Microeconometrics and Empirical Corporate Finance: Predictive and Prescriptive Analysis
- 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)
- 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
- Linear Regression BasicsData sources. OLS. Gauss-Markov Assumptions. Heteroscedasticity and robust, clustered robust standard errors. Multicollinearity and VIF test, correlations. Endogeneity and Instrumental Variables.
- Linear Regression Basics: SimulationSimulation. Limitations and problems in OLS.
- Instrumental VariablesTheory of IV approach. Strong and weak instruments. Examples from Finance.
- Time SeriesARMA, 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. BasicsFixed and Random Effect Models. Tests. Examples in Finance. Limitations and problems.
- Linear Panel Models. ExtensionsInstrumental variable approach in linear panel models. Short and long panels. Dynamic panels.
- Non-linear Panel ModelsPanel logit and probit models. Limitations and problems. Examples in Finance.
- 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.
- 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