This Specialisation provides rigorous training in econometric methods, via the exploration of theoretical concepts, real-data examples and the application of econometric techniques to industry relevant questions.
The methodologies in this Specialisation are an important part of an informed economics and financial decision-making process. You will learn:
- How to test economics and finance theories, as well as hypotheses on the relationships between variables. - Study the behaviour of prices, returns, growth and unemployment. - Effectively analyse the impact of macroeconomic changes on economic growth and performance, and forecast the future values of economic variables. Through deep exploration of the modern techniques, applications and possibilities made available by the latest advances in econometrics, you will be ready to use these successfully and navigate the associated risks. This Specialisation is for anyone who finds that econometric methods are the main framework of reference in their daily activities as well as researchers and policy makers.
Econometrics for Economists and Finance Practitioners
Queen Mary University of London via Coursera Specialization
Overview
Syllabus
Course 1: The Classical Linear Regression Model
- Offered by Queen Mary University of London. In this course, you will discover the type of questions that econometrics can answer, and the ... Enroll for free.
Course 2: Hypotheses Testing in Econometrics
- Offered by Queen Mary University of London. In this course, you will learn why it is rational to use the parameters recovered under the ... Enroll for free.
Course 3: Topics in Applied Econometrics
- Offered by Queen Mary University of London. In this course, you will discover models and approaches that are designed to deal with ... Enroll for free.
Course 4: The Econometrics of Time Series Data
- Offered by Queen Mary University of London. In this course, you will look at models and approaches that are designed to deal with challenges ... Enroll for free.
- Offered by Queen Mary University of London. In this course, you will discover the type of questions that econometrics can answer, and the ... Enroll for free.
Course 2: Hypotheses Testing in Econometrics
- Offered by Queen Mary University of London. In this course, you will learn why it is rational to use the parameters recovered under the ... Enroll for free.
Course 3: Topics in Applied Econometrics
- Offered by Queen Mary University of London. In this course, you will discover models and approaches that are designed to deal with ... Enroll for free.
Course 4: The Econometrics of Time Series Data
- Offered by Queen Mary University of London. In this course, you will look at models and approaches that are designed to deal with challenges ... Enroll for free.
Courses
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In this course, you will learn why it is rational to use the parameters recovered under the Classical Linear Regression Model for hypothesis testing in uncertain contexts. You will: – Develop your knowledge of the statistical properties of the OLS estimator as you see whether key assumptions work. – Learn that the OLS estimator has some desirable statistical properties, which are the basis of an approach for hypothesis testing to aid rational decision making. – Examine the concept of null hypothesis and alternative hypothesis, before exploring a statistic and a distribution under the null hypothesis, as well as a rule for deciding which hypothesis is more likely to hold true. – Discover what happens to the decision-making framework if some assumptions of the CLRM are violated, as you explore diagnostic testing. – Learn the steps involved to detect violations, the consequences upon the OLS estimator, and the techniques that must be adopted to address these problems. Before starting this course, it is expected that you have an understanding of some basic statistics, including mean, variance, skewness and kurtosis. It is also recommended that you have completed and understood the previous course in this Specialisation: The Classical Linear Regression model. By the end of this course, you will be able to: – Explain what hypothesis testing is – Explain why the OLS is a rational approach to hypothesis testing – Perform hypothesis testing for single and multiple hypothesis – Explain the idea of diagnostic testing – Perform hypothesis testing for single and multiple hypothesis with R – Identify and resolve problems raised by identification of parameters.
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In this course, you will discover models and approaches that are designed to deal with challenges raised by the empirical econometric modelling and particular types of data. You will: – Explore the motivations of each approach by means of graphs, preliminary statistics and presentation of economic theories – Discuss the problem of identification of the parameters, and how to address this problem by modelling simultaneous equations and causality in economics. – Examine the key features of panel data, and highlight the advantages and disadvantages of working with panel data rather than other structures of data. – Learn how to choose what econometric specification to adopt by introducing the test for poolability and the Hausman tests. – Discuss models for probability that are used where the variable under investigation is qualitative, and needs to be treated with a different approach. – Learn how to apply this approach to building an Early Warning system to forecast systemic banking crises using data from the World Bank. It is recommended that you have completed and understood the previous two courses in this Specialisation: The Classical Linear Regression Model and Hypothesis Testing in Econometrics. By the end of this course, you will be able to: – Respond appropriately to issues raised by some feature of the data – Resolve address problems raised by identification and causality – Resolve problems raised by simultaneous equation and instrumental variables models – Resolve problems raised by longitudinal data – Resolve problems raised by probability models – Manipulate and plot the different types of data.
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In this course, you will discover the type of questions that econometrics can answer, and the different types of data you might use: time series, cross-sectional, and longitudinal data. During the course you will: – Learn to use the Classical Linear Regression Model (CLRM) as well as the Ordinary Least Squares (OLS) estimator, as you discuss the assumptions needed for the OLS to deliver true regression parameters. – Look at cases with only one independent variable for one dependent variable, before progressing to regression analysis by generalising the bivariate model to multiple regression. – Explore different model-building philosophies, with particular focus on the general-to-specific approach, and learn how to use goodness-of-fit statistics as the measures of “how well your model explains variations in the dependent variable”. Throughout this course, you will see examples to help clarify which kind of relationship is of interest, and how we can interpret it. You will also have the opportunity to apply your learning to estimating the Capital Asset Pricing Model using real data with R. The course is for beginners, so little prior knowledge is required, but you will benefit from an ability to graph two variables in the xy framework, an understanding of basic algebra and taking derivatives. Knowledge of matrix algebra is not a requirement but will also provide you with an advantage. By the end of this course, you will be able to: – Describe the problems that econometrics can help addressing and the type of data that should be used – Explain why some hypotheses are needed for the approach to produce an estimate – Calculate the coefficients of interest in the classical linear regression model – Interpret the estimated parameters and goodness of fit statistics – Estimate single and multiple linear regression models with R.
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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
Taught by
Dr Leone Leonida
