Predicting Czech Economic Activity Using Toll Data
| Název práce v češtině: | Odhadování české ekonomické aktivity pomocí dat z mýtného systému |
|---|---|
| Název v anglickém jazyce: | Predicting Czech Economic Activity Using Toll Data |
| Klíčová slova: | nowcasting, ekonomická aktivita, mýtná data, elektronické mýtné, dynamický model, ARIMA, Akaikeho informační kritérium, Bayesovo informační kritérium |
| Klíčová slova anglicky: | nowcasting, economic activity, toll data, electronic toll collection, dynamic model, ARIMA, Akaike information criterion, Bayesian information criterion |
| Akademický rok vypsání: | 2016/2017 |
| Typ práce: | bakalářská práce |
| Jazyk práce: | angličtina |
| Ústav: | Institut ekonomických studií (23-IES) |
| Vedoucí / školitel: | Ing. David Kocourek |
| Řešitel: | skrytý - zadáno vedoucím/školitelem |
| Datum přihlášení: | 15.06.2017 |
| Datum zadání: | 20.06.2017 |
| Datum a čas obhajoby: | 11.06.2018 09:00 |
| Místo konání obhajoby: | Opletalova - Opletalova 26, O206, Opletalova - místn. č. 206 |
| Datum odevzdání elektronické podoby: | 11.05.2018 |
| Datum proběhlé obhajoby: | 11.06.2018 |
| Oponenti: | Mgr. Ing. Tomáš Šestořád |
| Kontrola URKUND: | |
| Zásady pro vypracování |
| Research question and motivation
The form of the toll system and its possible targets of use are actual topics in the Czech Republic. Highways and expressways began to be charged on January 1, 1995. The new toll system was introduced in 2007 and since that vehicles with a total weight greater than 12 tonnes have been obliged to pay an electronic toll for highways, expressways and selected sections of first-class roads. Since 2010 the obligation to pay electronic toll has been extended to vehicles with a total weight greater than 3.5 tonnes and buses have been added since 2011. The toll rates are based on emission class of given vehicle and its number of axles. Introduction of electronic toll collection system led to a new source of data containing information about truck transport. This data can be therefore a useful source of information which might be used to predict economic activity. Authors like Askitas and Zimmermann (2013) and Döhrn (2013) used toll data from Germany, and they found the strong correlation between transportation and business cycles. Especially, month-on-month variation in kilometers travelled appeared as a good predictor of German industrial production index. Electronic collection system provides a large amount of data which can be used. The aim of this thesis is to examine the ability of toll data to predict Czech economic activity. In my thesis, I would like to focus on following research questions. • Can the economic activity be predicted using toll data? • Which of the variable containing information about truck transport can best predict economic activity? • Are effects of toll data on economic activity positive or negative? Contribution Askitas and Zimmermann (2013) and Döhrn (2013) examined the effect of toll data on German industrial production index. To my knowledge, no authors focused on a similar analysis in the Czech Republic. Adding other dependent variables like real GDP, nominal GDP, and trade balance, some other significant effects might be found out. Thus, this thesis might serve as a framework for how to proceed in selecting the most appropriate model predicting economic activity, which can be then used in different states of Europe. |
| Seznam odborné literatury |
| ASKITAS, Nikolaos; ZIMMERMANN, Klaus F. Nowcasting business cycles using toll data. Journal of Forecasting, 2013.
DÖHRN, Roland. Transportation data as a tool for nowcasting economic activity–The German road pricing system as an example. 2013. LAHIRI, Kajal; YAO, Vincent Wenxiong. Economic indicators for the US transportation sector. Transportation Research Part A: Policy and Practice, 2006. YAO, Vincent W. The causal linkages between freight and economic fluctuations. International Journal of Transport Economics/Rivista internazionale di economia dei trasporti, 2005. HYNDMAN, Rob J.; ATHANASOPOULOS, George. Forecasting: principles and practice. OTexts, 2014. |
| Předběžná náplň práce |
| Methodology
I am going to be using time series data from 2007 to 2017 which shows the number of kilometers travelled, the number of vehicles travelled and the amount of toll collected among highways and roads. As a dependent variable, real GDP, nominal GDP, industrial production index and balance trade will be used. I am going to construct several models for each dependent variable, which will be later on compared using Akaike information criterion and the most appropriate model for each dependent variable will be selected. Outline 1. Introduction 2. Literature review 2.1. Indicators of economic activity 2.2. Transportation and the economy 2.3. Electronic toll collection 2.4. Toll data as a tool of nowcasting 3. Methodology 3.1. Dynamic models 3.2. ARIMA models 3.3. Regressions with ARIMA error 4. Results 5. Conclusion |
| Předběžná náplň práce v anglickém jazyce |
| Methodology
I am going to be using time series data from 2007 to 2017 which shows the number of kilometers travelled, the number of vehicles travelled and the amount of toll collected among highways and roads. As a dependent variable, real GDP, nominal GDP, industrial production index and balance trade will be used. I am going to construct several models for each dependent variable, which will be later on compared using Akaike information criterion and the most appropriate model for each dependent variable will be selected. Outline 1. Introduction 2. Literature review 2.1. Indicators of economic activity 2.2. Transportation and the economy 2.3. Electronic toll collection 2.4. Toll data as a tool of nowcasting 3. Methodology 3.1. Dynamic models 3.2. ARIMA models 3.3. Regressions with ARIMA error 4. Results 5. Conclusion |