Econometrics

ECONOMICS Econometrics

0222200003
DEPARTMENT OF ECONOMICS AND STATISTICS
EQF7
ECONOMICS
2022/2023

OBBLIGATORIO
YEAR OF COURSE 1
YEAR OF DIDACTIC SYSTEM 2018
AUTUMN SEMESTER
CFUHOURSACTIVITY
1060LESSONS
ExamDate
ECONOMETRIA10/01/2023 - 14:00
ECONOMETRIA10/01/2023 - 14:00
ECONOMETRIA25/01/2023 - 14:00
ECONOMETRIA25/01/2023 - 14:00
ECONOMETRIA07/02/2023 - 14:00
ECONOMETRIA07/02/2023 - 14:00
Objectives
KNOWLEDGE AND UNDERSTANDING: AIM OF THE COURSE IS TO PROVIDE STUDENTS WITH METHODOLOGICAL TOOLS FOR UNDERSTANDING THE MOST WIDESPREAD CONCEPTS OF CAUSAL ANALYSIS AND THE KEY CONCEPTS OF FORECASTING ANALYSIS.

APPLYING KNOWLEDGE AND UNDERSTANDING: AIM OF THE COURSE IS TO PROVIDE STUDENTS WITH THE ABILITY TO INTERPRET ECONOMIC QUESTIONS IN TERMS OF EMPIRICAL ANALYSIS, TO PRODUCE EMPIRICAL ANALYSES AT A BASIC AND INTERMEDIATE LEVEL AND TO UNDERSTAND EMPIRICAL ANALYSES AT AN ADVACED LEVEL.

MAKING JUDGEMENTS: WE EXPECT THAT IN LIVE DATA ANALYSIS STUDENTS WILL BE ABLE TO CRITICALLY SELECT THE ANALYTICAL MODELS WHICH BEST RESPOND TO THE FEATURES OF THE PROBLEM OBJECT OF STUDY.

COMMUNICATION SKILLS: AIM OF THE COURSE IS THAT STUDENTS WILL BE ABLE TO PRESENT TO A NON-TECHNICAL AUDIENCE THE RESULTS OBTAINED BY APPLYING INTERMEDIATE AND ADVANCED ECONOMETRIC TOOLS AND WILL BE ABLE DO DISCUSS WITH A TECHNICAL AUDIENCE THE DETAILS OF THEIR ANALYSIS.

LEARNING SKILLS: AIM OF THE COURSE IS THAT STUDENTS WILL BE ABLE TO COMMENCE ADVANCE STUDIES IN ECONOMETRICS AND IN APPLIED ECONOMICS AT MASTER OR DOCTORAL LEVEL.
Prerequisites
KNOWLEDGE OF THE MAIN CONCEPTS OF DESCRIPTIVE AND INFERENTIAL STATISTICS, IN PARTICULAR: RANDOM VARIABLES, ESTIMATORS AND THEIR MAIN PROPERTIES, HYPOTHESIS TESTING.
Contents
- BRIEF REFRESHER OF STATISTICAL BASES
- SIMPLE LINEAR REGRESSION.
- MULTIPLE LINEAR REGRESSION.
- NONLINEAR REGRESSION FUNCTIONS.
- EVALUATION OF MULTIPLE REGRESSION RESULTS.
- REGRESSION WITH PANEL DATA.
- REGRESSION WITH BINARY DEPENDENT VARIABLE.
- REGRESSION WITH INSTRUMENTAL VARIABLES.
- EXPERIMENTS AND QUASI-EXPERIMENTS.
- INTRODUCTION TO REGRESSION WITH TIME SERIES DATA AND FORECASTING.
- REGRESSION WITH DYNAMIC CAUSAL EFFECTS.
- PREDICTION WITH MANY REGRESSORS AND BIG DATA
Teaching Methods
60 HOURS OF LECTURES (10 CFU), TAUGHT IN CLASS WITH THE AID OF PROJECTIONS AND APPLIED SOFTWARE. ATTENDANCE IS STRONGLY RECOMMENDED BUT NOT COMPULSORY.

IN CASE OF RESTRICTIONS TO ATTENDANCE DUE TO SECURITY MEASURES, CLASSES WILL BE HELD ONLINE.
Verification of learning
THE ATTAINMENT OF THE AIMS OF THE COURSE IS VERIFIED THROUGH AN EXAM WHOSE EVALUATION IS EXPRESSED IN A SCORE THAT CAN TAKE VALUES FROM 0 TO 30/30. A “CUM LAUDE” FINAL GRADE IS POSSIBLE. THE EXAM IS STRUCTURED AS A WRITTEN PART FOLLOWED BY A BRIEF ORAL PART. THE TWO PARTS CAN TAKE PLACE IN DIFFERENT DAYS. THE FINAL GRADE WILL BE COMPUTED AS THE GRADE OF THE WRITTEN PART PLUS OR MINUS 3/30 AFTER THE ORAL PART.

THE WRITTEN PART WILL LAST ABOUT 90 MINUTES. ITS AIM IS TO CHECK THE LEVEL OF KNOWLEDGE OF THE TOPICS TREATED IN THE COURSE, THE ABILITY TO MANAGE THE ANALYTICAL INSTRUMENTS AND TO APPLY THE THEORETICAL SKILLS ACQUIRED.

THE WRITTEN PART IS STRUCTURED IN THREE PARTS. THE FIRST PART IS COMPOSED BY A NUMBER (VARIABLE FROM 5 TO 10) OF MULTIPLE CHOICE QUESTIONS. THE SECOND PART CONSISTS OF A NUMBER (VARIABLE FROM 2 TO 5) OF QUESTIONS WITH OPEN ANSWERS. THE THIRD PART CONSISTS IN THE ANALYSIS OF THE RESULTS OF AN ECONOMETRIC ESTIMATE. EACH PART IS ASSIGNED A WEIGHT BETWEEN 1/30 AND 30/30 WHICH VARIES WITH THE COMPLEXITY OF THE REQUIRED ANSWERS. WEIGHTS ARE CLEARLY STATED ON THE EXAM SHEET. THE SUM OF THE WEIGHTS OF ALL QUESTIONS IS 30/30. DURING THE WRITTEN PART, COOPERATION AND COMMUNICATION WITH OTHER STUDENTS ARE STRICTLY FORBIDDEN. THE EXAM IS CLOSED BOOKS, CLOSED NOTES. THE USE OF COMPUTERS OR ANY TYPE OF PHONES OR TABLETS IS STRICTLY FORBIDDEN DURING THE EXAM.

THE ORAL PART WILL CONSIST IN THE DISCUSSION OF THE DOCUMENT PRODUCED IN THE WRITTEN PART.
Texts
JAMES STOCK, MARK WATSON, INTRODUCTION TO ECONOMETRICS, ADDISON-WESLEY SERIES IN ECONOMICS.
More Information
PRACTICAL APPLICATIONS WILL BE PERFORMED USING THE R LANGUAGE
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