International Teaching | REGRESSION MODELS
International Teaching REGRESSION MODELS
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cod. 8861200013
REGRESSION MODELS
| 8861200013 | |
| DEPARTMENT OF ECONOMICS AND STATISTICS | |
| Corso di Dottorato (D.M.226/2021) | |
| ECONOMICS AND POLICY ANALYSIS OF MARKETS AND FIRMS | |
| 2022/2023 |
| OBBLIGATORIO | |
| YEAR OF COURSE 1 | |
| YEAR OF DIDACTIC SYSTEM 2022 | |
| FULL ACADEMIC YEAR |
| SSD | CFU | HOURS | ACTIVITY | |
|---|---|---|---|---|
| SECS-S/03 | 2 | 10 | LESSONS |
| Objectives | |
|---|---|
| KNOWLEDGE AND UNDERSTANDING AT THE END OF THE COURSE, STUDENTS SHOULD MASTER THE MAIN STATISTICAL METHODS FOR PROGRAM AND POLICY EVALUATION. IN PARTICULAR, STUDENTS ARE EXPECTED TO GAIN: - KNOWLEDGE OF THE ECONOMETRIC FOUNDATIONS OF PROGRAM AND POLICY EVALUATION; - KNOWLEDGE OF THE MAIN METHODS FOR PROGRAM AND POLICY EVALUATION IN EXPERIMENTAL AND NONEXPERIMENTAL SETTINGS; - KNOWLEDGE OF THE MAIN TECHNIQUES FOR TREATING THE EFFECTS OF SELECTION BIAS IN POLICY EVALUATION. APPLYING KNOWLEDGE AND UNDERSTANDING STUDENTS ARE EXPECTED TO DEVELOP THE ABILITY TO USE ADVANCED MODELS FOR PROGRAM AND POLICY EVALUATION. SPECIFICALLY, THEY ARE EXPECTED TO ACHIEVE THE FOLLOWING SKILLS: - ABILITY TO IDENTIFY, IN REAL-WORLD APPLICATIONS, THE METHODS AND MODELS SUITABLE FOR THE SPECIFIC DATA SET AND PROBLEM OF INTEREST; - ABILITY TO IMPLEMENT AT THE COMPUTER THE MAIN STATISTICAL METHODS WITH RESPECT TO THE PROBLEM OF INTEREST; - ABILITY TO INTERPRET, IN ECONOMIC AND POLICY TERMS, THE RESULTS OBTAINED FROM EMPIRICAL ANALYSES. |
| Prerequisites | |
|---|---|
| BASIC KNOWLEDGE OF PROBABILITY, DESCRIPTIVE STATISTICS AND INFERENCE. |
| Contents | |
|---|---|
| THE STATISTICAL FOUNDATIONS OF REGRESSION ANALYSIS WILL BE PROVIDED. TOPICS: - FUNDAMENTALS OF DESCRIPTIVE STATISTICS. - COVARIANCE, CORRELATION AND CAUSE-EFFECT RELATIONS. - SIMPLE LINEAR REGRESSION MODEL: MODEL, ESTIMATION AND DIAGNOSTICS, THE ANALYSIS OF CAUSAL EFFECTS IN THE SIMPLE LINEAR REGRESSION MODEL. - MULTIPLE LINEAR REGRESSION MODEL: MODEL, ESTIMATION AND DIAGNOSTICS, THE ANALYSIS OF CAUSAL EFFECTS IN THE MULTIPLE LINEAR REGRESSION MODEL, EFFECTS OF OMISSION OF RELEVANT VARIABLES, THE CONTROL VARIABLES: DEFINITION, CRITERIA FOR VARIABLE SELECTION AND PROPERTIES OF OLS ESTIMATES. - NONLINEAR REGRESSION FUNCTIONS: POLYNOMIAL FUNCTIONS, LOGARITHMIC TRANSFORMATIONS, INTERACTIONS. EXPOSITION OF THEORETICAL TOPICS WILL BE SUPPORTED WITH THE DEVELOPMENT AND DISCUSSION OF CASE STUDIES ON REAL DATA. MODELS WILL BE IMPLEMENTED USING THE STATISTICAL SOFTWARE R. |
| Teaching Methods | |
|---|---|
| LECTURES AND EXERCISES. |
| Verification of learning | |
|---|---|
| THE ACHIEVEMENT OF THE OBJECTIVES IS CERTIFIED BY PASSING AN EXAM BASED ON THE DISCUSSION OF A PROJECT WORK. |
| Texts | |
|---|---|
| JAMES H. STOCK, MARK W. WATSON (2020) INTRODUCTION TO ECONOMETRICS (GLOBAL EDITION), IV EDITION. PEARSON. MARNO VERBEEK (2017) A GUIDE TO MODERN ECONOMETRICS, 5TH EDITION. JOHN WILEY & SONS. GUIDO W. IMBENS, JEFFREY M. WOOLDRIDGE (2009) RECENT DEVELOPMENTS IN THE ECONOMETRICS OF PROGRAM EVALUATION, JOURNAL OF ECONOMIC LITERATURE, VOL. 47, NO. 1, MARCH 2009 (PP. 5-86). |
| More Information | |
|---|---|
| SUPPLEMENTARY TEACHING MATERIALS (DATA, SOFTWARE, SLIDES) WILL BE DISTRIBUTED THROUGH THE LECTURER'S WEBSITE. |
BETA VERSION Data source ESSE3
