Ugrás a tartalomhoz

ECONOMIC STATISTICS

Anikó Bíró

ELTE Közgazdaságtudományi Tanszék, MTA Közgazdaságtudományi Intézet, Balassi Kiadó

1. week: Introduction – course requirements, connections to other subjects. About economic data – examples for economic questions, data sources on the internet. Basic computer knowledge (Excel, Power Point, Word) – Excel worksheets, computations in Excel.Textbook chapter 22. week: Data types: cross sectional, time series, panel. Descriptive statistics – graphical methods, histogram, point diagram. Indicators: mean, mode, percentiles, variation, skewness. Descriptive statistics with Excel, Analysis ToolPak.Textbook chapter 23. week: Correlation – definition, interpretation of square of correlation. Properties of correlation. Correlation and causality? Correlation and regression – different approach. Introduction to simple regression – writing up the model, estimation. Calculation of correlation, OLS estimation with Excel. Textbook chapter 34. week: Simple regression – goodness of fit, definition and interpretation of R-squared. Nonlinearity, logarithmic form (elasticity). Influencing factors of estimation precision, confidence interval. Practicing.Textbook chapter 45. week: Hypothesis testing: null and alternative hypothesis, procedure of hypothesis testing, t-test, p-value. Connection between hypothesis testing and confidence interval. F-test (intuitively). Examples. Summary. 1st exam.Textbook chapter 56. week: Discussion of 1st exam. Multivariate regression – more explanatory variables. Estimation, interpretation of coefficients, confidence interval, hypothesis testing. Practicing examples: model of GDP growth, regression of production costs of electricity firms. Simple simulation with Excel. Textbook chapter 67. week: Bias due to omitted variables. Problems of “too many” and “too few” regressors. Multicollinearity: definition, symptoms, possible solutions. Binary variables, introduction: different intercept, different means of subgroups. Textbook chapters 6, 78. week: Binary variables – interactions: different intercept and different slope across groups. Examples: housing price regression (properties of the real estate as binary regressors), wage regression (gender discrimination). Binary dependent variable: limitations of OLS. Summary. 2nd exam. Textbook chapter 79. week: Discussion of 2nd exam. Basics of time series analysis – comparison with cross sectional analysis. Distributed lag model – model specification, interpretation of coefficients and their sum. Lag length selection. Introduction to EViews.Textbook chapter 810. week: Univariate time series analysis – graphical analysis with examples. Trend. Autocorrelation, autocorrelation function, and its interpretation. AR(1) model. Stacionarity based on the estimated coefficient in the AR(1) model. Practicing examples: time series of export and public debt.Textbook chapter 911. week: AR(p) models – basic and modified forms. Unit root, based on the modified form. Seasonality: definition and methods of seasonal adjustment. Unit root test: Dickey-Fuller test. Lag length selection in AR(p) models. Textbook chapter 912. week: Time series regression – basic and modified forms of ADL(p,q) models, interpretation of coefficients (temporary effect, long run multiplicator). First differencing of unit root processes. Definition and testing of cointegration (Engle-Granger test). Error correction model: specification, interpretation of coefficients.Textbook chapter 1013. week: Summary, outlook. Further topics: Volatility analysis of asset prices – why important, examples. Granger-causality: definition and testing. VAR model – advantages and disadvantages. Macroeconoic example for VAR models (RMPY).Textbook chapter 11

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01_week_economic_statistics.pdf
01_week_economic_statistics-ppt.pdf
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DC Metadata
Title:
ECONOMIC STATISTICS
Authors:
Anikó Bíró
Publisher:
ELTE Közgazdaságtudományi Tanszék, MTA Közgazdaságtudományi Intézet, Balassi Kiadó
Contributors:
ELTE TáTK Közgazdaságtudományi Tanszéke, MTA Közgazdaságtudományi Intézet, Balassi Kiadó
Sources:
eredeti tananyag
Language
English
Subjects
cross sectional, time series, panel, descriptive statistics-graphical methods, histogram, point diagram, indicators: mean, mode, percentiles, variation, skewness, correlation, simple regression, hypothesis testing, multivariate regression, binary variables, AR(p) models, time series regression
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