Delivered at:: Department of Management

Course type:: Elective course

When:: 2 year, 1, 2 module

Instructor

Zazdravnykh, Evgeny

Full 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.

Assessment Elements

Test 1
Test 2
Exam

Interim Assessment

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

Bibliography

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.

Recommended Additional Bibliography

Badi H. Baltagi. (2011). Econometrics. Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.spr.sptbec.978.3.642.20059.5
Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (2012). The Econometrics of Financial Markets. New Jersey: Princeton University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1891894

Master’s Programme 'Management and Analytics for Business'

Contacts

Qtem

Microeconometrics and Empirical Corporate Finance: Predictive and Prescriptive Analysis