• A
  • A
  • A
  • АБB
  • АБB
  • АБB
  • А
  • А
  • А
  • А
  • А
Обычная версия сайта
Бакалавриат 2017/2018


Статус: Курс обязательный (Бизнес-информатика)
Направление: 38.03.05. Бизнес-информатика
Когда читается: 2-й курс, 3 модуль
Формат изучения: без онлайн-курса
Язык: русский
Кредиты: 3

Программа дисциплины


This course covers the core set of statistical and econometric techniques as applied in empirical work with economic and business data. This is an obligatory course for the second-year students of Bachelor program “Business Informatics.” We start with an introduction to econometrics and discuss main types of data available for answering quantitative economic and business questions. Then we spend some time at summarizing the basic ideas of the theory of probability and statistics that are needed to perform descriptive statistics analysis, understand regression analysis and econometrics. After the introductory and review part, we learn how to perform simple and multiple regression analysis, create and test hypothesis on the real data, build your own models and assess their reliability.
Цель освоения дисциплины

Цель освоения дисциплины

  • to enable students to critically evaluate statistical reports and findings and learn how various statistical techniques assist in making business decisions and answering economic questions.
Планируемые результаты обучения

Планируемые результаты обучения

  • - outline the subject of Econometrics, its approach, the sources for study materials (including online ones), data, software, the course outcomes
  • - be able to use theoretical notions, concepts and interpret results using SLR model
  • - to analyze and estimate SLR model on real economic data using econometric software
  • - be able to use theoretical notions, concepts and interpret using MLR model
  • - to analyze and estimate MLR model on real economic data using econometric software
  • - be able to explain the need for variables transformations in Econometric analysis
  • - to apply variables transformations in econometric models and interpret the results
  • - be able to use theoretical notions, concepts and interpret results related to dummy variables
  • - to analyze and estimate models with dummy variables on real economic data using econometric software
  • - to analyze and estimate Panel Data models on real economic data using econometric software
  • - be able to use theoretical notions, concepts and interpret the models with Panel Data
  • - to analyze and estimate the models with Stationary and Nonstationary Time Series on real economic data using econometric software
  • - be able to use theoretical notions, concepts and interpret results on the models with Stationary and Nonstationary Time Series
  • - to analyze and estimate the models with Autocorrelated Disturbance Term on real economic data using econometric software
  • - be able to analyse reasons, consequences, methods of detection and remedial measures for the models with Autocorrelated Disturbance Term
  • - to analyze and estimate Dynamic Processes models on real economic data using econometric software
  • - be able to use theoretical notions, concepts and interpret results of modelling with Time Series Data
  • - to analyze and estimate Binary Choice Models and Limited Dependent Variable Models on real economic data using econometric software
  • - be able to apply the Binary Choice Models and Limited Dependent Variable Models
  • - to estimate economic models using maximum likelihood approach
  • - be able to define and use the maximum likelihood estimation approach
  • - to estimate the parameters of Simultaneous Equations model on real economic data using econometric software
  • - be able to use, interpret and transform the Simultaneous Equations models and the concept of identification
  • - to apply the Instrumental Variables approach in the models with stochastic explanatory variables on real economic data
  • - be able to use theoretical notions, concepts and interpret results on the topic
  • - to transform and estimate econometrics models with heteroscedasticity on real economic data
  • - be able to analyse reasons, consequences, methods of detection and remedial measures for heteroscedasticity
  • - to analyze and estimate LRM model in various specifications with real economic data using econometric software
  • - be able to choose and interpret the LRM model specification
Содержание учебной дисциплины

Содержание учебной дисциплины

  • Introduction to Econometrics
    Statistical Investigation of Economic Variables' Relationships. Relationships in the economy: examples, problems of estimation and analysis (demand functions, earnings functions, economic growth models). Economic data: cross section data, time series data, panel data. Main statistical concepts and facts used in the course. Data bases. Software. Course materials presentation.
  • Simple Linear Regression Model (SLR) with Non-stochastic Explanatory Variables. OLS estimation
    Simple Linear Regression Model: definitions and notation, Model A. SLR Model Estimation using Ordinary Least Squares (OLS). Expressions for the OLS estimators of slope coefficient and intercept: derivation and interpretation. Assumptions of the SLR models and the properties of OLS estimators. Gauss-Markov theorem (formulation). Standard deviations and standard errors of regression coefficients: derivation and interpretation. Statistical significance of OLS estimators: hypotheses testing using t-tests. Derivation and interpretation of confidence intervals. The general quality of regression: determination coefficient R2. F-statistics and F-tests. Relationship of R2 with correlation coefficients. SLR model without intercept. OLS-estimation, properties and applications.
  • Multiple Linear Regression Model (MLR): two explanatory variables and k explanatory variables
    Derivation and properties of OLS-estimators of MLR with two explanatory variables. Determination coefficient R2. Adjusted R2. Testing hypotheses using t-tests and F-tests. OLS-estimation of the model with k explanatory variables in vector-matrix form. Properties of coefficients’ estimators. F-test for groups of variables. Multicollinearity. Its consequences, detection and remedial measures. Estimation of production functions in volumes and growth rates' forms as multiple regression models. Making predictions using Multiple Linear Regression Model. Properties of predictors.
  • Variables Transformations in Regression Analysis
    Linear and Nonlinear regressions. Linearisation of non-linear functions and their estimation using Ordinary Least Squares. Disturbance term specification. Interpretation of linear, logarithmic and semi-logarithmic relationships. Estimation of functions with constant elasticity and exponential time trends. Comparison of the quality of regression relationships: linear and semi-logarithmic functions. Box-Cox transformation. Models with quadratic and interactive explanatory variables: estimation and interpretation.
  • Linear Regression Model Specification
    Consequences of Incorrect Specification. Omitting significant explanatory variable. Including unnecessary explanatory variable in the model. Monte-Carlo method in econometric analysis: general principles, areas of application and examples. Proxy Variables. Testing of linear restrictions on parameters of MLR: single and multiple restrictions, F-tests and t-tests. Role and examples of linear restrictions in economic models. Model reparametrisation: interpretation and examples. Short run and long run effects. Lagged Variables in economic models. SLR model assumptions’ violation. General principles of consequences’ analysis, detection and correction. Generalised Least Squares (GLS).
  • Heteroscedasticity
    Concept, consequences and detection of heteroscedasticity. Goldfeld-Quandt and White tests. Model correction. Logarithmic regressions. Weighted Least Squares (WLS) method as a special case of GLS. White’s heteroscedasticity-consistent standard errors. Reasons and examples of heteroscedasticity in economic models.
  • Stochastic Explanatory Variables. Measurement Errors. Instrumental Variables
    Stochastic explanatory variables in LR models. Model B assumptions. Properties of OLS-estimators and test statistics of stochastic explanatory variables’ coefficients: finite sample and asymptotic. Measurement errors: reasons and consequences. Milton Friedman's critique on consumption function estimation: Permanent income hypothesis. Instrumental variables. Using instrumental variables in M.Friedman’s consumption model and in other economic models. Asymptotic properties of IV estimators. Durbin-Wu-Hausman (DWH) test.
  • Simultaneous Equations Models
    Concept of simultaneous equations model. Exogenous and endogenous variables. Predetermined variables. The simultaneous equations bias. Inconsistency of OLS estimators. Structural and reduced forms of the model. Model of demand and supply and simple Keynesian equilibrium model as simultaneous equations models. Identification problem. Exact identification, underidentification, and overidentification. Rules of identification. Order condition. Testing exogeneity: Durbin-Wu-Hausman test. Methods of estimation. Indirect Least Squares (ILS). Instrumental Variables. Two-Stages Least Squares (TSLS). Examples of simultaneous equations models estimation in Economic Analysis.
  • Binary Choice Models, Limited Dependent Variable Models
    Linear probability model: problems of estimation. Logit-analysis. Probit-analysis. Using Maximum Likelihood for logit and probit models' estimation. Models’ interpretation and Marginal effects investigation. Examples of Binary Choice models in Economics. Censored samples. Direct and truncated estimation. Tobit-model: interpretation and ML estimation.
  • Maximum Likelihood Estimation
    The idea of maximum likelihood estimation (ML). SLR and MLR Models Estimation using ML. ML Estimators’ properties. Test statistics (z-statistics, pseudo- R2, LR-statistic) and statistical tests.
  • Modelling with Time Series Data. Dynamic Processes Models
    Time series data regressions: Model assumptions. Properties of OLS estimators. Distributed lag models: geometrically distributed lags, polynomial lags. Koyck transformation and estimation of geometrical lag’s parameters. Autoregressive Distributed Lag (ADL) models. Interpretation and asymptotic properties. Partial adjustment. Adaptive expectations. Error Correction. Vector autoregression. VAR model. Causality in Economics: Granger test.
  • Autocorrelated disturbance term
    Signs and consequences of disturbance term’s autocorrelation in LR model. Durbin-Watson d-test for first order autocorrelation. Breusch-Godfrey (BG) test of higher-order autocorrelation. Autocorrelated disturbance term and model misspecification: apparent autocorrelation. Model correction: Autoregressive transformation. Cochrane-Orcutt (CO) procedure and non-linear estimation. Autoregressive transformation and transformed’ model estimation as a special case of GLS. AR, MA, ARMA models. AR(1) and ADL(1,0) models: Common factor test and model selection. Autocorrelated disturbance term in a model with lagged dependent variable as one of the explanatory variables. Durbin h-statistic and test.
  • Time Series Econometrics: Nonstationary Time Series
    Stationary and nonstationary time series. Definitions and examples of stationary and nonstationary time series. Random walk. Drifts and trends. Consequences of nonstationarity. Spurious regressions. Detection of nonstationarity. Correlograms. Unit root tests. Akaike ans Schwarz Information Criteria. Cointegration. Fitting models with nonstationary time series. Detrending. Error Correction models.
  • Panel Data Models
    Panel data: economic examples. Unobserved heterogeneity problem. Pooled regressions. Fixed effect regressions. Within-groups regression models. First differences regression models. Least squares dummy variables (LSDV) regression models. Random effect regressions. Fixed effects or random effects: Durbin-Wu-Hausman (DWH) test. Fixed effects or pooled regression: F-test.
  • Dummy Variables
    Dummy variables in linear regression models. Reference category and dummy variables’ trap. Effects of change of the reference category. Types of dummy variables: intercept and slope dummies. Interaction dummies. Multiple sets of dummies. Chow test for structural break. Dummy group test. Dummy variables in economic models: earnings functions, production functions. Dummy variables in seasonal adjustment.
Элементы контроля

Элементы контроля

  • неблокирующий Term project
  • неблокирующий Quizzes
  • неблокирующий Final exam
Промежуточная аттестация

Промежуточная аттестация

  • Промежуточная аттестация (3 модуль)
    0.7 * Term project + 0.23 * Final exam + 0.07 * Quizzes
Список литературы

Список литературы

Рекомендуемая основная литература

  • Introduction to econometrics, Dougherty, C., 2011

Рекомендуемая дополнительная литература

  • Wooldridge, J. M. . (DE-588)131680463, (DE-576)298669293. (2006). Introductory econometrics : a modern approach / Jeffrey M. Wooldridge. Mason, Ohio [u.a.]: Thomson/South-Western. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.250894459