B2222 - ECONOMETRICS FOR DECISION MAKING

Learning outcomes

The course aims to provide a methodological and applied overview of the modern econometric methods for decision-making in a number of contexts (investment, financing, innovating, etc.). A central role is played by panel data, in which at least two dimensions of observations are present: classically, we can have cross-sectional time series data (CSTS); recently, the multiple aspects of economic decisions emerge from multilevel data, such as firms within industries within regions within countries, observed over time. A pervasive aspect of the methods will concern the measurement of complex phenomena, such as heterogeneity, innovation and intangibles, uncertainty and disagreement.

Course contents

The course provides an overview of methodological and empirical analyses that can be performed on panel data, beyond simple FE and RE estimates. The preliminary and crucial step in any empirical research on panel data, whether longitudinal, time-series-cross-section, or multilevel, is to study the nature and relevance of the components that influence the variability of the variables. Each panel dataset can be considered as a set of grouped data, whether they are temporal observations nested within individuals or individuals nested within groups and supergroups. The key steps in guiding modeling strategies focus on decomposing total variability into variances between and within clusters and assessing whether there are relevant common factors within clusters and, in the case of temporal observations, whether these are stationary.

Some panel datasets cannot be analyzed with fixed effects but require multilevel models, in which it is essential to consider the different levels of aggregation present in the data.

Other analyses may require dynamic models with heterogeneous slopes. Meta-analysis can be used to study the relationship between covariates and effect size.

The course clearly requires the necessary prerequisities, especially for Erasmus students:

1. At this page [https://corsi.unibo.it/magistrale/AppliedEconomicsMarkets/insegnamenti/piano/2025/6756/000/000/2025] take a look at the content of courses such as Econometrics for Individual Data and Time Series Econometrics. At this page [https://corsi.unibo.it/2cycle/lmec/course-structure-diagram/piano/2024/5977/000/000/2024] take a look at the content of courses such as Econometric Methos, Microeconometrics and Macroeconometrics. In a nutshell, Wooldridge J.M. 2020 Introductory Econometrics. A Modern Approach, Cengage, 7th Edition condenses the minimum requirement.

2. A knowledge, at least basic, of STATA software is also required. Have a look at Baum, C. F. (2006) An Introduction to Modern Econometrics Using Stata, Stata Press; Cox N. J. (2001) Speaking Stata: How to repeat yourself without going mad, The Stata Journal, 1, Number 1, pp. 86–97.

Readings/Bibliography

Discussion papers, commented notes & slides, stata programmes and datasets will be available on the VIRTUALE platform and explained during the lectures.

Some references are

Snijders, T. and Bosker, R. (2012) Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Analysis, 2nd edition, Sage

Hansen, B. C. (2022) Econometrics, Princeton University Press, ch. 17.

Hanga, M. and Geyer-Klingeberga, J. and Rathgebera, A. W. and Stöcklb, S. (2018) Measurement matters—A meta-study of the determinants of corporate capital structure, The Quarterly Review of Economics and Finance, 68, 211-225

Yin, S. and Jia, F. and Chen, L. and Wang, Q. (2023) Circular economy practices and sustainable performance: A meta-analysis, Resources, Conservation and Recycling, 190, 106838

Teaching methods

During the lectures, the methodologies will be accompanied by empirical applications based on the econometric software Stata (available via CAMPUS licence and student university credentials [https://www.unibo.it/secure/software-stata/]).

You will be involved in the implementation and discussion of empirical analyses, so please bring your laptop with you. Attendance at lectures is highly recommended.

Assessment methods

During the course, you will be assigned a homework (to be completed individually or in groups of up to three people) to be posted on VIRTUALE by the deadline. This will be a research question related to the topics covered in class (I will provide the data and questions), to be developed empirically, explaining your choices. The assignment contributes 30% to the overall assessment.

The final exam, which is compulsory and requires registration on AlmaEsami, accounts for the remaining 70%. It consists of an individual presentation in class (maximum duration 15 minutes) of an empirical analysis conducted on panel data. You can decide together with me which topic to explore in depth or agree that I will assign you a research question. The assessment focuses on your ability to analyse the topic in a theoretical and applied manner.

Non-attending students must take a written exam.

The overall assessment scale is as follows:

30 cum laude: flawless work

28-30: excellent work, demonstrating in-depth and comprehensive knowledge of the topic, as well as excellent comprehension and analytical skills.

24-27: good work demonstrating an appreciable degree of knowledge of the subject.

18-23: presence of disorder, with theoretical and methodological inaccuracies.

<18: insufficient work

Teaching tools

Theoretical lectures are combined with practical sessions during which you will receive the necessary guidance to carry out your empirical analyses. The data sets and programming files needed to perfom the applied analyses will be provided during the lectures. The material will be made available on the platform Virtuale. A virtual room on TEAMS will be available in case you are unable to physically attend one lecture and to communicate via chat.

Software STATA: go to https://www.unibo.it/secure/software-stata/

Office hours

See the website of Maria Elena Bontempi