The provides basic tools to interpret and analyze time series data: simple and composite index numbers; preliminary analyses; stochastic processes; ARIMA and seasonal ARIMA models; long memory models; GARCH-like models for financial time series.
Di Fonzo, T. e Lisi F. (2013). Serie storiche economiche. Analisi statistiche e applicazioni, Carocci Editore, Roma.
Additional readings could be provided during the course.
Index numbers; measures of inflation.
Analysis of time series data: fundamental concepts; ARIMA and seasonal ARIMA models; GARCH models.
To face the analysis of time series data: to set the analysis in its background; preliminary checks; modeling and diagnostic checks; practical use of the model (forecasts; simulations).
To transfer the results of the analyses to other people by using an appropriate language.
To interpret analyses of time series data made by other people (report, scientific articles).
To read the literature on the topics of the course.
1st course in Statistics; 2nd course in Statistics; R software
Traditional lessons plus practical lessons in the computer room guided by the teacher.
Traditional lessons: 47 hours
Practical lessons: 25 hours
Lessons, written using a pen tablet, made available on the Moodle page.
Further details and material available on the Moodle page of the course (http://e-l.unifi.it/). Students need to ask the teacher for the permission to login.
Type of Assessment
The exam consists of an oral conversation. The student must prepare a written report (including a statistical analysis of two economic time series) to be delivered to the teacher at least a week before the oral exam. The instructions on how to prepare the report are available on Moodle.
Introduction to time series data.
Preliminary analyses: graphical checks; transformations (index numbers, change rates, logarithms, lag and diff operators, moving averages); synthetic indicators.
Stochastic processes: Stationary processes; Estimation of expectations of a stochastic stationary process (ergodicity conditions); Examples of stochastic stationary processes; Non-stationary processes; Seasonal processes.
Stochastic processes and linear models (ARIMA).
The Box-Jenkins procedure: Identification; Parameter estimation; Diagnostic checks; Simulation; Seasonal models; Forecasting; Applications.
Stationary vs trending processes: Unit root tests (Dickey-Fuller and Augmented Dickey-Fuller tests).
Time series of financial activities: Stylized facts; models for the conditional variance.
Statistical analyses lead in the R statistical environment.