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 time series analysis in the time domain and the basic concepts of consumer price indices.
In particular, the course aims to provide the following knowledge, competencies and skills.
Knowledge and understanding: theoretical foundations of the linear time series statistical models and the methodological bases of the 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.
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 are required to send an e-mail request to the teacher. The e-mail must be sent from the institutional UNIFI address.
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.
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.