# 7 semester

 1 Title of the course Statistical methods in economics 2 Year and speciality 4, Mathematics (economic activity) 3 Semester 7 4 Number of credits 5 5 Lecturer Bakhtin Victor Ivanovich 6 Objectives of the course Enhancement of the mathematical scope, acquaintance with new methods of mathematical reasoning, possession of methods of statistical analysis of economic problems   As a result of the course study a student have to be able: — to choose an adequate statistical model describing a certain economic object or phenomenon; — to perform analysis of an economic object or forecasting of an economic process within the framework of the chosen model by means of the “Statistica” computer package; — to construct the point and interval estimates for parameters; — to verify statistical hypotheses; — to construct tests for statistical check of hypotheses by methods of multivariate statistical analysis; — to perform analysis and make forecasts for time series. 7 Prerequisites Linear algebra, functional analysis, probability theory 8 Contents of the course 3. Multivariate normal distribution 3.1. The definition and properties of multivariate normal distribution. 3.2. Mahalanobis metric. 3.3. A conditional normal distribution. 3.4. Rotations and projections of normal distributions. 3.5. The sample mean and covariances of the multivariate normal distribution. 3.6. The maximum likelihood estimates for the parameters of normal distributions. 3.7. Sample correlations.   4. Linear regression 4.1. The linear regression of random variables. 4.2. The multiple and partial correlations. 4.3. Multivariate sample estimates. 4.4. The least squares method. 4.5. The general multiple linear regression model.   5. Selected problems of multivariate statistical analysis 5.1. The method of principal components. 5.2. The discriminant and cluster analyses. 5.3. Hotelling’s T2-statistics. 5.4. Comparison-of-means tests.   6. Time series 6.1. Time series. 6.2. Estimates for the time series parameters. 6.3. Autoregressions. 6.4. Moving averages. 6.5. The autoregression and integrated moving average time series. 6.6. Time series with trend. 9 Recommended literature Basic. 1. Bakhtin, V.I. An introduction to applied statistics. Lecture course. Part I. Mathematical statistics. — Minsk, BGU, 2011 (in Russian). 2. Bakhtin, V.I. An introduction to applied statistics. Lecture course. Part II. The methods of applied statistics. — Minsk, BGU, 2012 (in Russian). 3. Kharin, Yu.S.; Zhuk, E.E. Mathematical and applied statistics. — Minsk, BGU, 2005 (in Russian) 4. Aivazian, S.A.; Mkhitarian, V.S. Applied statistics. Elements of econometrics. V. 1, 2. Moscow, 2001 (in Russian).   Auxiliary 1. Borovkov, A.A. Mathematical statistics. — Moscow, Nauka, 1984 (in Russian). 2. Lagutin, M.B. Visualizing mathematical statistics. — Moscow, Binom, 2009 (in Russian). 3. Mardas, A.N. Econometrics. — St. Petersburg, 2004 (in Russian). 4. Orlov, A.I. Econometrics. — Moscow, Ekzamen, 2004 (in Russian). 5. Mynbayev, K.; Lemas, A. Econometrics. — Almaty, 2004 (in Russian).   The full-text lectures are available at http://elib.bsu.by/handle/123456789/12993 10 Teaching methods Lectures, exercises, laboratory works, supervised independent work Examples of laboratory works are available at 11 Language Russian 12 Conditions (requirements), running control laboratory works 13 Form of assessment Exam