Detail práce

Predicting Czech Economic Activity Using Toll Data

Autor: Bc. Jana Učňová
Rok: 2018 - letní
Vedoucí:
Konzultant:
Typ práce: Bakalářská
Jazyk: Anglicky
Stránky: 67
Ocenění:
Odkaz: https://is.cuni.cz/webapps/zzp/detail/191797/
Abstrakt: Many analysts coincide that transportation is closely linked to economic activity. However,
data containing information about transportation have not been part of their research
for a long time. Introduction of electronic toll collection systems in recent years
led to a new source of data containing information about truck transport. This thesis
aims to examine the ability of seasonally adjusted toll data to predict Czech economic
activity. Economic activity is represented by four variables - real GDP, nominal GDP, industrial
production index and the volume of foreign trade. Seven models - five dynamic
models, ARIMA model, and regression with ARIMA error - are constructed for each
dependent variable. These models are then compared using both Akaike and Bayesian
information criterion and the most appropriate model for each dependent variable is
selected. It was concluded that both real GDP and industrial production index can be
predicted using toll data. Both the number of kilometers travelled, and the amount of
toll collected seems to be good predictors of economic activity. Particularly, data containing
information about toll collected might be more beneficial because the amount of
toll collected in given quarter can even predict economic activity in the next quarter.

Partneři

Deloitte

Sponzoři

CRIF
McKinsey
Patria Finance