Maria Lucia PARRELLA | STATISTICAL METHODS IN ECONOMICS
Maria Lucia PARRELLA STATISTICAL METHODS IN ECONOMICS
cod. 0212400023
STATISTICAL METHODS IN ECONOMICS
| 0212400023 | |
| DEPARTMENT OF ECONOMICS AND STATISTICS | |
| EQF6 | |
| ECONOMICS | |
| 2023/2024 |
| OBBLIGATORIO | |
| YEAR OF COURSE 3 | |
| YEAR OF DIDACTIC SYSTEM 2016 | |
| AUTUMN SEMESTER |
| SSD | CFU | HOURS | ACTIVITY | |
|---|---|---|---|---|
| SECS-S/01 | 10 | 60 | LESSONS |
| Objectives | |
|---|---|
| KNOWLEDGE AND ABILITY OF COMPREHENSION THE AIM OF THE COURSE IS: 1)TO EXPLORE AND GENERALIZE SOME IMPORTANT TOPICS WHICH HAVE BEEN DEALT IN PREVIOUS STATISTICS COURSES-IN PARTICULAR SOME BASIC PRINCIPLES OF STATISTICAL INFERENCE BASED ON THE LIKELIHOOD WILL BE COVERED. 2)TO PRESENT A UNIFIED AND CONCEPTUAL FRAMEWORK FOR LINEAR STATISTICAL MODELLING, FOCUSING ON THEIR USE AS TOOL FOR ANALYSING THE RELATIONSHIPS AMONG ECONOMIC VARIABLES. ABILITY TO APPLY KNOWLEDGE AND UNDERSTANDING STATISTICAL TOOLS INTRODUCED IN THE COURSE WILL BE PRESENTED WITH THE PURPOSE TO HIGHLIGHT SOME IMPORTANT THEORETICAL RESULTS AND THEIR POSSIBLE IMPLEMENTATION IN EMPIRICAL CONTEXTS THE STUDENT WILL BE GIVEN EVIDENCE OF HOW TO SELECT AND USE THE APPROPRIATE STATISTICAL TOOLS AS WELL AS HOW TO INTERPRET AND COMMENT THE RESULTS OF THE ANALYZES PERFORMED. |
| Prerequisites | |
|---|---|
| STATISTICS |
| Contents | |
|---|---|
| INFERENCE (5 CFU) • SAMPLING: INDUCTIVE INFERENCE; POPULATIONS AND SAMPLES; DISTRIBUTION OF SAMPLE; STATISTIC AND SAMPLE MOMENTS. SAMPLE MEAN: LAW OF LARGE NUMBERS, CENTRAL LIMIT THEOREM. SAMPLING FROM THE NORMAL DISTRIBUTIONS. • PARAMETRIC POINT ESTIMATION. METHODS OF FINDING ESTIMATORS: METHODS OF MOMENTS; MAXIMUM LIKELIHOOD; OTHER METHODS. PROPERTIES OF POINT ESTIMATORS: CLOSENESS; MEAN SQUARES ERROR; CONSISTENCY AND BAN; LOSS AND RISK FUNCTIONS. SUFFICIENCY: SUFFICIENT STATISTICS, FACTORIZATION CRITERION; MINIMAL SUFFICIENT STATISTICS. UNBIASED ESTIMATION. LOCATION OR SCALE INVARIANCE. BAYES ESTIMATORS. • MAXIMUM LIKELIHOOD ESTIMATION. PARAMETRIC MODELS. LIKELIHOOD FUNCTION. LOG-LIKELIHOOD FUNCTION. SCORE FUNCTION. FISHER INFORMATION. ASYMPTOTIC PROPERTIES OF MAXIMUM LIKELIHOOD ESTIMATORS. • COMPUTATIONAL PROBLEMS. TESTS BASED ON MAXIMUM LIKELIHOOD (ML RATIO, WALD’S TEST). LINEAR MODELS (5CFU) INTRODUCTION TO THE LOGIC OF STATISTICAL MODELS. VISUALIZATION OF RELATIONSHIPS BETWEEN STATISTICAL VARIABLES. THE LINEAR REGRESSION MODEL. MODEL ESTIMATION. SAMPLE PROPERTIES OF OLS AND ML ESTIMATORS. GOODNESS OF FIT AND TEST ON COEFFICIENTS. ASYMPTOTIC PROPERTIES. MULTICOLLINEARITY. OUTLIERS, INFLUENTIAL OBSERVATIONS AND ROBUST MODEL ESTIMATION. THE INTERPRETATION OF THE MODEL. VARIABLE SELECTION AND INFORMATION CRITERIA. PREDICTION. MISPECIFICATION OF THE FUNCTIONAL FORM. THE PROBLEM OF HETEROSKEDASTICITY. WHITE'S CORRECTION FOR STANDARD ERRORS. TESTS FOR HETEROSKEDASTICITY. THE PROBLEM OF AUTOCORRELATION. HAC STANDARD ERRORS. TESTS FOR AUTOCORRELATION. |
| Teaching Methods | |
|---|---|
| LECTURES |
| Verification of learning | |
|---|---|
| THE EVALUATION OF THE PROFIT IS MADE ON THE BASIS OF A WRITTEN TEST AND AN ORAL TEST. THE WRITTEN TEST (ABOUT 90 MINUTES) IS AIMED TO ASSESS THE STUDENT'S ABILITY TO USE THE STATISTICAL TECHNIQUES ACQUIRED DURING THE COURSE. THE TRACK OF THE WRITTEN TEST INCLUDES THREE EXECISES WORTH 1-10 POINTS EACH, FOR A TOTAL SCORE OF 30. THE ORAL TEST, OF ABOUT 20 MINUTES, IS INTENDED TO EVALUATING THE ARGUMENTATION CAPACITY, THE ACCURACY OF LANGUAGE AND THE ABILITY TO MAKE CRITICAL USE OF THE ACQUIRED STATISTICAL TOOLS. THE FINAL SCORE WILL TAKE INTO ACCOUNT BOTH THE WRITTEN AND ORAL EVALUATIONS. |
| Texts | |
|---|---|
| A.M. MOOD, F.A. GRAYBILL, D.C. BOES, INTRODUCTION TO THE THEORY OF STATISTICS,1974, MCGRAW HILL. MARNO VERBEEK, A GUIDE TO MODERN ECONOMETRICS, FIFTH EDITION, WILEY (CHAPTERS 1-4) |
| More Information | |
|---|---|
| ADDITIONAL MATERIALS WILL BE PROVIDED BY THE PROFESSOR DURING THE COURSE |
BETA VERSION Data source ESSE3 [Ultima Sincronizzazione: 2024-11-05]
