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COMPUTATIONAL STATISTICS

International Teaching COMPUTATIONAL STATISTICS

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8861200015
DEPARTMENT OF ECONOMICS AND STATISTICS
Corso di Dottorato (D.M.226/2021)
ECONOMICS AND POLICY ANALYSIS OF MARKETS AND FIRMS
2023/2024



YEAR OF COURSE 1
YEAR OF DIDACTIC SYSTEM 2023
FULL ACADEMIC YEAR
CFUHOURSACTIVITY
210LESSONS
VINCENZO CANDILA T Curriculum
Objectives
KNOWLEDGE AND UNDERSTANDING

THE AIM IS TO PROVIDE STUDENTS WITH THE TOOLS TO UNDERSTAND AND APPLY COMPUTATIONAL STATISTICAL METHODS FOR BOTH CONSTRAINED AND UNCONSTRAINED MAXIMUM LIKELIHOOD ESTIMATION, FOR THE CREATION OF MONTE CARLO SIMULATIONS, AND FOR THE USE OF BOOTSTRAP METHODS.

APPLICATION OF KNOWLEDGE AND UNDERSTANDING

THE GOAL IS TO EQUIP STUDENTS WITH THE ABILITY TO OPTIMIZE MAXIMUM LIKELIHOOD ESTIMATORS, CREATE COMPLEX MONTE CARLO ANALYSES, AND UTILIZE BOOTSTRAP TECHNIQUES TO DERIVE STANDARD ERRORS AND CONFIDENCE INTERVALS.
Prerequisites
KNOWLEDGE OF BASIC CONCEPTS IN DESCRIPTIVE AND INFERENTIAL STATISTICS.
Contents
THE COURSE INTRODUCES THE BASICS OF COMPUTATIONAL METHODS FOR STATISTICS. THE R PROGRAMMING LANGUAGE WILL BE USED AS THE MAIN WORKING TOOL. IN PARTICULAR, THE COURSE WILL COVER: (I) CONSTRAINED AND UNCONSTRAINED OPTIMIZATION OF MAXIMUM LIKELIHOOD FUNCTIONS; (II) MONTE CARLO SIMULATIONS; (III) AN INTRODUCTION TO BOOTSTRAP PROCEDURES.
Teaching Methods
LECTURES AND COMPUTER-BASED EXERCISES.
Verification of learning
THE ACHIEVEMENT OF TEACHING OBJECTIVES IS CERTIFIED THROUGH THE SUCCESSFUL COMPLETION OF AN EXAM BASED ON THE DISCUSSION OF A PROJECT WORK.
Texts
MARIA L. RIZZO, 2008, STATISTICAL COMPUTING WITH R, CHAPMAN & HALL/CRC, FIRST EDITION
More Information
ADDITIONAL TEACHING MATERIALS (DATA, SOFTWARE, SLIDES) WILL BE DISTRIBUTED THROUGH THE TEACHER'S WEBSITE.
  BETA VERSION Data source ESSE3