Cira PERNA | METODI STATISTICI PER L'ECONOMIA
Cira PERNA METODI STATISTICI PER L'ECONOMIA
cod. 0212400023
METODI STATISTICI PER L'ECONOMIA
| 0212400023 | |
| DIPARTIMENTO DI SCIENZE ECONOMICHE E STATISTICHE | |
| EQF6 | |
| ECONOMICS | |
| 2017/2018 |
| YEAR OF COURSE 3 | |
| YEAR OF DIDACTIC SYSTEM 2014 | |
| PRIMO SEMESTRE |
| SSD | CFU | HOURS | ACTIVITY | |
|---|---|---|---|---|
| SECS-S/01 | 10 | 60 | LESSONS |
| Objectives | |
|---|---|
| KNOWLEDGE AND ABILITY OF COMPREHENSION THE COURSE AIMS AT EXAMINING AND GENERALIZING SOME IMPORTANT TOPICS WHICH HAVE BEEN DEALT IN PREVIOUS STATISTICS COURSES-IN PARTICULAR SOME ISSUES OF PROBABILITY THEORY WILL BE REVIEWED AND TECHNIQUES FOR FINDING THE DISTRIBUTION OF A TRANSFORMATION OF RANDOM VARIABLES WILL BE INTRODUCED . BASIC PRINCIPLES OF STATISTICAL INFERENCE BASED ON THE LIKELIHOOD WILL ALSO BE COVERED 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 | |
|---|---|
| PROBABILITY (5 CFU) • PROBABILITY MODELS. DEFINITIONS OF SAMPLE SPACE AND EVENTS. DEFINITION OF PROBABILITY. FINITE SAMPLE SPACES. CONDITIONAL PROBABILITY AND INDEPENDENCE. • RANDOM VARIABLE AND CUMULATIVE DISTRIBUTION FUNCTION. DENSITY FUNCTIONS. EXPECTATIONS AND MOMENTS. • DISCRETE DISTRIBUTIONS: UNIFORM DISTRIBUTION; BERNOULLI AND BINOMIAL DISTRIBUTIONS; HYPERGEOMETRIC DISTRIBUTION; POISSON DISTRIBUTION; GEOMETRIC AND NEGATIVE BINOMIAL DISTRIBUTION. CONTINUOUS DISTRIBUTIONS: UNIFORM DISTRIBUTION; NORMAL DISTRIBUTION, EXPONENTIAL DISTRIBUTION BETA DISTRIBUTION. • JOINT AND CONDITIONAL DISTRIBUTIONS. STOCHASTIC INDEPENDENCE. • DISTRIBUTIONS OF FUNCTIONS OF RANDOM VARIABLES. EXPECTATIONS OF FUNCTIONS OF RANDOM VARIABLES. CUMULATIVE DISTRIBUTION FUNCTION TECHNIQUE: DISTRIBUTION OF MINIMUM AND MAXIMUM; DISTRIBUTION OF SUM AND DIFFERENCE OF TWO RANDOM VARIABLES; DISTRIBUTION OF PRODUCT AND QUOTIENT. MOMENT GENERATING FUNCTION TECHNIQUE: DISTRIBUTION OF SUMS OF INDEPENDENT RANDOM VARIABLES. THE DISTRIBUTION OF Y=G(X). PROBABILITY INTEGRAL TRANSFORM. 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. ORDER STATISTICS. • 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). |
| Teaching Methods | |
|---|---|
| LECTURES |
| Verification of learning | |
|---|---|
| THE METHOD OF VERIFICATION OF LEARNING CONSISTS OF A WRITTEN TEST AIMED TO ASSESS THE ABILITY TO USE THE STATISTICAL TECHNIQUES ACQUIRED AND AN ORAL EXAM TO TEST THEIR METHODOLOGICAL IMPLICATIONS. |
| Texts | |
|---|---|
| ALEXANDER M. MOOD, FRANKLIN A. GRAYBILL, DUANE C. BOES, INTRODUCTION TO THE THEORY OF STATISTICS, MC GRAW HILL |
| More Information | |
|---|---|
| ADDITIONAL MATERIALS WILL BE PROVIDED BY THE PROFESSOR DURING THE COURSE |
BETA VERSION Data source ESSE3 [Ultima Sincronizzazione: 2019-05-14]
