Gasoline Demand Research Articles

A Consistent LM Type Specification Test for Semiparametric Models

This paper develops a consistent heteroskedasticity robust Lagrange Multiplier (LM) type specification test for semiparametric conditional mean models. Consistency is achieved by turning a conditional moment restriction into a growing number of unconditional moment restrictions using series methods. The proposed test statistic is straightforward to compute and is asymptotically standard normal under the null. Compared with the earlier literature on series-based specification tests in parametric models, I rely on the projection property of series estimators and derive a different normalization of the test statistic. Compared with the recent test in Gupta (2018), I use a different way of accounting for heteroskedasticity. I demonstrate using Monte Carlo studies that my test has superior finite sample performance compared with the existing tests. I apply the test to one of the semiparametric gasoline demand specifications from Yatchew and No (2001) and find no evidence against it.

Carbon Taxes and CO2 Emissions: Sweden as a Case Study

This quasi-experimental study is the first to find a significant causal effect of carbon taxes on emissions, empirically analyzing the implementation of a carbon tax and a value-added tax on transport fuel in Sweden. After implementation, carbon dioxide emissions from transport declined almost 11 percent, with the largest share due to the carbon tax alone, relative to a synthetic control unit constructed from a comparable group of OECD countries. Furthermore, the carbon tax elasticity of demand for gasoline is three times larger than the price elasticity. Policy evaluations of carbon taxes, using price elasticities to simulate emission reductions, may thus significantly underestimate their true effect. (JEL H23, L91, Q54, Q58)

휘발유 및 경유의 수요탄력성 추정에 관한 연구: 수요체계적 접근

석유자원은 현대인의 생활에 필수불가결한 자원이다. 그러나 석유자원에 대한 의존성은 기후변화라는 범지구적인 문제를 야기했으며, 이에 전 세계적으로 기후변화를 완화시키기 위한 정책적 관심이 급증하고 있다. 따라서 본 연구에서는 휘발유와 경유의 수요탄력성을 추정하기 위해 2005년부터 2015년까지의 가계동향조사 데이터를 소비체계적 접근을 통해 분석했다. 가계의 소비지출은 소득과 재화·서비스 가격에 영향을 받으며, 수요체계적인 접근을 통해 가계의 소비지출에 대한 분석은 정부의 정책결정 과정에 있어 기초적인 참고자료가 될 수 있다. 실증분석 결과 휘발유와 경유의 소득탄력성은 각각 0.958, 0.880으로 나타나 모두 양(+)의 값을 가지는 정상재로 나타났으며, 소득에 대해 비탄력적인 것을 알 수 있었다. 두 재화의 자기가격탄력성 또한 모두 음(-)의 값을 가지는 것으로 나타났으며, 휘발유의 자기가격탄력성은 절댓값이 1보다 작게나와 자기가격에 대해 비탄력적인 재화로 나타났고, 경유의 경우 자기가격탄력성의 절댓값이 1보다 크게나와 탄력적인 재화로 나타났다. 이러한 결과는 경유가 휘발유에 비해 가격에 더 민감하게 반응함을 의미한다.Oil resources are very necessary goods for modern life. But high dependency of oil resources cause various problems. Especially environmental problem that global warming caused is becoming a major concern. In this paper, using micro data from 2005 to 2015 rounds of the household income and expenditure survey data, we estimate demand elasticity for transport fuel by demand system approach. Changes in Income and price of goods impact on expenditure of household and analysis of expenditure of household by demand system could be a basis of policy making process. Our estimates show that income elasticity for gasoline and diesel fuel are 0.957528, 0.880147, respectively. The finding show that gasoline and diesel fuel are normal goods and inelastic for income changes. We also find that, both of own-price elasticity are negative. As our finding, demand for gasoline is inelastic for own-price change and demand for diesel fuel has opposite result to gasoline. This founding indicates demand for diesel fuel is more sensitive to own-price change than gasoline.

Market power and second degree price discrimination in retail gasoline markets

Empirical evidence of ‘quality-based’ second degree price discrimination is scarce. The co-existence of regular-grade 10% ethanol-blended gasoline (E10) and regular-grade non-blended gasoline (E0) in a major metropolitan city allows for second degree price discrimination to be examined while eliminating the issue of endogenous product differentiation because the two blends of gasoline are near-perfect substitutes. Using a unique data set including daily price information for 402 retailers that sell only E0 gasoline, only ethanol-blended E10 gasoline, or both blends simultaneously, I find evidence that retailers use second degree price discrimination to segment the population between perceived lower and higher quality gasoline; despite the fact that both blends can safely be used in nearly all vehicles. Additionally, I uncover two elasticities of demand for gasoline and discuss the welfare effects of price changes and market segmentation.

Rare-Event Simulation for Distribution Networks

Catastrophic events can dramatically disrupt distribution networks and supply chains; think, for example, about the threat of a hurricane, which causes significant spikes in demands for gasoline, water, and other products. What are the weak links in such a supply network, or what is the most likely way in which such a network can be disrupted? In “Rare Event Simulation for Distribution Networks,” J. Blanchet, J. Li, and M. K. Nakayama devise optimal simulation algorithms for estimating the occurrence of disruptive events in complex distribution networks. This development is noteworthy because it can be used to mitigate the impact in populations that are susceptible to these types of events, which can incur tremendous costs to society.

Skew-normal Bayesian spatial heterogeneity panel data models

ABSTRACT This paper proposes a new regression model for the analysis of spatial panel data in the case of spatial heterogeneity and non-normality. In empirical economic research, the normality of error components is a routine assumption for the models with continuous responses. However, such an assumption may not be appropriate in many applications. This work relaxes the normality assumption by using a multivariate skew-normal distribution, which includes the normal distribution as a special case. The methodology is illustrated through a simulation study and application to insurance and gasoline demand data sets. In these analyses, a simple Bayesian framework that implements a Markov chain Monte Carlo algorithm is derived for parameter estimation and inference.

Foreign Exchange Shocks and Gasoline Consumption

In an open economy, the foreign exchange rate (FER) influences incentives for the cross-border shopping of gasoline as well as the opportunity cost of using vehicles and the relative attractiveness of home versus foreign travel. Using monthly regional-level data of gasoline consumption in Iran, we estimate the impact of exchange rate shocks (both in level and volatility) on gasoline demand. We find that positive exchange rate shocks have a negative impact on total gasoline consumption as well as on vehicle users’ demand in both short-run and long-run. Furthermore, we find that in the periods that FER is less volatile, the responsiveness of gasoline demand to a change in FER is more pronounced.

Multi-objective optimization of lignocellulosic feedstock selection for ethanol production in India

Indian biofuel policy targets large scale utilization of lignocellulosic feedstock. Efficient utilization of the available feedstocks without increasing the pressure on the agricultural land, water and other inputs is essential for overall sustainability. This work focuses on recommending the optimal selection of lignocellulosic feedstock for the Indian biofuel sector considering several sustainability indicators. Six different feedstocks, namely, cotton stalk, rice husk, wheat stalk, sorghum stalk, sugarcane bagasse, and sugarcane molasses, are considered for the production of ethanol using a standardized process. The criteria for selection include life cycle greenhouse gas (GHG) emissions, energy return on investment (EROI), life cycle water use, and land use for ethanol production. Since the decision making criteria show trade-offs, multi-objective linear programming problem was formulated. The model targeted a specific amount of ethanol production without exceeding the feedstock availability, and was solved for different ethanol blending targets. The results recommended complete utilization of sugarcane molasses followed by use of feedstocks such as sugarcane bagasse and sorghum stalk. A strong trade-off was observed between GHG footprint and the other two objectives for EROI 5.08 for base case of 20% blending. Optimal feedstock combination included sugarcane molasses (26.8%), sugarcane bagasse (67%), and sorghum stalk (6.2%) for lowest EROI of 5.08. But for highest EROI of 5.6 the optimal feedstock combination included sugarcane molasses (10.2%), sugarcane bagasse (68.3%), sorghum stalk (6.2%) and cotton stalk (15.3%). The scenarios such as reduced availability of sugarcane bagasse, increase in gasoline demand, effect of data variation gives the additional insights for optimal selection of feedstocks for different blending percentages. The work concluded that sugarcane based feedstock were very critical. Moreover, the proposed approach needs to be systematically used by the decision makers for sustainable biofuel development in India.

Conditional quantile processes based on series or many regressors

Quantile regression (QR) is a principal regression method for analyzing the impact of covariates on outcomes. The impact is described by the conditional quantile function and its functionals. In this paper we develop the nonparametric QR-series framework, covering many regressors as a special case, for performing inference on the entire conditional quantile function and its linear functionals. In this framework, we approximate the entire conditional quantile function by a linear combination of series terms with quantile-specific coefficients and estimate the function-valued coefficients from the data. We develop large sample theory for the QR-series coefficient process, namely we obtain uniform strong approximations to the QR-series coefficient process by conditionally pivotal and Gaussian processes. Based on these two strong approximations, or couplings, we develop four resampling methods (pivotal, gradient bootstrap, Gaussian, and weighted bootstrap) that can be used for inference on the entire QR-series coefficient function.We apply these results to obtain estimation and inference methods for linear functionals of the conditional quantile function, such as the conditional quantile function itself, its partial derivatives, average partial derivatives, and conditional average partial derivatives. Specifically, we obtain uniform rates of convergence and show how to use the four resampling methods mentioned above for inference on the functionals. All of the above results are for function-valued parameters, holding uniformly in both the quantile index and the covariate value, and covering the pointwise case as a by-product. We demonstrate the practical utility of these results with an empirical example, where we estimate the price elasticity function and test the Slutsky condition of the individual demand for gasoline, as indexed by the individual unobserved propensity for gasoline consumption.

Design and development of interdisciplinary learning in a new oncology unit of study in the new integrated pharmacy curriculum

Since the first oil crisis in the 1970s, demand for energy has undergone significant transformations. This paper intends to model the demand by the transportation sector for the petroleum products in Iran. We propose a structural time-series model with an underlying trend. After transforming the model to state-space form, we apply the Kalman Filter algorithm to estimate the parameters of the model for the period between 1980 and 2016. Results indicate that the underlying trend for gasoline and diesel demand by the transportation sector is non-linear. This trend can be used as proxy applicable for studying those variables which could enable us to explain technological improvements and changes in consumer preferences for gasoline and diesel over time. Results also show that technological improvements and changes in consumers’ behavior led to a decrease in the consumption of diesel in recent years while it has no decreasing effect on gasoline. Demand functions show that gasoline and diesel are inelastic. Gasoline price elasticity of demand ranges between −0.17 and −0.24. The price elasticity of demand for diesel is very low and statistically insignificant at the 5% level. The income elasticity of demand for gasoline and diesel ranges between 0.34 and 0.48, and between 0.33 and 0.52 respectively, being strongly statistically significant. Finally, this paper proposes that the price of fuel should be determined by market mechanism to reduce fuel consumption.

Asymmetric price effects on gasoline consumption: evidence and Implications

ABSTRACTThe increased volatility of gasoline prices in the last 10 years has renewed the interest in how gasoline demand responds to new changes. Using recent urban household survey and Metropolitan Statistical Areas (MSA) level gasoline prices, this paper examines whether rising and falling fuel prices have symmetric effects on gasoline consumption. The findings indicate that an increase in gasoline prices induces larger behavioural responses than a decrease in gasoline prices would do. These results not only challenge the perfect price reversibility assumption used in many energy demand models, but also bear important implications for evaluating gasoline tax policies and demand projections.

Development of Fuel/Engine Systems—The Way Forward to Sustainable Transport

Abstract The global demand for transport energy is large, growing, and primarily met by petroleum-derived liquid fuels powering internal combustion engines (ICEs). Moreover, the demand for jet fuel and diesel is projected to grow faster than the demand for gasoline in the future, and is likely to result in low-octane gasoline components becoming more readily available. Significant initiatives with varying motivations are taking place to develop the battery electric vehicle (BEV) and the fuel cell as alternatives to ICE vehicles, and to establish fuels such as biofuels and natural gas as alternatives to conventional liquid fuels. However, each of these alternatives starts from a very low base and faces significant barriers to fast and unrestrained growth; thus, transport—and particularly commercial transport—will continue to be largely powered by ICEs running on petroleum-based liquid fuels for decades to come. Hence, the sustainability of transport in terms of affordability, energy security, and impact on greenhouse gas (GHG) emissions and air quality can only be ensured by improving ICEs. Indeed, ICEs will continue to improve while using current market fuels, through improvements in combustion, control, and after-treatment systems, assisted by partial electrification in the form of hybridization. However, there is even more scope for improvement through the development of fuel/engine systems that can additionally leverage benefits in fuels manufacture and use components that may be readily available. Gasoline compression ignition (GCI), which uses low-octane gasoline in a compression ignition engine, is one such example. GCI would enable diesel-like efficiencies while making it easier to control nitrogen oxides (NOx) and particulates at a lower cost compared with modern diesel engines. Octane on demand (OOD) also helps to ensure optimum use of available fuel anti-knock quality, and thus improves the overall efficiency of the system.

A detailed analysis and performance analysis of diesel blended with eucalyptus/pine seed oil on a CI engine

In the recent era, an alternative fuel plays a vital role in the automobile and petroleum industry because of the fuel (petrol and diesel) demand, development of industries and diminution of natural resources. The economic way to produce bio-diesel from various resources and mixing or blending bio-oil with diesel, this method can widely follow by all developing and developed countries. This paper mainly examines the consumption of pine oil, methanol and eucalyptus blend with diesel in a diesel engine, the performance and emission characteristics of three different blends of pine oil, methanol, and eucalyptus oils. It is found that the above said bio-diesel blend is the best substitute for the pure diesel which plays a vital role against natural resource which may help the future generation. From these reports, the outcome of bio-diesel against engine power, economy, durability and combustion, and the corresponding effect factors are surveyed and analysed in detail.

Structural time series model for energy demand in Iran's transportation sector

Since the first oil crisis in the 1970s, demand for energy has undergone significant transformations. This paper intends to model the demand by the transportation sector for the petroleum products in Iran. We propose a structural time-series model with an underlying trend. After transforming the model to state-space form, we apply the Kalman Filter algorithm to estimate the parameters of the model for the period between 1980 and 2016. Results indicate that the underlying trend for gasoline and diesel demand by the transportation sector is non-linear. This trend can be used as proxy applicable for studying those variables which could enable us to explain technological improvements and changes in consumer preferences for gasoline and diesel over time. Results also show that technological improvements and changes in consumers’ behavior led to a decrease in the consumption of diesel in recent years while it has no decreasing effect on gasoline. Demand functions show that gasoline and diesel are inelastic. Gasoline price elasticity of demand ranges between −0.17 and −0.24. The price elasticity of demand for diesel is very low and statistically insignificant at the 5% level. The income elasticity of demand for gasoline and diesel ranges between 0.34 and 0.48, and between 0.33 and 0.52 respectively, being strongly statistically significant. Finally, this paper proposes that the price of fuel should be determined by market mechanism to reduce fuel consumption.

Increasing stringent regional environmental regulations impact gasoline demand in China

Forecasting of the gasoline demand is a key issue for refineries in China. Some international energy organizations (IEOs) and international oil companies have estimated the gasoline demand in China for the next 20 years. These organizations and companies used the assumptions that the technology of clean-energy vehicles do not gain significant progresses, and they do not consider the trends of policy changes of different provinces in China. So the forecast of IEOs (international energy organizations) may not reflect the real trend of gasoline demand in China. In this paper, we considered more stringent regional environmental regulations and the possibility of technological progress in clean-energy vehicles, using the bottom to up method to estimate the gasoline demand of all 30 provinces in China, adding them up, to gain the forecast of total gasoline demand in China for the next twenty years. The result shows that gasoline demand will increase slower and decrease faster than the estimation of some mean IEOs (international energy organizations).

Biofuels policies and fuel demand elasticities in Brazil

Biofuels are often seen by policymakers as solutions to concerns about the environment, energy diversification, and rural development. To understand the impacts of biofuel policy, however, it is important to understand demand elasticities. Brazil, a leader in biofuels, provides a unique setting to increase our knowledge about biofuel policy and the interactions within and between the gasoline and ethanol markets. We estimate own-price, cross-price, and income elasticities of the demand for ethanol and gasoline using a novel instrumental variable approach to control for the inherent endogeneity between supply and demand. This results in own-price elasticities for both fuels higher than previous literature suggests: approximately − 0.9 for gasoline and − 1.5 for ethanol. Income elasticities for both fuels are approximately 0.8. We also examine the elasticity impacts following the introduction of flex-fuel cars into the Brazilian market. By estimating the model with over 100 subsamples across time, we find that cross-price elasticities become positive, significant, and increasing, but only after larger market penetration of flex-fuel cars, which occurred approximately three years after their introduction.

Redistributive Effects of Gasoline Prices

Consumers face significantly different gasoline prices across gas stations. Using gasoline price data obtained from 98,753 gas stations within the U.S., it is shown that such differences can be explained by a model utilizing the gasoline demand of consumers depending on their income and commuting distance/time, where the pricing strategies of both gas stations and refiners are taken into account. The corresponding welfare analysis shows that there are significant redistributive effects of gasoline price changes among consumers, where the welfare costs of an increase in gasoline prices are found to be higher for lower income consumers.

Clustered Covariate Regression

High covariate dimensionality is an increasingly occurrent phenomenon in model estimation. A common approach to handling high-dimensionality is regularisation, which requires sparsity of model parameters. However, sparsity may not always be supported by economic theory or easily verified in some empirical contexts; severe bias and misleading inference can occur. This paper introduces a grouped parameter estimator (GPE) that circumvents this problem by using a parameter clustering technique. The large sample properties of the GPE hold under fairly standard conditions including a compact parameter support that can be bounded away from zero. Monte Carlo simulations demonstrate the excellent performance of the GPE relative to competing estimators in terms of bias and size control. Lastly, an empirical application of the GPE to the estimation of price and income elasticities of demand for gasoline illustrates the practical utility of the GPE.

Income and price elasticities of gasoline demand: An empirical analysis for Russia

Income and price elasticities of gasoline demand: An empirical analysis for Russia

Nonparametric Instrumental-Variable Estimation

In this article, we introduce the commands npiv and npivcv, which implement nonparametric instrumental-variable (NPIV) estimation methods without and with a cross-validated choice of tuning parameters, respectively. Both commands can impose the constraint that the resulting estimated function is monotone. Using such a shape restriction may significantly improve the performance of the NPIV estimator (Chetverikov and Wilhelm, 2017, Econometrica 85: 1303–1320) because the ill-posedness of the NPIV estimation problem leads to unconstrained estimators that suffer from particularly poor statistical properties such as high variance. However, the constrained estimator that imposes the monotonicity significantly reduces variance by removing nonmonotone oscillations of the estimator. We provide a small Monte Carlo experiment to study the estimators’ finite-sample properties and an application to the estimation of gasoline demand functions.