Course 2023-2024 a.y.

Classes: 23 (I sem.)

Instructors:
Class 23: LUCA SALA

The main goal of this course is to give students a working knowledge of the most used econometric techniques. The key concepts of statistical theory underlying each method are covered, but special emphasis is placed on implementation of each method in actual applications. The course is divided in two parts. The first, deals with regression and causal inference methods used in the analysis of cross-sectional and longitudinal data, typically used in micro-econometrics (where the focus is on the individual behavior of individuals, households, firms and so on). The second, deals with time series data and methods, typically used in macro-economic applications (where the focus is on the interaction of macroeconomic variables). As observational data, most commonly used in non-experimental sciences such as economics, hardly tell the researcher what is the effect of a certain treatment variable on a given outcome variable of interest, economists have devised a variety of approaches to address questions of cause-and-effect among economic variables both in microeconomics and macroeconomics. The unifying theme of the two parts of the course is a focus on understanding causality.

Part I (Microeconometrics):

• Introduction to Microeconometrics: (Description, Prediction, and Causal inference)  
• Potential Outcomes Framework (POF) I: POF basics (Ideal RCTs, Missing outcomes, Selection)            
• Regression Fundamentals I: Regressions as best predictors (loss functions; types of prediction; identification vs. estimation; parametric vs. nonparametric regressions)
• Regression Fundamentals II: Conditional expectation function and mean regression  
• Regression Fundamentals III: More on regression specifications (dummy variables on the right and on the left; saturated models)        
• Regression Fundamentals IV (if time permits): Limited dependent variables models (LDV)  
• Potential Outcomes Framework II: POF meets regression                           
• Selection on Observables: Conditional independence and other solutions based on observables
• Selection on Unobservables I: "Classic" Instrumental Variables (IV)  
• Selection on Unobservables II: Differences-in-Differences (DID)
• Potential Outcomes Framework III: POF with imperfect compliance
• Selection on Unobservables III: LATE IV      
• Selection on Unobservables IV: Regression Discontinuity Designs (Sharp and Fuzzy RRDs)  

 

Part II (Macroeconometrics):

• Univariate models.
• Why multivariate models.
• Simultaneous equation bias. The problem of identification.
• The Sims critique to old Keynesian macroeconometrics.
• VAR models.
• Granger causality (application: Sims, 1972).
• Structural VAR and identification (applications: a) Sims, 1980, b) Blanchard-Quah, 1989 and Gali, 1999. c) news shocks).
• External instruments.
• A first pass on Big Data: principal components and factor models.
• Using the Kalman filter to build the likelihood function.

At the end of the course student will be able to...

Part I (Microeconometrics):

• Define key concepts in econometrics, for instance “econometric causality;” “fundamental problem of causal inference;” “average treatment effect;” “instrumental variable;” “loss function.”
• Explain key differences and links between distinct but related econometric concepts, for instance “identification and statistical inference;” “potential, realized, and counterfactual outcomes;” “average treatment effect, average treatment effect on the treated, and average treatment effect on the untreated;” “classical instrumental variables estimator and local average treatment effect estimator;” “sharp and fuzzy regression discontinuity designs;” “parametric, semiparametric, and nonparametric specification of an econometric model.”  
• For each causal method covered during the course, describe the roles of the key assumptions underlying the method in yielding identification of the causal effect parameter of interest. Distinguish untestable assumptions from testable ones, and describe how to test the latter. Discuss the main consequence/s of the failure of each assumption, illustrating with specific examples or applications.
• For each causal method covered during the course, describe the basic data requirements for application of each method and discuss advantages and disadvantages of each method.

 

Part II (Macroeconometrics):

• Be familiar with the main concepts and tools of time series analysis and use them in other contexts.
• Understand a vast majority of the scientific literature on time-series and macroeconometrics.
• Identify what are the modelling assumptions underlying any structural macroeconometric model.
• Translate the main assumptions in economic theories into restrictions on the statistical model.

At the end of the course student will be able to...

Part I (Microeconometrics):

• Design a simple randomized experiment to quantify the average causal effect of a treatment variable on an outcome variable of interest.
• Given a specified microeconomic application, a data set, and a causal parameter of interest, select the most appropriate microeconometric method among those covered in class.
• Implement the chosen method to quantify the causal parameter of interest and test hypotheses about the parameter’s true value.
• Evaluate the causal effect(s) of a policy intervention or program.

 

Part II (Macroeconometrics):

• Perform empirical analysis to uncover the effects of shocks in the economy.
• Design a well-functioning empirical macroeconometric model.
• Communicate effectively the empirical results of his/her analysis.
• Do empirical analysis in a constructive way and think critically.

• Face-to-face lectures
• Exercises (exercises, database, software etc.)

The learning experience of the course includes:

• Face-to-face lectures, introducing and illustrating the main topics of the course.
• Interactive in-class discussions around stylized microeconomic and macroeconomic applications, focusing on specific aspects of their implementation and interpretation.

	Continuous assessment	Partial exams	General exam
Written individual exam (traditional/online)		x	x

With the purpose of measuring the acquisition of the above-mentioned learning outcomes, the assessment process is based on a written examination (100% of the final grade). 

The written exam consists of exercises and/or open questions, aimed at assessing students' ability to: 

 

• Apply the analytical tools illustrated during the course.  
• Understand the research papers discussed during the course 
• Be able to interpret econometric results and understand their economic meaning
• Translate economic assumption into restrictions on econometric models. 
• Understand and explain the distinctions and relationships between prediction and causal inference, both abstractly and within specific applications.
• Understand and explain the distinction between identification of a causal effect (a population concept) and statistical inference on the effect (a sample concept), as well as the role of data in each of these.

 

Students can take a partial written exam that covers the Microeconometrics part and complete a partial written exam at the end of the course, covering the Macroeconometrics part. In this case the weight is 50% for the partial exam (Microeconometrics) and 50% for the partial exam at the end of the course (Macroeconometrics). Alternatively, students can take a final written exam, on both parts, that accounts for 100% of the final grade. 

The main course material for both attending and non-attending students is:

 

Part I (Microeconometrics)

Main References

1. Roberts, M.R. and T.M. Whited (2013), "Endogeneity in Empirical Corporate Finance", in G.M. Constantinides, M. Harris and R. Stulz, eds, Handbook of the Economics of Finance, Vol. 2A, Elsevier, chapter 7, pp. 493-572.

2. Slides and reading material (papers, book chapters) on specific topics will be made available to students on BlackBoard.

 

Useful Textbook References 

1. Angrist, J.D. and J.-S. Pischke (2009), Mostly Harmless Econometrics, Princeton University Press (free e-version online)
2. Angrist, J.D. and J.-S. Pischke (2014), Mastering 'Metrics, Princeton University Press
3. Cunningham, S. (2021), Causal Inference: The Mixtape, Yale University Press (free 2018 e-version online)
4. Békés, G. and G. Kézdi (2021). Data Analysis for Business, Economics, and Policy. Cambridge University Press 
5. Wooldridge, J.M., Introductory Econometrics: A Modern Approach (2012 or any following edition)

 

 

 

Part II (Macroeconometrics)

Main References

1. Sala, L., Lecture Notes on Time Series Analysis, (available on Bboard).
2. Enders, W. Applied Econometric Time Series, last edition, selected chapters.
3. Hamilton, J. H., Time Series Analysis, Princeton University Press, 1994, selected chapters.

 

The slides of the course, additional readings and support material will be uploaded to the Bboard platform of the course.