Academic literature on the topic 'Car price'

Дипломна робота містить: 117 с., 13 табл., 29 рис., 2 дод. та 46 джерел. Об’єктом дослідження є вибірка даних вторинного автомобільного ринку України за 2020 рік. Предметом дослідження є методи інтелектуального аналізу даних на основі регресії з використанням дерев рішень. Програмною мовою обрано Python. Метою роботи є визначення найкращої моделі для виконання прогнозу ціни автомобіля використовуючи дані вторинного автомобільного ринку України. В роботі проведено дослідження застосування дерев рішень та різних методів, що засновуються на них в розглядаємій задачі при прогнозуванні на основі існуючих даних 2020 року. Виділено основні фактори, які впливають на ціну. У ході дослідження було встановлено, що метод випадкових лісів дає хороші результати на досліджуваних даних. Планується розвивати роботу у напрямку дослідження застосування даного методу з метою подальшого зменшення похибки прогнозування у різних задачах, пов’язаних з прогнозуванням цін не лише автомобілів, але і інших транспортних засобів.<br>The bachelors work consists of: 117 p., 13 tables, 29 fig., 2 add. and 46 references. The object of the study is a sample of data from the secondary automotive market of Ukraine for 2020. The subject of research is methods of data mining based on regression using decision trees. Python is selected as the programming language. The aim of the work is to determine the best model for forecasting the price of a car using data from the secondary car market of Ukraine. The study of the application of decision trees and various methods based on them in this problem in forecasting based on existing data for 2020. The main factors that affect the price are highlighted. In course of the study, it was found that the method of random forests gives good results on the studied data. It is planned to develop work in the direction of research on the application of this method in order to further reduce the forecasting error in various tasks related to forecasting the prices not only of cars but also of other vehicles.

The thesis is focused on verifying the pro-cyclical relationship of sales of new passenger cars in the Czech Republic and Slovakia and the subsequent evaluation of the impact of macroeconomic variables in the period from the first quarter of 2005 to the third quarter of 2015. In total, there are four different models estimated of multiple linear regressions of car sales, where for the Czech R. the estimation of different sectors of households and firms is available as well. By comparing the Czech R. and Slovakia regarding aggregate sales, fuel prices and gross domestic products are especially crucial and the pro-cyclical relationship is successfully verified. The economic crisis at the turn of 2008-2009 hit especially Slovakia, while the effect in the Czech R. was offset by a positive shock to fuel prices. For the model of households the procyclical relationship is denied unlike from the model of firms, shock to GDP therefore only deviate sales to firms. For Czech R. is also confirmed a significant relationship of the sales with interest rate or population growth.

The first chapter of this dissertation examines the returns to frequent acquirers from emerging markets and analyzes the cross-country variations in cumulative abnormal returns. The sample consists of 5,147 transactions carried out by firms from 17 common and civil-law countries during the period of January 1985 to June 2008. I find that the cumulative abnormal returns decline over the deal order and it is more pronounced in civil-law countries than in common-law countries. There is also evidence that the premiums paid by acquirers from civillaw countries with a first successful acquisition are higher than those from common-law countries. These findings are consistent with agency problems and the hubris hypothesis, first introduced by Roll (1986). The second chapter examines the information links across futures markets in different nations, using Vector Autoregressive (VAR)-Dynamic Conditional Correlation (DCC) model. The data comprise a large set of commodity and financial futures traded in U.S., U.K., China, Japan, Canada, and Brazil during the period from August 1998 to December 2008. The primary finding is that market interactions are relatively high for commodities for which information production generally is more diverse (metal commodities), while moderate for commodities for which information is more concentrated (agricultural commodities). Furthermore, the strength and persistence of interactions among futures markets decline after excluding the most informative markets. These findings indirectly support the breadth of information being a relevant factor in the extent of information linkage. The results also indicate that the dynamic correlation in futures markets is high in most commodity and financial futures if there is a significant bi-directional return and volatility spillover. Additionally, I estimate a market’s contribution to the price discovery process. In general, the market that has a stronger price impact and a stronger volatility spillover tends to be the market that has greater contribution or leadership in price discovery.

The decrease of car emission levels has stagnated in the latter years in Stockholm, Sweden. Since the city’s publictransit system is highly developed via its large access to areas located in the city’s outskirts, it could serve as a tool topartially replace the city's car traffic and reduce emissions. This study therefore aims to examine expected travelbehavior changes from a fare-free public transport system and investigate potential limitations when increasing thepublic transport travel degree in Stockholm. The theoretical background consists of the mode choice theory thatdissects the reasons behind travel habits, and the zero-price effect which explains the effects from abolishing priceswhen purchasing a service product. The methodological approach was conducted through a random probabilitysurvey conducted in a face-to-face mix mode survey interviews in outdoor environments and via computer-assistedtelephone interviewing. The data was then analyzed through MS Excel and SPSS to extract patterns and correlations.The results thereafter implicated preferences from the survey participants implying their desire to primarily reduce orabolish the public transport fares, which would lead to significant travel habits changes among the majority ofrespondents. This would result in a high number of both frequent car drivers and frequent public transit commutersthat would commute more by public transit and drive less.