Start Date: 02/23/2020
Course Type: Common Course |
Course Link: https://www.coursera.org/learn/erasmus-econometrics
Explore 1600+ online courses from top universities. Join Coursera today to learn data science, programming, business strategy, and more.Welcome! Do you wish to know how to analyze and solve business and economic questions with data analysis tools? Then Econometrics by Erasmus University Rotterdam is the right course for you, as you learn how to translate data into models to make forecasts and to support decision making. * What do I learn? When you know econometrics, you are able to translate data into models to make forecasts and to support decision making in a wide variety of fields, ranging from macroeconomics to finance and marketing. Our course starts with introductory lectures on simple and multiple regression, followed by topics of special interest to deal with model specification, endogenous variables, binary choice data, and time series data. You learn these key topics in econometrics by watching the videos with in-video quizzes and by making post-video training exercises. * Do I need prior knowledge? The course is suitable for (advanced undergraduate) students in economics, finance, business, engineering, and data analysis, as well as for those who work in these fields. The course requires some basics of matrices, probability, and statistics, which are reviewed in the Building Blocks module. If you are searching for a MOOC on econometrics of a more introductory nature that needs less background in mathematics, you may be interested in the Coursera course “Enjoyable Econometrics” that is also from Erasmus University Rotterdam. * What literature can I consult to support my studies? You can follow the MOOC without studying additional sources. Further reading of the discussed topics (including the Building Blocks) is provided in the textbook that we wrote and on which the MOOC is based: Econometric Methods with Applications in Business and Economics, Oxford University Press. The connection between the MOOC modules and the book chapters is shown in the Course Guide – Further Information – How can I continue my studies. * Will there be teaching assistants active to guide me through the course? Staff and PhD students of our Econometric Institute will provide guidance in January and February of each year. In other periods, we provide only elementary guidance. We always advise you to connect with fellow learners of this course to discuss topics and exercises. * How will I get a certificate? To gain the certificate of this course, you are asked to make six Test Exercises (one per module) and a Case Project. Further, you perform peer-reviewing activities of the work of three of your fellow learners of this MOOC. You gain the certificate if you pass all seven assignments. Have a nice journey into the world of Econometrics! The Econometrics team
By studying this module, you get the required background on matrices, probability and statistics. Each topic is illustrated with simple examples, and you get hands-on training by doing the training exercise that concludes each lecture. Three lectures on matrices show you the basic terminology and properties of matrices, including transpose, trace, rank, inverse, and positive definiteness. Two lectures on probability teach you the basics of univariate and multivariate probability distributions, especially the normal and associated distributions, including mean, variance, and covariance. Finally, two lectures on statistics present you with the basic ideas of statistical inference, in particular parameter estimation and testing, including the use of matrix methods and probability methods.
Econometrics: Methods and Applications This course gives you an introduction to the most important pillar of economics: Economics as a science. We will learn how to define and measure things like prices, wages, interest rates, exchange rates, and economic growth. You will also learn how to measure things like competition, government intervention, and welfare payments. We’ll also cover things like unemployment, inequality, and the environment. These and other topics are all related to the process of economic analysis and decision-making. Besides, you’ll learn how to use statistical measures and methods to make your economic models more realistic. After completing this course, you will be able to use statistical measures of prices and wages to answer questions like: How much do we pay for our goods and services? What is the interest rate paid by firms? How much does government intervention work? How much do taxes pay for public services? Do taxes increase or decrease as a function of exchange rates? How much do subsidies and tax credits pay for public services? How does the environment affect economic growth? What is the role of government in economic management?Are taxes and exchange rates really such powerful determinants of economic outcomes? What is government intervention? How do we measure economic growth? Do taxes and exchange rates really affect economic outcomes? Do government programs and subsidies pay for public services?Are taxes and exchange rates really such powerful determinants of economic outcomes? How do we measure economic
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Center for Operations Research and Econometrics | The current research areas in econometrics are financial econometrics, time series econometrics and Bayesian methods. |
Econometrics | The basic tool for econometrics is the multiple linear regression model. Econometric theory uses statistical theory and mathematical statistics to evaluate and develop econometric methods. Econometricians try to find estimators that have desirable statistical properties including unbiasedness, efficiency, and consistency. "Applied econometrics" uses theoretical econometrics and real-world data for assessing economic theories, developing econometric models, analyzing economic history, and forecasting. |
Single-equation methods (econometrics) | A variety of methods are used in econometrics to estimate models consisting of a single equation. The oldest and still the most commonly used is the ordinary least squares method used to estimate linear regressions. |
Econometrics | Econometrics may use standard statistical models to study economic questions, but most often they are with observational data, rather than in controlled experiments. In this, the design of observational studies in econometrics is similar to the design of studies in other observational disciplines, such as astronomy, epidemiology, sociology and political science. Analysis of data from an observational study is guided by the study protocol, although exploratory data analysis may be useful for generating new hypotheses. Economics often analyzes systems of equations and inequalities, such as supply and demand hypothesized to be in equilibrium. Consequently, the field of econometrics has developed methods for identification and estimation of simultaneous-equation models. These methods are analogous to methods used in other areas of science, such as the field of system identification in systems analysis and control theory. Such methods may allow researchers to estimate models and investigate their empirical consequences, without directly manipulating the system. |
Econometrics | Econometrics is the application of statistical methods to economic data and is described as the branch of economics that aims to give empirical content to economic relations. More precisely, it is "the quantitative analysis of actual economic phenomena based on the concurrent development of theory and observation, related by appropriate methods of inference." An introductory economics textbook describes econometrics as allowing economists "to sift through mountains of data to extract simple relationships." The first known use of the term "econometrics" (in cognate form) was by Polish economist Paweł Ciompa in 1910. Ragnar Frisch is credited with coining the term in the sense in which it is used today. |
Econometrics | The main journals that publish work in econometrics are "Econometrica", the "Journal of Econometrics", the "Review of Economics and Statistics", "Econometric Theory", the "Journal of Applied Econometrics", "Econometric Reviews", the "Econometrics Journal", "Applied Econometrics and International Development", and the "Journal of Business & Economic Statistics". |
Econometrics | "Applied econometrics" uses theoretical econometrics and real-world data for assessing economic theories, developing econometric models, analyzing economic history, and forecasting. |
Financial econometrics | Financial econometrics is the subject of research that has been defined as the application of statistical methods to financial market data. Financial econometrics is a branch of financial economics, in the field of economics. Areas of study include capital markets |
Teun Kloek | Teunis (Teun) Kloek (born 1934) is a Dutch economist and Emeritus Professor of Econometrics at the Erasmus Universiteit Rotterdam. His research interests centered on econometric methods and their applications, especially nonparametric and robust methods in econometrics. |
Center for Operations Research and Econometrics | Among the major CORE contributions in econometrics are Bayesian estimation of simultaneous equations systems (Bayesian inference methods are widely used in research at CORE) and the concepts of weak and strong exogeneity used in statistical inference. Other important research fields include financial econometrics with such topics as the microstructure of financial markets or volatility models and structural econometrics. |
Econometrics | One of the fundamental statistical methods used by econometricians is regression analysis. Regression methods are important in econometrics because economists typically cannot use controlled experiments. Econometricians often seek illuminating natural experiments in the absence of evidence from controlled experiments. Observational data may be subject to omitted-variable bias and a list of other problems that must be addressed using causal analysis of simultaneous-equation models. |
Spatial econometrics | Spatial econometrics is the field where spatial analysis and econometrics intersect. In general, econometrics differs from other branches of statistics in focusing on theoretical models, whose parameters are estimated using regression analysis. Spatial econometrics is a refinement of this, where either the theoretical model involves interactions between different entities, or the data observations are not truly independent. Thus, models incorporating spatial auto-correlation or neighborhood effects can be estimated using spatial econometric methods. Such models are common in regional science, real estate economics, and education economics. |
Center for Operations Research and Econometrics | Econometrics research at CORE is aimed at the development of quantitative models as well as statistical and computational methods applied to treating economic data. |
Financial econometrics | Premier-quality journals which publish financial econometrics research include Econometrica, Journal of Econometrics and Journal of Business & Economic Statistics. The Journal of Financial Econometrics has an exclusive focus on financial econometrics. It is edited by Federico Bandi and Andrew Patton, and it has a close relationship with SoFiE. |
Methodology of econometrics | Econometrics may use standard statistical models to study economic questions, but most often they are with observational data, rather than in controlled experiments. In this, the design of observational studies in econometrics is similar to the design of studies in other observational disciplines, such as astronomy, epidemiology, sociology and political science. Analysis of data from an observational study is guided by the study protocol, although exploratory data analysis may by useful for generating new hypotheses. Economics often analyzes systems of equations and inequalities, such as supply and demand hypothesized to be in equilibrium. Consequently, the field of econometrics has developed methods for identification and estimation of simultaneous-equation models. These methods are analogous to methods used in other areas of science, such as the field of system identification in systems analysis and control theory. Such methods may allow researchers to estimate models and investigate their empirical consequences, without directly manipulating the system. |
Econometrics | The basic tool for econometrics is the multiple linear regression model. In modern econometrics, other statistical tools are frequently used, but linear regression is still the most frequently used starting point for an analysis. Estimating a linear regression on two variables can be visualized as fitting a line through data points representing paired values of the independent and dependent variables. |
Financial econometrics | The Nobel Memorial Prize in Economic Sciences has been awarded for significant contribution to financial econometrics; in 2003 to Robert F. Engle "for methods of analyzing economic time series with time-varying volatility" and Clive Granger "for methods of analyzing economic time series with common trends" and in 2013 to Eugene Fama, Lars Peter Hansen and Robert J. Shiller "for their empirical analysis of asset prices". Other highly influential researchers include Torben G. Andersen, Tim Bollerslev and Neil Shephard. |
Econometrics | A simple example of a relationship in econometrics from the field of labor economics is: |
Financial econometrics | The Society for Financial Econometrics (SoFiE) is a global network of academics and practitioners dedicated to sharing research and ideas in the fast-growing field of financial econometrics. It is an independent non-profit membership organization, committed to promoting and expanding research and education by organizing and sponsoring conferences, programs and activities at the intersection of finance and econometrics, including links to macroeconomic fundamentals. SoFiE was co-founded by Robert F. Engle and Eric Ghysels. |
Bayesian econometrics | where formula_12 and which is the centerpiece of Bayesian statistics and econometrics. It has the following components: |