F nest. An introduction to Monte Carlo simulations and bootstrap using Stata. Financial Intermediation Network of European Studies

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1 F nest Financial Intermediation Network of European Studies S U M M E R S C H O O L An introduction to Monte Carlo simulations and bootstrap using Stata Dr. Giovanni Cerulli September 2014 University of Rome III, Italy

2 Lecturer Dr. Giovanni Cerulli CERIS - CNR National Research Council of Italy Institute for Economic Research on Firms and Growth Short bio Dr. Giovanni Cerulli is researcher at the Ceris-CNR, unit of Rome, Italy. He received a degree in Statistics and Economics and a PhD in Economics at the University of Rome La Sapienza. His research deals with the analysis of the effects of public policies based on counterfactual econometric modelling. Dr. Cerulli is also Editor-in-chief of the International Journal of Computational Economics and Econometrics. When September 2014 Where University of Rome III, Faculty of Economics, Via S. D Amico 77, Rome, Italy Overall aims and purpose This course will provide participants with the essential tools for performing Monte Carlo simulations and bootstrap techniques with Stata. Instructional examples with real and simulated datasets will be used. Learning outcomes After attending the course, the participant will be able to: 1. setting up and managing a Monte Carlo simulation experiment; 2. applying bootstrap techniques, using Stata. In particular, he will understand the Stata environment to deal with both subjects, and will be familiar with basic Stata programming and intuitive commands syntax. Pre-requisites Basic knowledge of descriptive and inferential statistics. Basic knowledge of Stata. 2

3 Readings Monte Carlo Adkins, L.C. and Gade, M.N. (2012), Monte Carlo Experiments Using Stata: A Primer With Examples, Manuscript, March 18. Baum, C.F. (2007), Monte Carlo Simulation in Stata, Faculty Micro Resource Center, Boston College, July. Cameron, A.C. and Trivedi, P.K. (2009), Microeconometrics using Stata, Stata Press. Ch. 4. Bootstrap Bradley Efron and Robert J. Tibshirani (1993), An Introduction to the Bootstrap, Boca Raton: Chapman & Hall. Brownstone, David and Robert Valetta (2001), The Bootstrap and Multiple Imputations: Harnessing Increased Computing Power for Improved Statistical Tests, Journal of Economic Perspectives, 15(4), Cameron, A. C. and P. K. Trivedi (2005), Microeconometrics: Methods and Applications, Cambridge University Press. Sections 7.8 and Chapter 11. Cameron, A. C. and P. K. Trivedi (2009), Microeconometrics using Stata, Stata Press. Chapter 13. Horowitz, Joel L. (1999) The Bootstrap, In: Handbook of Econometrics, Vol. 5. Fees* Ph.D students Young Researchers (up to 35 years old) Mature Researchers & Professionals 300 euro 450 euro 800 euro How to register and pay To register, please send an to finest@uniroma3.it by attaching the registration form (please, DOWNLOAD THE FORM HERE). Registration is on a first come, first served basis and SPACE IS LIMITED! After we will have received your registration form, we will confirm you a place in the selected course. Thereafter, you will have to finalize your registration within a week by paying fees via bank transfer. Please use the following information: Bank Name: Banca Unicredit Spa. Bank Address: Agenzia 108, Via Ostiense 105, Rome, Italy Account Holder: Università degli Studi Roma Tre, Dip. di Studi Aziendali 3

4 Account number: IBAN: IT05T SWIFT/BIC code: UNCRITM1B58 Reasons for the payment to be specified: DSTA - FINEST Summer School + Name and Surname + Title of the course Notes There is a limited number of places available at computer lab. The course will only be offered if a minimum of six people will enrol. Courses are offered to FINEST members. If you are not a member, fees include the FINEST admission fees. Fees also include full lunch during the course, not the accommodation. We have arranged a special allotment of rooms and rates for the courses with some hotels at walking distance from the University of Rome III to facilitate your travel arrangements. Any applicant booking more than a course in 2014 will receive: o a 10% discount on the overall amount for two courses o a 20% discount on the overall amount for three courses 4

5 Program Day 1 Session I: Monte Carlo Simulations in Econometrics What are Monte Carlo Simulations (MCS) and why are they useful? Stata 13 tools for conducting MCS: an overview The concept of Data Generating Process (DGP) The Monte Carlo Simulation s protocol Stata basic syntax Global and local macro Looping Program definition Programming Monte Carlo Simulation: Basics The command simulate The command postfile Basic protocol for postfile and loop constructs Example: the Classical Normal Linear Regression and Confidence Intervals Using simulate: overview A simple example of the use of simulate Session II: Monte Carlo applications Defining important statistics to assess simulation results Comparing the simulated and theoretical t-distribution Assessing test size Assessing test power Regression examples Inconsistency of OLS under measurement error Simulating a model with endogenous regressors Day 2 Session III: An Introduction to bootstrap The bootstrap: an overview When is it convenient to use the bootstrap? Bootstrap re-sampling scheme Bootstrap standard errors Bootstrap algorithm Bootstrap confidence intervals Bootstrap Normal Bootstrap Percentile Bootstrap Bias-corrected Bootstrap Bias-corrected accelerated Bootstrap hypothesis tests Session IV Bootstrap implementation using Stata Stata 13 implementation of bootstrap Bootstrap standard errors as option The Stata command: bootstrap Bootstrap all types of confidence-intervals: an example Clustered bootstrap Final observations 5