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Provides a comprehensive framework for the analysis of time series data and builds a solid foundation for further study.

View Time Series Analysis and Its Applications: With... on Amazon

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Read Chapters 1-3 to gain an understanding of the fundamentals of time series data, including stationarity and non-stationarity.
Work through practice problems to apply concepts and techniques covered in the chapters.

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Time Series Analysis Exercises

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Sharpens analytical skills through targeted exercises, reinforcing concepts and techniques.

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Attempt practice questions and exercises provided in the course materials.
Seek additional practice problems from textbooks or online resources.
Discuss solutions and approaches with peers or the instructor for feedback.

Time Series Analysis Study Group

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Fosters collaboration and peer learning, providing opportunities for students to discuss concepts and solve problems together.

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Organize regular study sessions with peers.
Review course materials, discuss concepts, and work through practice problems.
Provide feedback and support to fellow learners.

Time Series Forecasting with Python

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Develops proficiency in using Python for time series forecasting, a valuable skill for practitioners.

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Complete the tutorials provided by the instructor on the course platform.
Explore additional resources and tutorials online to deepen understanding.
Apply the learned techniques to real-world time series datasets.

Time Series Analysis Workshop

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Provides an immersive learning experience where students can engage with experts and apply their knowledge to practical scenarios.

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Attend a time series analysis workshop led by industry professionals.
Participate in hands-on exercises and case studies.
Network with other professionals in the field.

Time Series Analysis Project

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Provides an opportunity to apply knowledge and skills to a real-world problem, fostering creativity and problem-solving.

Browse courses on Time Series Analysis

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Identify a real-world time series dataset of interest.
Analyze the data, identify patterns, and develop a forecasting model.
Write a report summarizing the analysis, findings, and recommendations.

Career center

Learners who complete The Econometrics of Time Series Data will develop knowledge and skills that may be useful to these careers:

Economist

Economists use time series data to understand and forecast economic trends. As a result, this course would be helpful for Economists looking to strengthen their foundational knowledge of time series data, ARIMA models, and time series econometrics.

See salaries and explore the career path for Economist

Quantitative Analyst

Quantitative Analysts use time series data to forecast financial trends and build statistical models to inform investment decisions. Thus, this course may be useful for those looking to enter this field.

See salaries and explore the career path for Quantitative Analyst

Data Scientist

Data Scientists leverage time series data to build models that forecast future events and analyze historical trends. This course covers foundational concepts in time series analysis that could be used to enhance their toolkit.

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Financial Analyst

Financial Analysts use time series data to analyze financial trends and make investment decisions. As a result, this course may be useful for aspiring Financial Analysts.

See salaries and explore the career path for Financial Analyst

Actuary

Actuaries use time series data to assess risk and make financial projections. Therefore, this course may be helpful for Actuaries looking to advance their understanding of time series analysis.

See salaries and explore the career path for Actuary

Econometrician

Econometricians use time series data to develop and test economic models. This course may be useful for Econometricians looking to strengthen their foundational knowledge of time series econometrics.

See salaries and explore the career path for Econometrician

Statistician

Statisticians use time series data to analyze trends and make predictions. Consequently, this course may be useful for aspiring Statisticians.

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Business Analyst

Business Analysts may use time series data to analyze business trends and make recommendations. Thus, this course may be useful for those looking to enter this field.

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Risk Analyst

Risk Analysts may use time series data to assess and manage risk. Therefore, this course may be helpful for aspiring Risk Analysts.

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Market Researcher

Market Researchers may use time series data to analyze market trends and consumer behavior. Consequently, this course may be helpful for those looking to enter this field.

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Data Engineer

Data Engineers may use time series data to build and maintain data systems. Thus, this course may be useful for those looking to enter this field.

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Software Engineer

Software Engineers may use time series data to develop and maintain software systems. Consequently, this course may be helpful for those looking to enter this field.

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Operations Research Analyst

Operations Research Analysts may use time series data to analyze and improve business operations. Therefore, this course may be helpful for those looking to enter this field.

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Financial Planner

Financial Planners may use time series data to analyze investment trends and make financial recommendations. Thus, this course may be useful for aspiring Financial Planners.

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Investment Analyst

Investment Analysts may use time series data to analyze financial trends and make investment decisions. As a result, this course may be useful for those looking to enter this field.

See salaries and explore the career path for Investment Analyst