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 notes and teaching materials are available on the Moodle platform.
Textbook: Di Fonzo T. e F. Lisi (2005), Serie storiche economiche. Analisi statistiche e applicazioni, Carocci Editore, Roma. ( some chapters or sub-chapters will be skipped and these are indicated in a file available on the Moodle platform).
Learning Objectives
The objective of the course is to develop the skills needed to do empirical analysis in economics with time series. Special attention will be placed on the quality of data and on limitations and pitfalls of different methods.
Prerequisites
Introductory statistics.
Teaching Methods
Classroom lectures and computer labs.
Further information
Slides, teaching material and more detailed information on the course are available on the Moodle platform (http://e-l.unifi.it/).
Type of Assessment
Before the oral examination students have to write and submit a short report containing the analysis of a real time series (instructions and deadlines are available on the Moodle platform).
The oral examination shall cover the contents of the report and the subjects described in the next section.
Course program
Time series analysis in economics.
Introductory univariate time series analysis with linear methods.
Exploratory analysis: plots, summary statistics, transformations (logs, differencing, index numbers). Sample autocorrelation.
Time series decomposition. Time series components (trend, cycle, seasonal component and error).
Moving averages. Census I seasonal decomposition.
Exponential smoothing. General introduction. Simple exponential smoothing. Holt’s linear trend method. Holt-Winters’ seasonal method.
Stochastic processes. Stationarity, ergodicity, invertibility, gaussianity. Wold’s theorem.
AR, MA, ARMA, ARIMA and SARMA models. Box-Jenkins methodology.
Consumer price indexes. Istat indexes (NIC, FOI; IPCA). Price collection and calculation method. Measures of inflation. Core inflation. Perceived inflation.
Classroom lectures are accompanied by computer labs.