Download Introductory Econometrics for Business Studies Bocconi University

Introductory Econometrics for Business Studies
Bocconi University
PhD in Business Administration and Management
Syllabus, A.Y. 2016/17
Professors:
Chiara Gigliarano
(chiara.gigliarano(at)unibocconi.it)
Alessandro Bucciol
(alessandro.bucciol(at)univr.it)
Office hours: TBA (Email)
Summary and aims: The class provides an introduction to the statistical tools needed for
inference on observational data. The first part of the class will be taught by Chiara Gigliarano
and is aimed at reviewing the principles of probability and estimation theory. The second part of
the class will be taught by Alessandro Bucciol and introduces ordinary least squares estimation.
Pre-requisite: None
Course requirements: The assessment of the class will be entirely based on a sit in exam.
Course: 1st year Ph.D. course.
Reading Material: The lectures will not follow any textbook closely, but will generally follow
the subject matter and level of: (i) Greene, W.H., “Econometric Analysis,” Prentice
Hall; (ii) Wooldridge, J. M., “Introductory Econometrics: A Modern Approach,” SouthWestern College Pub; (iii) Verbeek, M., “A Guide to Modern Econometrics”, Wiley.
Course Secretary: Marialuisa Ambrosini.
Part 1: Principles of Probability and Estimation
Instructor: Chiara Gigliarano
1. UNIVARIATE AND MULTIVARIATE DISTRIBUTIONS
•
Univariate distributions
a. Discrete and continuous distributions (recall). Quantiles. Skewness and Kurtosis.
b. Some noteworthy distribution: binomial, uniform, normal, log-normal, exponential.
c. Distributions derived from the normal distribution: Chi-square, Student.
d. Examples and applications for each family of distribution.
•
Bivariate and multivariate distributions
a. Review of basic matrix calculus.
b. Covariance, Correlation and independence. Examples.
c. Bivariate and multivariate normal. Marginal and conditional distributions from the
multivariate normal random vectors. Linear and quadratic transformation of normal
random vectors.
2. ESTIMATION
•
Elements of Point Estimation Theory. Estimators and their properties: unbiasedness,
asymptotic properties. Examples and applications.
REFERENCES
•
Greene, W.H., “Econometric Analysis,” Prentice Hall. [Appendices A,B,C,D]
•
Wooldridge, J. M., “Introductory Econometrics: A Modern Approach,” South-Western
College Pub. [Appendices B, C, D]
Part 2: Introduction to OLS
Instructor: Alessandro Bucciol
1. BASICS OF OLS
•
What is Econometrics?
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Univariate and multivariate regressions
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Marginal effects and elasticities
2. DEALING WITH OLS
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Goodness of fit statistics
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Properties of the estimator
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Linear and quadratic transformation of normal random vectors.
a. Unbiasedness
b. Efficiency
c. Consistency
d. Asymptotic normality
•
Hypothesis testing
a. Critical value and p-value
b. Confidence interval
c. t tests for single hypothesis
d. F tests for multiple hypotheses
Empirical applications and exercises in preparation for the exam will be discussed together
with each topic.
REFERENCE
•
Verbeek, M., “A Guide to Modern Econometrics,” Wiley [Chapter 2]