Measuring long‐run gasoline price elasticities in urban travel demand

PurposeThis study examines the long term effects of macroeconomic fundamentals on apartment price dynamics in major metropolitan areas in Sweden and Germany.Design/methodology/approachThe main approach is panel cointegration analysis that allows to overcome certain data restrictions such as spatial heterogeneity, cross-sectional dependence, and non-stationary, but cointegrated data. The Swedish dataset includes three cities over a period of 23 years, while the German dataset includes seven cities for 29 years. Analysis of apartment price dynamics include population, disposable income, mortgage interest rate, and apartment stock as underlying macroeconomic variables in the model.FindingsThe empirical results indicate that apartment prices react more strongly on changes in fundamental factors in major Swedish cities than in German ones despite quite similar development of these macroeconomic variables in the long run in both countries. On one hand, overreactions in apartment price dynamics might be considered as the evidence of the price bubble building in Sweden. On the other hand, these two countries differ in institutional arrangements of the housing markets, and these differences might contribute to the size of apartment price elasticities from changes in fundamentals. These arrangements include various banking sector policies, such as mortgage financing and valuation approaches, as well as different government regulations of the housing market as, for example, rent control.Originality/valueIn distinction to the previous studies carried out on Swedish and German data for single-family houses, this study focuses on the apartment segment of the market and examines apartment price elasticities from a long term perspective. In addition, the results from this study highlight the differences between the two countries at the city level in an integrated long run equilibrium framework.

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Income elasticity of overnight stays over seven decades

Tourism Economics ◽

10.1177/1354816618803781 ◽

2018 ◽

Vol 24 (8) ◽

pp. 1015-1028 ◽

Cited By ~ 4

Author(s):

Martin Falk ◽

Xiang Lin

Keyword(s):

Purchasing Power Parity ◽

Travel Demand ◽

Income Elasticity ◽

Power Parity ◽

Income Elasticities ◽

Long Run ◽

The Stability ◽

Overnight Stays ◽

New Evidence ◽

Specific Country

This article provides new evidence on the stability of the long-run income elasticity of tourism and travel demand by use of the recently developed smooth time-varying cointegration regression model. The estimations control for relative purchasing power parity of the source country and make use of a specific country dataset where domestic and foreign overnight stays are available over a longer period of time (Switzerland, 1934–2015). Results show that the income elasticity of foreign overnight stays peaks at approximately two in the early 1960s, drops to around one in the early 1980s and from then on remains stable until the end of the sample. Domestic income elasticity reaches its highest levels in the 1930s, then steadily falls towards one in the mid-1960s, and therefrom remains stable until 2015. Different phases in the tourism area life cycle might be a major explanatory factor for variation in income elasticities over time.

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Time-Series Forecasting Models for Gasoline Prices in China

International Journal of Economics and Finance ◽

10.5539/ijef.v10n12p43 ◽

2018 ◽

Vol 10 (12) ◽

pp. 43

Author(s):

Feng Xu ◽

Mohamad Sepehri ◽

Jian Hua ◽

Sergey Ivanov ◽

Julius N. Anyu

Keyword(s):

Time Series ◽

Arima Model ◽

Time Series Forecasting ◽

Gasoline Prices ◽

Support Vector ◽

Grey System ◽

Gasoline Price ◽

Forecasting Models ◽

Long Run ◽

Environmental Protection Policies

Accurate prediction of gasoline price is important for the automobile makers to adjust designs and productions as well as marketing plans of their products. It is also necessary for government agencies to set effective inflation monitoring and environmental protection policies. To predict future levels of the gasoline price, due to difficulties of obtaining accurate estimates of influential external factors, data driven time-series forecasting models thus become more suitable given the convenience and practicability they are providing. In this paper, five popular time-series forecasting models, i.e., ARIMA-GARCH, exponential smoothing, grey system, neural network, and support vector machines models, are applied to predict gasoline prices in China. Comparing the performances of these models, it is noted that for this specific time series, a parsimonious ARIMA model performs the best in predicting the gasoline prices for a short time horizon, while for the medium length and long run the SVR and FNN models outperforms others respectively.  

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Understanding Spatio-Temporal Characteristics of Urban Travel Demand Based on the Combination of GWR and GLM

Sustainability ◽

10.3390/su11195525 ◽

2019 ◽

Vol 11 (19) ◽

pp. 5525 ◽

Cited By ~ 6

Author(s):

Jinjun Tang ◽

Fan Gao ◽

Fang Liu ◽

Wenhui Zhang ◽

Yong Qi

Keyword(s):

New York ◽

Travel Demand ◽

Geographical Area ◽

Temporal Characteristic ◽

Temporal Variations ◽

Road Density ◽

Transit System ◽

Factors Affecting ◽

Urban Travel ◽

Spatio Temporal

Taxis are an important part of the urban public transit system. Understanding the spatio-temporal variations of taxi travel demand is essential for exploring urban mobility and patterns. The purpose of this study is to use the taxi Global Positioning System (GPS) trajectories collected in New York City to investigate the spatio-temporal characteristic of travel demand and the underlying affecting variables. We analyze the spatial distribution of travel demand in different areas by extracting the locations of pick-ups. The geographically weighted regression (GWR) method is used to capture the spatial heterogeneity in travel demand in different zones, and the generalized linear model (GLM) is applied to further identify key factors affecting travel demand. The results suggest that most taxi trips are concentrated in a fraction of the geographical area. Variables including road density, subway accessibility, Uber vehicle, point of interests (POIs), commercial area, taxi-related accident and commuting time have significant effects on travel demand, but the effects vary from positive to negative across the different zones of the city on weekdays and the weekend. The findings will be helpful to analyze the patterns of urban travel demand, improve efficiency of taxi companies and provide valuable strategies for related polices and managements.

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