- Open Access
Market collusion and regime analysis in the US gasoline market
© The Author(s) 2018
- Received: 8 January 2018
- Accepted: 24 April 2018
- Published: 21 May 2018
This article is concerned with disequilibrium regime switching model to capture different regimes in the US gasoline markets. The purpose is to illustrate potential regimes in gasoline market. Following a suggestion in Hunter and Tabaghdehi (Cointegration and US regional gasoline prices: testing market efficiency from the stationarity of price proportions. Brunel University Working Paper, 13-03, 2013a), gasoline markets may not be efficient either across regions or within local markets. The Markov model may also be used as a benchmark to make comparison with other methods. The finding specifies that deviations from long-run equilibrium have an effect on gasoline price dynamics and captures two different regimes of supply and demand in this market.
- Regime switching
- Energy market efficiency
Global demand for gasoline is affected by technological change, global population growth, motor vehicle ownership and heating oil consumption. Since the last decade, we can clearly observe that gasoline prices are highly volatile and this makes price modelling and forecasting, and risk management very challenging. Global warming and greenhouse gas emissions interact with the demand for gasoline. However, political instability in the oil-producing countries caused a remarkable disruption in energy supply, market equilibrium and prices since the 1990s.
In the gasoline market the equilibrium price is set at the intersection point of market aggregated demand and supply. Gasoline demand modelling, following Ramsey et al. (1975), Dahl (1979, 1995), and Yang and Hu (1984), considers supply and demand to emphasize supply along with demand in the gasoline market, and also the level of supply-side intervention and policy in the gasoline market. Relatedly, Dahl and Dugga (1996) studied price elasticity of demand and supply in US oil market and indicated that US oil reserves to be elastic. Furthermore, Hunter and Tabaghdehi (2013a) examined gasoline price behaviour across different regions in the long-run and the short-run, specifying that the market structures and price dynamics may differ across regions.
For a product such as gasoline, there is little quality uncertainty as the quality of the product is regulated for reasons of safety and the manufacturer needs to meet a standard for the product to avoid litigation from the public, corporate employees and the motor vehicle manufacturers who might engage in a class action where such failure to impact their reputation and affect sales.
Price uncertainty is an important issue, and it might reflect the potential for disequilibrium in the energy market (Arrow 1962). Hence in this analysis, using different regime switching models we investigate the market disequilibrium caused by imperfect competition or price leadership in gasoline market. Yang and Hu (1984) formulate an endogenous switching model to examine a gasoline market but their analysis paid no attention to non-stationarity. Hence, in this article we formulate two different switching models more adequately and examine their behaviour and the nature of the different regimes in the US gasoline market.
The study of demand and the notion of an economy or a market is not in full equilibrium which was investigated in early literature by Hicks (1936), Arrow (1962), Maddala and Nelson (1974), Rosen and Quandt (1978), Maddala (1983), Muellbauer (1983), Andrews and Nickell (1985) and Robinson (1994). Under disequilibrium hypothesis only one regime can be observed at the time.1 However, the disequilibrium approach derived initially to estimate demand and supply equation in a static context was not developed to handle non-stationary series. Here a static switching structure is devised to identify demand via the min condition and to measure the long-run market failure.
Q t = S t if D t > S t this implies there is excess demand and quantity transacted in the market is equal to quantity supplied
Q t = D t if D t < S t this implies there is excess supply in the market and quantity transacted in the market is equal to quantity demanded.
Yang and Hu (1984) formulated a gasoline market model testing disequilibrium that may have been caused by either imperfect price adjustment by buyers and sellers or institutional price restrictions. In Yang and Hu (1984) they take no account of non-stationarity or the potential that the estimations may need to handle an autoregressive unit root. In their estimation using the errors is serially correlated and the test statistics are non-standard.
The distribution of the OLS estimator in Eq. (2) with non-stationary series is non-standard, and the parameters are super-consistent when there is cointegration, although the t tests are not well defined. Hence, the Phillips and Hansen fully modified OLS estimator computes an estimate of the long-run variance that corrects the regression to take account of the serial correlation associated with the potential unit root in the error. With the exception of the conventional least squares regression result that applies with truly exogenous variables such as indicators, dummies and time, the estimations and inference are valid as long as the dependent variable and any potentially endogenous regressors are I(1), Kitamura and Phillips (1995).
To identify the dummy for demand (d d ) and supply (d s ), we evaluated relative price from the following equations, where if ΔlpRetail Price − ΔlpConsumer Price Index > 0 indicates that the relative price is increasing and D > S which classifies d s , otherwise (ΔlpRetail Price − ΔlpConsumer Price Index < 0) there is a decrease in the relative price identifying that D < S and that indicates d d .
Static disequilibrium switching estimation 1
Bartlett weighs, truncation lag = 64
− 3.78** [0.00]
− 3.43** [0.00]
P res t
− 1.005 [0.92]
P dst t
− 3.26 [0.74]
P w t
− 0.02 [0.98]
− 0.34 [0.73]
The positive sign of γ5 indicates that the price of gasoline affects a gasoline supply positively that is consistent with economic theory. Its significance value of 3.07 identifies that refiners are sensitive to gasoline price changes in changing output level. However, the negative sign of γ 6 and γ 7 indicates that residual fuel oil and distillate fuel oil price rises will reduce the supply of gasoline so the refiner produces for these markets where possible and substitute away from gasoline. While insignificant coefficients γ 6 and γ 7 identify that changes in gasoline production cannot be attributed to fluctuations in price of residuals and distillate fuel oil. The crude oil price, which explains the effect of the input price on gasoline supply, has an expected negative sign but statistically insignificant identifying that change in gasoline production cannot be impacted by input price fluctuations. Finally, the refineries net input of crude oil explains the scale effect in the supply equation, has a negative sign and is statistically insignificant indicating that it appears not to affect gasoline supply.
Static disequilibrium switching estimation 2
Bartlett weighs, truncation lag = 64
− 8.91** [0.00]
− 11.19** [0.00]
− 3.21** [0.00]
− 0.82 [0.41]
P W t
− 1.18 [0.24]
Comparing above estimations 3 and 4 via the regression that imposes the switch, the variables used in Eq. 4 seem to explain the model more appropriately as most of the variables are statistically significant. The significant coefficient subject to all series being I(1) implies that this is a long-run relation. This suggests that models based on the supply and demand regimes give rise to meaningful long-run equations.
Here, the intention is to use Markov switching method as a mechanism to identify supply and demand regimes in the long-run. Each regime is characterized by a different parameterization. We focus on modelling the gasoline market as a single market and to observe both sides of the market. The primary method to estimate disequilibrium models was investigated in a static context by Fair and Jaffee (1972), Fair and Kelejian (1974) and Maddala and Nelson (1974). Maddala (1983) provides a useful summary of this early literature and compares this with the same latent effects captured by error correction models.
Considering an static model would usually be poorly specified especially in relation to serial correlation. In Robinson (1994) a number of corrections were applied to take account of this and in Davidson et al. (1978) the notion of disequilibrium in dynamic equations was embedded in error correction models. Furthermore, Muellbauer (1983) developed at the macro-level continuous switching when markets are aggregated. Maddala (1983) discussed disequilibrium where the latent variable equilibrium term is determined by switching, and this is embedded in an error correction term.
The regime switching ECM can be explained as an expanded linear error correction model by allowing the short-run parameters to switch in different regimes. Hence a Markov switching error correction model (MSECM) can be used to describe the short-run variation in gasoline sales. MSECM signifies that when the system is in a stable state the error correction takes place and in the unstable state there are deviations from the long-run equilibrium that cannot be corrected through the ECM. In terms of the disequilibrium model, these would be the same when there is correction to another equilibrium state.
Here the error correction model is also embedded in a Markov switching equation and the Markov regime switching error-correction model (MRSECM) is used to determine regimes that are latent in the data.
Dynamic disequilibrium switching
Variables in Eq. 7
Variables in Eq. 7
Variables in Eq. 7
Assuming stationarity of price proportion based on conventional inference, the two correction terms in Table 3 are significant and this implies negative reaction of gasoline market prices to CPI as indicative of demand responds, and positive reaction of gasoline market price to PPI as indicative of supply responds.
In this paper, we applied regime switching model on market data to identify any potential disequilibrium in the long-run. Long-run disequilibrium in energy markets indicates the need to consider the demand and supply management to improve energy market efficiency and stability. The results on the disequilibrium study imply that the long-run gasoline price dynamics may not always correct the system. Furthermore, the Markov regime switching model with two different regimes identifies there is a significant effect of regular gasoline costs, gas retail real price, residual fuel oil price, and distillate fuel oil price on retail gasoline prices in the USA and consequently on the stability of correction to these regimes.
Here it has been shown that the switch model can be estimated by a single regression with the series being scaled by dummy variables of DS and DD. The dummy DS is 1 when the change in the relative price exceeds zero, while DD is 1 when the change in the relative price is less than zero. With sufficient data it should be possible to utilize the two-step regression method of Engle and Granger (1987) to test whether the regression residuals are stationary. Unfortunately, the switch increases the number of parameters as the demand and supply equations are being computed simultaneously so with more than two hundred observations the available software cannot compute the critical value of Dickey Fuller test. To determine the importance of the parameters in the cointegrating regression, they are computed using the fully modified estimation procedure of Phillips and Hansen (1990). The semi-parametric method corrects the estimator for both autoregressive and moving average errors, and this implies that it is possible to determine the significance of these parameters via conventional inference as long as the regressors are I(1) except for series that are truly exogenous.
The data are then separated using the relative as compared with absolute price changes. This separation is applied to the static model of Yang and Hu (1984) on a more recent data set. However, the static model only has a long-run interpretation. Based on the estimation results, the demand curve seems well defined, while it is less easy to interpret the second relation as a supply equation. A more recent approach to demand has also been used to define this equation and compared with a new supply equation, but this worked less well than the model of Yang and Hu (1984). Another interpretation of the supply equations is that the long-run supply function is flat implying firms set price as a mark-up of their cost.
The final analysis relates to a dynamic model for real gasoline prices in the USA from 1993 to 2012. This approach is based on an error correction model where the adjustment coefficients switch between regimes. Disequilibrium is captured by the correction, but this may be unstable or relate to a further equilibrium. Estimation of the Markov Switching ECM indicates that deviations from long-run equilibrium have an effect on gasoline price dynamics. Also the result signifies that regular gasoline costs (insurance and freight), gas retail real price, residual fuel oil price, and distillate fuel oil price significantly affect the relative real gasoline price in the market. More specifically it demonstrates that there are two different regimes of Supply and Demand in the US gasoline market indicating that the market collusion may be less concern of this market in the USA.
Muellbauer (1983) suggested at the aggregate level the switch would be smoothed that gave rise to continuous switching.
Hotelling (1932) determined that profit-maximising price-taking firms based their prices on selection of their input and output levels. Thus the crude oil price plays an important role in the supply function for the gasoline market.
No. 2 distillate fuel oil is used in high-speed diesel engines, such as those in railroad locomotives, trucks, and automobiles.
Hotelling (1932) identified that profit-maximising price-taking firms based to their prices they determine their input and output level. Thus crude oil price plays an important role in the supply function of the gasoline market.
By knowing which observation of the dependent variable of y was generated by which regime a Chow test can examine whether σ1 = σ2 and β1′ = β2′. However if this is unknown and it is not clear which of the dependent variable (y) was generated by, then Goldfeld and Quandt’s D-method for switching regression might clarify this problem.
Pg is gasoline retail price, PGAS is gas retail real price to analyse the substitute effect in the demand process, CPI is consumer price index, PPI is producer price index, COST is unleaded regular gasoline costs (insurance and freight), PWTI is WTI spot price, Pres is residual fuel oil price and Pdst is distillate fuel oil price.
ST carried out the regime switching studies to analyse the US gasoline market equilibrium and drafted the manuscript. The author read and approved the final manuscript.
The present study is based on the same working article and can be accessed on: “http://regents.ac.uk/media/2390943/rwpbm1602-tabaghdehi-s.pdf” (Tabaghdehi 2016).
The author declares that she has no competing interests.
Ethics approval and consent to participate
Availability of data and materials
Data are available in request.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
- Andrews M, Nickell S (1985) A disaggregated disequilibrium model of the labour market. Oxford Econ Pap 38(3):386–402View ArticleGoogle Scholar
- Arrow K (1962) Economic welfare and the allocation of resources for invention. Princeton University Press, Princeton. ISBN: 0-87014-304-2Google Scholar
- Dahl CN (1979) Consumer adjustment to a gasoline tax. Rev Econ Stat 61:427–432View ArticleGoogle Scholar
- Dahl CN (1995) Demand for transportation fuels: a survey of demand elasticities and their components. J Energy Lit 1:3–27Google Scholar
- Dahl CN, Dugga TE (1996) U.S. energy product supply elasticities: a survey and application to the U.S. oil market. Resour Energy Econ 18(3):243–263View ArticleGoogle Scholar
- Davidson JEH, Hendry DF, Srba F, Yeo S (1978) Econometric modelling of the aggregate time series relationships between consumers expenditure and income in the United Kingdom. Econ J 88:661–692View ArticleGoogle Scholar
- Engle RF, Granger CWJ (1987) Co-integration and error-correction: representation, estimation and testing. Econometrica 55:251–276View ArticleGoogle Scholar
- Fair RC, Jaffee DM (1972) Methods of estimation for markets in disequilibrium. Econometrica 40:497–514View ArticleGoogle Scholar
- Fair RC, Kelejian HH (1974) Methods of estimation for markets in disequilibrium: a further study. Econometrica 42:177–190View ArticleGoogle Scholar
- Hicks JR (1936) Keynes’ theory of employment. Econ J 46(182):238–253View ArticleGoogle Scholar
- Hotelling H (1932) Edgeworth’s taxation paradox and the nature of demand and supply functions. J Political Economy 40(5):577–616View ArticleGoogle Scholar
- Hunter J, Tabaghdehi SAH (2013a) Cointegration and US regional gasoline prices: testing market efficiency from the stationarity of price proportions. Brunel University Working Paper, 13-03Google Scholar
- Hunter J, Tabaghdehi SAH (2013b) Extracting long-run information from energy prices: the role of exogeneity. Brunel University Working Paper, 13-19Google Scholar
- Kitamura Y, Phillips PC (1995) Efficient IV estimation in nonstationary regression. Econom Theory 11(05):1095–1130View ArticleGoogle Scholar
- Maddala GS (1983) Methods for estimation for models of markets with bounded price variation. Int Econ Rev 24:361–387View ArticleGoogle Scholar
- Maddala GS, Nelson FD (1974) Maximum likelihood methods for models of markets in disequilibrium. Econometrica 42:1013–1030View ArticleGoogle Scholar
- Muellbauer J (1983) Surprises in the consumption function. Econ J 93:34–50View ArticleGoogle Scholar
- Phillips PCB, Hansen BE (1990) Statistical inference in instrumental variables regression with I(1) processes. Rev Econ Stud 57(1):99–125View ArticleGoogle Scholar
- Ramsey JR, Rasche R, Allen B (1975) An analysis of the private and commercial demand for gasoline. Rev Econ Stat 57:502–507View ArticleGoogle Scholar
- Robinson PM (1994) Efficient tests of nonstationary hypotheses. J Am Stat Assoc 89(428):1420–1437View ArticleGoogle Scholar
- Rosen HS, Quandt RE (1978) Estimation of a disequilibrium aggregate labor market. Rev Econ Stat 40:371–379View ArticleGoogle Scholar
- Tabaghdehi SAH (2016) Energy price uncertainty and market collusion: disequilibrium regime switching model. Regents University Working Paper, 24Google Scholar
- Yang BM, Hu TW (1984) Gasoline demand and supply under a disequilibrium market. Energy Econ 6:276–282View ArticleGoogle Scholar