## Эконометрика (Econometrics)

Start Date: 02/23/2020

 Course Type: Common Course

Explore 1600+ online courses from top universities. Join Coursera today to learn data science, programming, business strategy, and more. Эконометрика – наука, позволяющая исследовать закономерности в реальных данных. К концу курса мы научимся отвечать на два вопроса. Как одна переменная, y, зависит от другой переменной, x? Как спрогнозировать переменную y? Мы будем подробно изучать линейные регрессионные модели, рассмотрим наиболее частые отклонения от предпосылок классической линейной регрессии. Изучим базовые модели (логит и пробит) для качественных зависимых переменных. Наряду с теоретической основой мы будем работать с реальными данными, используя статистический пакет R. Необходимые знания: Теория вероятностей и математическая статистика. Линейная алгебра опционально. Появились технические трудности? Обращайтесь на адрес: coursera@hse.ru

#### Course Syllabus

Материалы всех недель доступны сразу, но в системе указаны рекомендуемые сроки выполнения всех заданий. Форум также открыт для Ваших вопросов. Ознакомьтесь с правилами оценивания и проведения контрольных работ. Обращаем Ваше внимание на то, что тест можно делать сколько угодно раз, но на каждые 8 часов даются только три попытки. Если Вы начали выполнять тест, то время на его выполнение неограничено. Ссылки на pdf-файлы лекций и скрипты для всех недель находятся в разделе Ценные ресурсы так же, как и правки к видео-фрагментам :) В первых же тестах есть вопросы, где необходимо использовать R, поэтому надеемся, что Вы уже успели поставить его на свои компьютеры. Если нет, то в Ценных ресурсах лежат подробные инструкции по установке софта. После установки не забудьте открыть в R-Studio (File -> Open file) файл "install_all.R" и запустите его, чтобы поставить все необходимые нам в ближайшее время пакеты. Выделите все строчки и запустите с помощью сочетания Ctrl + Enter на Windows или Cmd + Enter на Mac. Чтобы познакомиться с R поближе, в данной виньетке представлено краткое и наглядное описание интерфейса R-studio, шоткатов и других полезных вещей. Успехов! #### Course Introduction

Эконометрика – наука, позволяющая исследовать закономерности в реальных данных. К концу курса мы нау

#### Course Tag

Statistics Time Series Econometrics R Programming

#### Related Wiki Topic

Article Example
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.
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.
Econometrics A simple example of a relationship in econometrics from the field of labor economics is:
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 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.
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.
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
Bayesian econometrics Bayesian econometrics is a branch of econometrics which applies Bayesian principles to economic modelling. Bayesianism is based on a degree-of-belief interpretation of probability, as opposed to a relative-frequency interpretation.
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.
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.
Bayesian econometrics Since the beginning of the 21st century, research in Bayesian econometrics has concentrated on:
Center for Operations Research and Econometrics The current research areas in econometrics are financial econometrics, time series econometrics and Bayesian methods.
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:
Journal of Econometrics The journal also publishes a supplement to the Journal of Econometrics which is called "Annals of Econometrics". Each issue of the Annals includes a collection of papers on a single topic selected by the editor of the issue.
Financial econometrics , financial institutions, corporate finance and corporate governance. Topics often revolve around asset valuation of individual stocks, bonds, derivatives, currencies and other financial instruments. Financial econometrics is different from other forms of econometrics because the emphasis is usually on analyzing the prices of financial assets traded at competitive, liquid markets.
Journal of Econometrics The Journal of Econometrics is a scholarly journal in econometrics. It was first published in 1973. Its current editors are A. Ronald Gallant, John Geweke, Cheng Hsiao, and Peter M. Robinson.
Bayesian econometrics The ideas underlying Bayesian statistics were developed by Rev. Thomas Bayes during the 18th century and later expanded by Pierre-Simon Laplace. As early as 1950, the potential of the Bayesian inference in econometrics was recognized by Jacob Marschak. The Bayesian approach was first applied to econometrics in the early 1960s by W. D. Fisher, Jacques Drèze, Clifford Hildreth, Thomas J. Rothenberg, George Tiao, and Arnold Zellner. The central motivation behind these early endeavors in Bayesian econometrics was the combination of the parameter estimators with available uncertain information on the model parameters that was not included in a given model formulation. From the mid-1960s to the mid-1970s, the reformulation of econometric techniques along Bayesian principles under the traditional structural approach dominated the research agenda, with Zellner's "An Introduction to Bayesian Inference in Econometrics" in 1971 as one of its highlights, and thus closely followed the work of frequentist econometrics. Therein, the main technical issues were the difficulty of specifying prior densities without losing either economic interpretation or mathematical tractability and the difficulty of integral calculation in the context of density functions. The result of the Bayesian reformulation program was to highlight the fragility of structural models to uncertain specification. This fragility came to motivate the work of Edward Leamer, who emphatically criticized modelers' tendency to indulge in "post-data model construction" and consequently developed a method of economic modelling based on the selection of regression models according to the types of prior density specification in order to identify the prior structures underlying modelers' working rules in model selection explicitly. Bayesian econometrics also became attractive to Christopher Sims' attempt to move from structural modeling to VAR modeling due to its explicit probability specification of parameter restrictions. Driven by the rapid growth of computing capacities from the mid-1980s on, the application of Markov chain Monte Carlo simulation to statistical and econometric models, first performed in the early 1990s, enabled Bayesian analysis to drastically increase its influence in economics and 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.