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Regular version of the site
Master 2019/2020

## Microeconometrics and Empirical Corporate Finance: Predictive and Prescriptive Analysis

Area of studies: Management
When: 2 year, 1, 2 module
Mode of studies: offline
Instructors: Evgeny Zazdravnykh
Master’s programme: Management and Analytics for Business
Language: English
ECTS credits: 3

### Course Syllabus

#### Abstract

This course provides students with skills in basic microeconometrics with applications in empirical corporate finance. Students will learn the limitations of the most widely used methods and should be able to apply them for analytical exercises. The course is oriented for prospective doctoral students.

#### Learning Objectives

• To be able to estimate microeconometrics models using STATA
• To be able to find data for the econometric analysis
• To be able to read and understand econometric reports (empirical papers)

#### Expected Learning Outcomes

• be able to use OLS regression in STATA
• be able to interpret OLS coefficients
• be able to simulate OLS models in STATA
• be able to detect potential problems in the regression model using simulation approach
• to know IV approach theory
• be able to explain reasons to use a variable as an instrument
• be able to verify whether an instrument weak or strong
• be able to import time series data in STATA
• be able to run OLS regressions for time-series data
• be able to forecast time-series in STATA
• be able to predict binary outcome variable
• be able to interpret outputs for binary regression models
• be able to estimate marginal effects
• be able to import panel data in STATA
• be able to estimate FE and RE models
• be able to choose between Pooled OLS, RE, FE models
• be able to explain reasons to use instruments for panel regression models
• be able to detect whether an instrument weak or strong for panel regression models
• be able to estimate panel probit models
• be able to evaluate the goodness of fit for panel probit models

#### Course Contents

• Linear Regression Basics
Data sources. OLS. Gauss-Markov Assumptions. Heteroscedasticity and robust, clustered robust standard errors. Multicollinearity and VIF test, correlations. Endogeneity and Instrumental Variables.
• Linear Regression Basics: Simulation
Simulation. Limitations and problems in OLS.
• Instrumental Variables
Theory of IV approach. Strong and weak instruments. Examples from Finance.
• Time Series
ARMA, ARIMA models. Autocorrelation and test for autocorrelation. Multivariate regression models in time series.
• Binary outcome models (Discrete choice)
Probit and logit. Model diagnostics and coefficient interpretation. Ordered and multinominal probit/logit.
• Linear Panel Models. Basics
Fixed and Random Effect Models. Tests. Examples in Finance. Limitations and problems.
• Linear Panel Models. Extensions
Instrumental variable approach in linear panel models. Short and long panels. Dynamic panels.
• Non-linear Panel Models
Panel logit and probit models. Limitations and problems. Examples in Finance.

• Test 1
• Test 2
• Exam

#### Interim Assessment

• Interim assessment (2 module)
0.5 * Exam + 0.25 * Test 1 + 0.25 * Test 2

#### Recommended Core Bibliography

• Levendis, J. D. (2018). Time Series Econometrics : Learning Through Replication. Cham, Switzerland: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2016053
• Statistics and Causality : Methods for Applied Empirical Research, edited by Wolfgang Wiedermann, and Eye, Alexander von, John Wiley & Sons, Incorporated, 2016. ProQuest Ebook Central, https://ebookcentral.proquest.com/lib/hselibrary-ebooks/detail.action?docID=4530803.