20594  ECONOMETRICS FOR BIG DATA
Department of Economics
LUCA SALA
Synchronous Blended: Lezioni erogate in modalità sincrona in aula (max 1 ora per credito online sincrona)
Mission & Content Summary
MISSION
CONTENT SUMMARY
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: DifferencesinDifferences (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) BlanchardQuah, 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.
Intended Learning Outcomes (ILO)
KNOWLEDGE AND UNDERSTANDING
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 timeseries 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.
APPLYING KNOWLEDGE AND UNDERSTANDING
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 wellfunctioning empirical macroeconometric model.
 Communicate effectively the empirical results of his/her analysis.
 Do empirical analysis in a constructive way and think critically.
Teaching methods
 Facetoface lectures
 Exercises (exercises, database, software etc.)
DETAILS
The learning experience of the course includes:
 Facetoface lectures, introducing and illustrating the main topics of the course.
 Interactive inclass discussions around stylized microeconomic and macroeconomic applications, focusing on specific aspects of their implementation and interpretation.
Assessment methods
Continuous assessment  Partial exams  General exam  


x  x 
ATTENDING AND NOT ATTENDING STUDENTS
With the purpose of measuring the acquisition of the abovementioned 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.
Teaching materials
ATTENDING AND NOT ATTENDING STUDENTS
The main course material for both attending and nonattending 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. 493572.
2. Slides and reading material (papers, book chapters) on specific topics will be made available to students on BlackBoard.
Useful Textbook References
 Angrist, J.D. and J.S. Pischke (2009), Mostly Harmless Econometrics, Princeton University Press (free eversion online)
 Angrist, J.D. and J.S. Pischke (2014), Mastering 'Metrics, Princeton University Press
 Cunningham, S. (2021), Causal Inference: The Mixtape, Yale University Press (free 2018 eversion online)
 Békés, G. and G. Kézdi (2021). Data Analysis for Business, Economics, and Policy. Cambridge University Press
 Wooldridge, J.M., Introductory Econometrics: A Modern Approach (2012 or any following edition)
Part II (Macroeconometrics)
Main References
 Sala, L., Lecture Notes on Time Series Analysis, (available on Bboard).
 Enders, W. Applied Econometric Time Series, last edition, selected chapters.
 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.