Statistical models for economic time series analysis: decomposition methods; moving averages; exponential smoothing models; AR, MA, ARMA, ARIMA and seasonal ARIMA models. Consumer price indexes: theory and practice.
Lecture material is posted on the Moodle class web page.
A useful reference book is:
Di Fonzo T. e F. Lisi (2015), Serie storiche economiche. Analisi statistiche e applicazioni, Carocci Editore, Roma.
The list of the sections to be skipped is posted on the Moodle class web page.
Learning Objectives
The students are expected to master the basic concepts of linear time series analysis and the basic concepts of consumer price indices.
Knowledge and understanding: theoretical foundations of the linear time series statistical models and the methodological bases of Istat's consumer price indices and inflation measures. Official statistical sources for economic time series and consumer price indices and related metadata.
Application of knowledge and understanding: the student will acquire the methodological bases that make her/him able to apply the statistical procedures on time series and to interpret the results, dedicating particular attention to the nature and reliability of the analyzed data and to the potentialities and limits of the methods used. She/he will be able to interpret in a correct way the inflation indicators diffused by Istat.
Making judgements: the student must be able to collect and interpret the data considered useful for the preparation of statistical analyzes over time and to make independent judgments and critical evaluations based on the results of the analysis.
Communication skills: The student must be able to master the technical-statistical terminology relating to the analysis of economic time series and price index numbers. You will be able to communicate concepts, analyzes and results effectively to both specialized and non-specialist audiences.
Learning skills: The student will have to develop the learning skills necessary to be able to apply the acquired knowledge to his own professional context and/or to face more advanced-level courses.
Prerequisites
It is assumed that students are familiar with basic descriptive and inferential statistics (topics covered in B018993-STATISTICA).
Teaching Methods
Classroom lectures.
Further information
In order to get access to the Moodle class web page, students must send an e-mail request to the teacher from their institutional UNIFI address.
Interested students are advised that in course B028396-Laboratorio di Analisi Dati (3 CFU), applications of the methods presented in the Economic Statistics course are carried out using R, a free software environment for statistical calculations and graphics.
Type of Assessment
The exam will be oral. The questions will cover the whole program specified in the Syllabus "Course program" section.
Course program
1. Time series analysis in economics.
Introductory univariate time series analysis with linear methods.
2. Exploratory analysis: plots, summary statistics, transformations (logs, differencing, index numbers), sample autocorrelation.
3. Time series decomposition. Time series components (trend, cycle, seasonal component and error).
4. Moving averages. Census I seasonal decomposition.
5. Exponential smoothing. General introduction. Simple exponential smoothing. Holt’s linear trend method. Holt-Winters’ seasonal method.
6. Stochastic processes. Wold’s theorem. AR, MA, ARMA, ARIMA and SARMA models. Box-Jenkins methodology. Forecasting with ARMA models.
7. Time series data produced by Istat.
8. Consumer price indexes. Istat indexes (NIC, FOI; IPCA). Price collection and calculation method. Measures of inflation. Core inflation. Perceived inflation.