Marcella NIGLIO | STATISTICAL MODELS

cod. 0212800012

STATISTICAL MODELS

	0212800012
	DEPARTMENT OF ECONOMICS AND STATISTICS
	EQF6
	STATISTICS FOR BIG DATA
	2022/2023

PERCORSO A.SHARED STUDY PATHWAY

	OBBLIGATORIO
	YEAR OF COURSE 2
	YEAR OF DIDACTIC SYSTEM 2018
	SPRING SEMESTER

TEACHING UNITS

	SSD	CFU	HOURS	ACTIVITY	TYPE OF ACTIVITY
	SECS-S/01	10	60	LESSONS	COMPULSORY SUBJECTS, CHARACTERISTIC OF THE CLASS

PROFESSORS

	MARCELLA NIGLIO T Curriculum
	MARIALUISA RESTAINO Curriculum

	Objectives
	THE COURSE AIMS TO INTRODUCE THE MAIN STATISTICAL METHODS AND MODELS USED TO DESCRIBE, INTERPRET AND PREDICT DATA FROM VARIOUS CONTEXTS. KNOWLEDGE AND UNDERSTANDING CAPACITY THE COURSE AIMS TO PROVIDING THE THEORETICAL AND PRACTICAL TOOLS NECESSARY FOR THE CONSTRUCTION AND THE USE OF A WIDE CLASS OF STATISTICAL MODELS, EVEN COMPLEX, AND TO PREPARE THE STUDENT FOR THE INTERPRETATION, SPECIFICATION AND ESTIMATION OF THE AFOREMENTIONED MODELS. THE STRUCTURE OF THE COURSE IS MAINLY APPLIED, IN ORDER TO FOCUS ON SOLUTIONS OF THE TYPICAL PROBLEMS WITH DATA ANALYSIS, SUPPORTED BY APPROPRIATE STATISTICAL SOFTWARE, AND TO PREPARE STUDENTS FOR THE CORRECT CHOICE OF THE MOST APPROPRIATE MODELS IN THE PARTICULAR CONTEXT UNDER ANALYSIS. ALL THE TOPICS COVERED DURING THE COURSE WILL BE ACCOMPANIED BY TUTORIALS ON REAL DATA. APPLYING KNOWLEDGE AND UNDERSTANDING THE KNOWLEDGE ACQUIRED DURING THE COURSE WILL ALLOW THE STUDENT: TO BE ABLE TO IDENTIFY THE MOST APPROPRIATE MODELS FOR THE ANALYSIS AND FORECASTING OF DATA GENERATED IN DIFFERENT EMPIRICAL CONTEXTS (ECONOMIC, MANAGERIAL, MEDICAL, SOCIAL, ETC.); TO BE ABLE TO IDENTIFY AND USE STATISTICAL SOFTWARE USEFUL FOR THE ESTIMATION OF SUCH MODELS AND FOR THE CONSTRUCTION OF SUITABLE PLOTS AND TABLES TO SUMMARIZE THE RESULTS OF THE ESTIMATION AND FORECASTING; TO BE ABLE TO COMMUNICATE IN A TECHNICAL OR INFORMATIVE WAY THE FINAL ESTIMATIONS AND TO PRODUCE OR INTERPRET STATISTICAL REPORTS SUMMARIZING THE RESULTS OF THE ANALYSIS.

THE COURSE AIMS TO INTRODUCE THE MAIN STATISTICAL METHODS AND MODELS USED TO DESCRIBE, INTERPRET AND PREDICT DATA FROM VARIOUS CONTEXTS.
KNOWLEDGE AND UNDERSTANDING CAPACITY
THE COURSE AIMS TO PROVIDING THE THEORETICAL AND PRACTICAL TOOLS NECESSARY FOR THE CONSTRUCTION AND THE USE OF A WIDE CLASS OF STATISTICAL MODELS, EVEN COMPLEX, AND TO PREPARE THE STUDENT FOR THE INTERPRETATION, SPECIFICATION AND ESTIMATION OF THE AFOREMENTIONED MODELS. THE STRUCTURE OF THE COURSE IS MAINLY APPLIED, IN ORDER TO FOCUS ON SOLUTIONS OF THE TYPICAL PROBLEMS WITH DATA ANALYSIS, SUPPORTED BY APPROPRIATE STATISTICAL SOFTWARE, AND TO PREPARE STUDENTS FOR THE CORRECT CHOICE OF THE MOST APPROPRIATE MODELS IN THE PARTICULAR CONTEXT UNDER ANALYSIS. ALL THE TOPICS COVERED DURING THE COURSE WILL BE ACCOMPANIED BY TUTORIALS ON REAL DATA.

APPLYING KNOWLEDGE AND UNDERSTANDING
THE KNOWLEDGE ACQUIRED DURING THE COURSE WILL ALLOW THE STUDENT: TO BE ABLE TO IDENTIFY THE MOST APPROPRIATE MODELS FOR THE ANALYSIS AND FORECASTING OF DATA GENERATED IN DIFFERENT EMPIRICAL CONTEXTS (ECONOMIC, MANAGERIAL, MEDICAL, SOCIAL, ETC.); TO BE ABLE TO IDENTIFY AND USE STATISTICAL SOFTWARE USEFUL FOR THE ESTIMATION OF SUCH MODELS AND FOR THE CONSTRUCTION OF SUITABLE PLOTS AND TABLES TO SUMMARIZE THE RESULTS OF THE ESTIMATION AND FORECASTING; TO BE ABLE TO COMMUNICATE IN A TECHNICAL OR INFORMATIVE WAY THE FINAL ESTIMATIONS AND TO PRODUCE OR INTERPRET STATISTICAL REPORTS SUMMARIZING THE RESULTS OF THE ANALYSIS.

	Prerequisites
	IT IS REQUIRED THE EXAM ON STATISTICAL INFERENCE AND AN ADEQUATE KNOWLEDGE OF THE LINEAR ALGEBRA

	Contents
	MODULE 1 - REGRESSION MODEL STATISTICAL MODELS. UNIVARIATE AND MULTIVARIATE REGRESSION MODEL: SPECIFICATION, ASSUMPTIONS, PARAMETERS ESTIMATION. GAUSS-MARKOV THEOREM. MODEL CHECKING. MODULE 2: MODELS FOR QUALITATIVE AND LIMITED DEPENDENT VARIABLES MODELS FOR BINARI DEPENDENT VARIABLES. LOGIT MODEL. MULTINOMIAL MODEL. LOGIT CONDITIONAL MODEL. MODELS FOR ORDINAL DATA. MODELS FOR COUNT DATA. MODELS WITH DEPENDENT LIMITED VARIABLES.

Contents

MODULE 1 - REGRESSION MODEL
STATISTICAL MODELS. UNIVARIATE AND MULTIVARIATE REGRESSION MODEL: SPECIFICATION, ASSUMPTIONS, PARAMETERS ESTIMATION. GAUSS-MARKOV THEOREM. MODEL CHECKING.

MODULE 2: MODELS FOR QUALITATIVE AND LIMITED DEPENDENT VARIABLES
MODELS FOR BINARI DEPENDENT VARIABLES. LOGIT MODEL. MULTINOMIAL MODEL. LOGIT CONDITIONAL MODEL. MODELS FOR ORDINAL DATA. MODELS FOR COUNT DATA. MODELS WITH DEPENDENT LIMITED VARIABLES.

	Teaching Methods
	THE TOTAL NUMBER OF HOURS IS 60: 44 IN CLASSROOM AND 26 IN COMPUTING ROOM.

	Verification of learning
	THE FINAL EXAM IS CHARACTERIZED BY TWO PARTS: THE WRITTEN AND THE ORAL EXAMINATION. THE FIRST PART INCLUDES THEORETICAL QUESTIONS, EXERCISES ON THE THREE MODULES OF THE COURSE AND A QUESTION THAT REQUIRE THE USE OF THE R-SOFTWARE. THE ORAL PART IS BASED ON AN INTERVIEW. THE ORAL INTERVIEW CAN BE TAKEN IF THE STUDENT HAS PASSED THE WRITTEN EXAM WITH A MINIMUM SCORE OF 18.

	Texts
	LECTURES NOTES AND REFERENCES SUGGESTED BY THE INSTRUCTORS HILL C.R., GRIFFITHS W.E., LIM G. C. (2011): PRINCIPLES OF ECONOMETRICS, WILEY (FIFTH EDITION) WOOLDRIDGE, J.M. (2020): INTRODUCTORY ECONOMETRICS: A MODERN APPROACH, SOUTH-WESTERN PUB (7TH EDITION) GREENE, W.H. (2018): ECONOMETRIC ANALYSIS, PEARSON (8TH EDITION)

	More Information
	WEB PAGES I MODULE: MARCELLA NIGLIO HTTPS://DOCENTI.UNISA.IT/003299/HOME II MODULE: MARIALUISA RESTAINO HTTPS://DOCENTI.UNISA.IT/022266/HOME

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Marcella NIGLIO | STATISTICAL MODELS