STATISTICAL METHODS FOR TOURISM

Single discipline educational activity

Course Sheet

Academic Year of enrolment:

2017/2018

Type of Course:

Related/Supplementary

Optional/Mandatory:

Optional

Degree:

Master's Degree Programme in BUSINESS ADMINISTRATION

Location:

OLBIA

Disciplinary Sector:

Demographics

Credits:

Programme Year:

Professor and Collaborators:

SALINARI Giambattista

Hours of classroom activity:

Prerequisites:

None

AF code:

A001060

Assessment Methods

Verification of learning:

Students are requested to present a short paper of about 10-15 pages composed of two main parts. In the first part the student will present, following a bibliography provided by the teacher, the analysis of a specific method employed nowadays in tourism market forecasting. In the second part of the paper it will be requested, instead, to forecast tourism arrivals in a given Italian area following the methodology (SARIMA) described during the course. For each part of the paper the student will receive up to 15 points.

Teaching period

Second semester

Objectives

The course will introduce time series analysis with applications to tourism market forecasting. The initial part of the course will recall some basic notions of statistics - such as the hypothesis testing theory or the linear regression model - and the R statistical programming language. The course will then continue with the presentation of the auto-regressive (AR) and the moving average (MA) processes. In the last part of the course students will use the ARIMA and the SARIMA models to forecast tourism arrivals in the Sardinian provinces.
At the end of the class the student will get:
(1) The knowledge necessary to understand the positioning of a tourism firm on the market;
(2) The knowledge necessary to understand the evolution over time of tourism destinations;
(3) The ability of solving problems according to the procedure of statistical hypothesis testing;
(4) The knowledge necessary to understand the general trend of the tourism market and its macroeconomic and geographic framework;
(5) The ability to access data sources on tourism;
(6) The ability to take a decision based on data;
(7) The ability to use a statistical software (R) to make calculation or producing graphics;
(8) The ability to work in a computer lab.

Contents

The class will focus on some models frequently employed in the analysis of tourism time series. The course will initially present some important stationary processes such as the auto-regressive (AR) and the moving average (MA) models. After that, the course will present the concepts of autocorrelation and partial-autocorrelation function necessary for the implementation of the Box-Jenkins methodology. The AR and the MA models will be subsequently generalized and extended to non-stationary processes thanks to the ARIMA models. Finally, the course will introduce the seasonality component trough the SARIMA models. Thanks to the latter the course will present a forecast of the tourism arrivals in Sardinia in the next few years.

Recommended Bibliography

Cowpertwait P.S.P, Metcalfe A.V. (2009), Introductory Time Series with R, Springer

Teaching Methods

The class generally takes place in the computer lab. Each lesson typically envisages a first part devoted to the presentation of a theoretical topic (the AR or the MA processes). In the second part an application of the theory to real data follows. The analysis is first performed at the computer by the teacher and then repeated by the students on a specific case study. This approach gives the students the possibility to immediately put in practice the theoretical concepts presented in the first part of the lesson and to become acquainted with different kind of data. During the class two statistical software are used: Excel and R.

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STATISTICAL METHODS FOR TOURISM

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