This thesis presents an analysis of the pricing behaviour of firms in Canadian retail gasoline markets. The time series of retail prices for certain Canadian cities can be categorized as exhibiting one of two distinct patterns. In many cities, retail prices remain unchanged for many weeks at a time, despite frequent changes to the wholesale gasoline price. In other cities, retail prices cycle, increasing sharply, and declining more slowly. This thesis addresses questions arising from the observation of these patterns. The first essay considers a theoretical model of price setting behavior, and asks whether the number of stations operated by each firm in a market can determine whether constant prices or price cycles are observed. Constant prices are found to exist only when firms are of similar size. On the other hand, cycle equilibria can be constructed when the firms are of similar size, but also when their sizes differ greatly. Evidence of a negative relationship between price stability and the presence of small firms is also found through an examination of a panel data set of retail prices for a number of Canadian cities. The second essay examines the response of retail prices to wholesale price movements in the presence of a retail price cycle. A simple model based on the predictions of the theory is constructed, and estimated using data for the city of Windsor, Ontario. I find that a new cycle is initiated by a price increase whenever the distance between the previous retail price and the current wholesale price becomes sufficiently small. In addition, retail prices are found to be more responsive to wholesale prices over the increasing portion of the cycle. Finally, when the asymmetric error correction model of Borenstein, Cameron, and Gilbert (1997) is estimated, a more rapid response to wholesale price increases than to decreases is indicated. This asymmetry is shown to be consistent with my structural model, which thus provides an additional potential explanation for the regularities found in previous studies.

This thesis presents an analysis of the pricing behaviour of firms in Canadian retail gasoline markets. The time series of retail prices for certain Canadian cities can be categorized as exhibiting one of two distinct patterns. In many cities, retail prices remain unchanged for many weeks at a time, despite frequent changes to the wholesale gasoline price. In other cities, retail prices cycle, increasing sharply, and declining more slowly. This thesis addresses questions arising from the observation of these patterns. The first essay considers a theoretical model of price setting behavior, and asks whether the number of stations operated by each firm in a market can determine whether constant prices or price cycles are observed. Constant prices are found to exist only when firms are of similar size. On the other hand, cycle equilibria can be constructed when the firms are of similar size, but also when their sizes differ greatly. Evidence of a negative relationship between price stability and the presence of small firms is also found through an examination of a panel data set of retail prices for a number of Canadian cities. The second essay examines the response of retail prices to wholesale price movements in the presence of a retail price cycle. A simple model based on the predictions of the theory is constructed, and estimated using data for the city of Windsor, Ontario. I find that a new cycle is initiated by a price increase whenever the distance between the previous retail price and the current wholesale price becomes sufficiently small. In addition, retail prices are found to be more responsive to wholesale prices over the increasing portion of the cycle. Finally, when the asymmetric error correction model of Borenstein, Cameron, and Gilbert (1997) is estimated, a more rapid response to wholesale price increases than to decreases is indicated. This asymmetry is shown to be consistent with my structural model, which thus provides an additional potential explanation for the regularities found in previous studies. / Arts, Faculty of / Vancouver School of Economics / Graduate

Researchers have difficulty modeling the influence of retailing attributes on consumer choice. The literature of retailing that has dealt with this issue has conventionally used experimental data for estimating the influence of retailing attributes on consumer behavior. The present research applies hedonic analysis to the measurement of the value of retailing attributes. This is accomplished by applying hedonic specifications to supply and demand models for the retail sales of unleaded gasoline for the purpose of estimating the influence of specified retailing attributes on retail prices. Four retailing attributes-accessibility, convenience, service, and competition-were expected to have a determinable value that was measurable through hedonic specifications. Spatial competition was expected to influence retail prices by lowering them. The value of retailing attributes was expected to be variable relative to household income. It was found that the value of the specified retailing attributes could be isolated and determined. The application of hedonic analysis to the supply and demand of unleaded gasoline provided a relatively precise and consistent market value, which was represented by the "ask" and "bid" implicit prices of these retailing attributes. Spatial competition was seen to exert an important influence on retailing, tending to lower retail prices. The value of retailing attributes was found to be variable relative to household income. The relative consistency and precision of hedonic analysis in the measurement of the value of retailing attribute was reinforced insofar as the findings were consistent with generally accepted notions of retail marketing and consumer behavior as represented in the literature in the field.

<p> In the spring of 1983, when this project was in its most preliminary stages, a simple hypothesis was put forward. This hypothesis suggested that auto users would react to rising retail gasoline prices by switching to an alternative mode of transportation, such as public transit. It was thought that, since any increase in fuel costs could be spread out among all transit users, public transit would become an attractive alternative to the private automobile in an individual's transportation mode decision as retail gasoline prices increased. Therefore, a positive relationship was anticipated-to exist between public transit ridership and retail gasoline prices. </p> <p> Having established the hypothesis to be investigated, an extensive review of current literature associated with the hypothesis was completed. This review presented conflicting opinions concerning the hypothesis, and also suggested that other variables were more important than the price of retail gasoline in affecting an individual's transportation mode decision. </p> <p> Unfortunately, the literature review did not suggest any relevant method of analysis for this project. It was decided that, for reasons to be discussed later, linear regression would be the method of analysis. The results of the application of a number of linear regression models to data obtained for the Hamilton study area indicated that no definitive statement could be made with respect to the hypothesis of this project. This lack of significant results was attributed to extraneous variance created by certain variables that could not be controlled. </p> <p> However, as a contribution to knowledge, this project provides a basis on which future studies can be built. If the extraneous variance that is discussed in this project can be eliminated in future studies, then- it may be possible to obtain more significant results with respect to the hypothesis that public transit ridership is positively related to retail gasoline prices. </p> / Thesis / Bachelor of Arts (BA)

This dissertation consists of three essays. Chapter II examines the possible asymmetric response of gasoline prices to crude oil price changes using an error correction model with GARCH errors. Recent papers have looked at this issue. Some of these papers estimate a form of error correction model, but none of them accounts for autoregressive heteroskedasticity in estimation and testing for asymmetry and none of them takes the response of crude oil price into consideration. We find that time-varying volatility of gasoline price disturbances is an important feature of the data, and when we allow for asymmetric GARCH errors and investigate the system wide impulse response function, we find evidence of asymmetric adjustment to crude oil price changes in weekly retail gasoline prices Chapter III discusses the relationship between fiscal deficit and exchange rate. Economic theory predicts that fiscal deficits can significantly affect real exchange rate movements, but existing empirical evidence reports only a weak impact of fiscal deficits on exchange rates. Based on US dollar-based real exchange rates in G5 countries and a flexible varying coefficient model, we show that the previously documented weak relationship between fiscal deficits and exchange rates may be the result of additive specifications, and that the relationship is stronger if we allow fiscal deficits to impact real exchange rates non-additively as well as nonlinearly. We find that the speed of exchange rate adjustment toward equilibrium depends on the state of the fiscal deficit; a fiscal contraction in the US can lead to less persistence in the deviation of exchange rates from fundamentals, and faster mean reversion to the equilibrium. Chapter IV proposes a kernel method to deal with the nonparametric regression model with only discrete covariates as regressors. This new approach is based on recently developed least squares cross-validation kernel smoothing method. It can not only automatically smooth the irrelevant variables out of the nonparametric regression model, but also avoid the problem of loss of efficiency related to the traditional nonparametric frequency-based method and the problem of misspecification based on parametric model.

Anticipating changes to public transportation ridership demand is important to planning for and meeting service goals and maintaining system viability. These changes may occur in the short- or long-term; extensive academic work has focused on bettering long-term forecasting procedures while improvements to short-term forecasting techniques have not received significant academic attention. This dissertation combines traditional forecasting approaches with multivariate regression to develop a transferable short-term public transportation ridership forecasting model that incorporates fuel price as a prediction parameter. The research herein addresses 254 US transit systems from bus, light rail, heavy rail, and commuter rail modes, and uses complementary methods to account for seasonal and non-seasonal ridership fluctuations. Models were built and calibrated using monthly data from 2002 to 2007 and validated using a six-month dataset from early 2008. Using variable transformations, classical data decomposition techniques, multivariate regression, and a variety of forecasting model validation measures, this work establishes a benchmark for future research into transferable transit ridership forecasting model improvements that may aid public transportation system planners in an era when, due to fuel price concerns, global warming and green initiatives, and other impetuses, transit use is seeing a resurgence in popularity. / text

This thesis explores the cross-price elasticity of rail and bus usage with gasoline prices. This is done to see how the short-run cross price elasticity has changed and to see if the same long-run relationship can be seen in the long run as previous pooled models. It is done in order to investigate whether policies such as higher gasoline taxes may make consumers move from car usage towards public transportation. Historically the cross-price elasticity has been around 0.2 with a higher elasticity for rail than for buses. The relationship also seemed to be greater in the long run than short run. Investigating this long run cross price elasticity for modes of public transportation separately would give greater insight into how consumers behave when gasoline prices shift. An ARDL model was therefore used to investigate the long run coefficients of gasoline prices with rail usage and bus usage separately as well as the short run coefficients. No cointegration could be found in this model for the two different modes. The results of the short-run cross-price elasticity seemed to be greater for buses as a direct effect, while it was greater at first lag for rail usage. The cross-price elasticity was lower for the period than previous studies, indicating that the cross-price elasticity may have decreased. The reasons for this cannot be concluded, but theory may explain these differences by the availability in substitutes for the periods, or lower levels of gasoline prices in recent years. This thesis therefore suggests further studies that investigate how usage of rail affects the usage of buses in metropolitan areas, and how the attributes of a modes of public transportation may change the usage of another form of public transportation.

The existing theoretical and empirical literature to investigate the existence of local market power is typically based on spatial competition models in the tradition of Hotelling's (1929) linear city and Salop's (1979) circular city. In models of this kind, strong assumptions are made that lead to a spatial homogeneity (symmetry) of firms in a highly stylized one-dimensional market space. However, some of these assumptions are hardly satisfied in many (retail) markets. The present thesis builds on a recent model by Chen and Riordan (2007), in which the market is characterized by a star-shaped graph with a central intersection. In an extension of Chen and Riordan, I distinguish between firms close to the center and firms in the periphery of a spatial market. This spatial heterogeneity leads to an asymmetric competition between firms. A central firm directly competes with a larger number of firms than remote firms do. The implications of the theoretical model are tested in two empirical applications to the retail gasoline market of Vienna and Austria. Using station level data on diesel prices, I estimate price reaction functions for gasoline stations in two different approaches. In the first approach the Austrian retail gasoline market is divided into numerous highly localized and delimited markets. The second approach analyzes the metropolitan area of Vienna and treats the whole market as one big network of gasoline stations, which are connected through the road network. In both approaches I apply econometric spatial autoregressive (SAR) models. The estimated parameters of the slopes of the reaction functions are used to evaluate the impact of individual gasoline stations on equilibrium market prices depending on their location within the market (network). All results obtained provide evidence for (more) central suppliers serving as a stronger reference in pricing than (rather) remote suppliers. Thus, the assumption of a symmetry in spatial competition which is usually implied by spatial competition models in theoretical and applied research, is rejected. (author's abstract)

We empirically investigate the importance of centrality (holding a central position in a spatial network) for strategic interaction in pricing for the Austrian retail gasoline market. Results from spatial autoregressive models suggest that the gasoline station located most closely to the market center - defined as the 1-median location - exerts the strongest effect on pricing decisions of other stations. We conclude that centrality influences firms' pricing behavior and further find that the importance of centrality increases with market size. (authors' abstract) / Series: Department of Economics Working Paper Series

We highlight the importance of "centrality" for pricing. Firms characterized by a more central position in a spatial network are more powerful in terms of having a stronger impact on their competitors' prices and on equilibrium prices. These propositions are derived from a simple theoretical model and investigated empirically for the retail gasoline market of Vienna, Austria. We compute a measure of network centrality based on the locations of gasoline stations in the road network. Results from a spatial autoregressive model show that prices of gasoline stations are more strongly correlated with prices of central competitors.