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I survey applications of Markov switching models to the asset pricing and portfolio choice literatures. In particular, I discuss the potential that Markov switching models have to fit financial time series and at the same time provide powerful tools to test hypotheses formulated in the light of financial theories, and to generate positive economic value, as measured by risk-adjusted performances, in dynamic asset allocation applications. The chapter also reviews the role of Markov switching dynamics in modern asset pricing models in which the no-arbitrage principle is used to characterize the properties of the fundamental pricing measure in the presence of regimes.

We study a microfounded search model of exchange in the laboratory. Using a within-subjects design, we consider exchange behavior with and without an intrinsically worthless token object. While these tokens have no redemption value, like fiat money they may foster greater exchange and welfare via the coordinating role of having prices of goods in terms of tokens. We find that welfare is indeed improved by the presence of tokens provided that the economy starts out with a supply of such tokens. In economies that operate for some time without tokens, the later surprise introduction of tokens does not serve to improve welfare. We also explore the impact of announced changes in the economy-wide stock of tokens (fiat money) on prices. Consistent with the quantity theory of money, we find that increases in the stock of money (tokens) have no real effects and mainly result in proportionate changes to prices. However, the same finding does not hold for decreases in the stock of money.

This study aims to explore the dynamics between the brand manufacturer’s revenue model choice and the platform’s private label product entry strategy, specifically, (1) Under the wholesale and agency models, when should the platform introduce its private label product to compete with the manufacturer? (2) Facing the potential threat of the platform’s entry, how should the manufacturer choose between the wholesale and agency models? (3) How does the platform’s entry strategy affect the manufacturer’s price decisions and demand? (4) What are the implications of the strategic interaction between the manufacturer’s model selection and the platform’s encroachment on social welfare and consumer surplus?

This study develops a multistage game model consisting of a manufacturer, a platform and consumers. The model describes the strategic interaction between the manufacturer and the platform, where the manufacturer first chooses one of the revenue models from the agency model and the wholesale model, followed by the platform’s decision whether to offer its own products to enter the competition, after which the two parties set the price of their products to compete on price according to their strategies, then the consumers make purchase decisions based on the principle of utility maximization.

For the platform, to not hurt its profitability in the manufacturer channel with too much competition, lower social utility sensitivity and lower similarity of product imitation rather favor platform entry. Platform entry affects manufacturers’ retail prices and demand differently across different revenue models. Interestingly, if the social utility sensitivity and imitation similarity are moderate, when the commissions extracted by the platform are low, manufacturers still have the incentive to adopt the wholesale model rather than the agency model. In addition, platform entry into competition increases consumer surplus and social welfare only when consumer sensitivity to social utility is low.

The research model innovatively describes the strategic interaction between the manufacturer and the platform; to more accurately portray the consumer demand model, this model also introduces the parameters of the similarity of the platform’s products to the products of the manufacturer, as well as the social utility sensitivity of consumers. Conclusions are drawn on the choice of platform entry strategy versus the choice of revenue model for manufacturers. Relevant managerial insights are provided for both platforms and manufacturers, which partially explains the existing market situation.

The notes reproduced here were taken by Glenn Johnson in Lloyd Mints’ course on Money and Banking at the University of Chicago in the fall of 1946. Several additional sets of course notes taken by Glenn Johnson have been published in the archival volumes of Research in the History of Economic Thought and Methodology. These included notes from Frank Knight's course on economic theory (Volume 24C) and Albert L. Meyer's course entitled elements of modern economics (appearing in this volume). A brief biography of Glenn Johnson is provided in Volume 24C, along with notes from his course on Agricultural Economics Methodology taught at Michigan State University.

This study aims to investigate the moderating role of hedonic and utilitarian purchase motives for the presentation order effect. Although past research finds that presenting item first and price later (e.g. 70 items for $29) increases consumers’ purchase intention more than presenting the information in the opposite order (e.g. $29 for 70 items), the effect was mostly examined in a hedonic consumption context. This study examines whether the effect is applicable for hedonic purchases but is less applicable for utilitarian purchases, and why.

Seven experiments tested the moderating effect of purchase motives for the presentation order effect. Two serial mediation analyses were conducted to examine the underlying mechanism.

The “item-price” (vs “price-item”) order increases hedonic purchases, but not utilitarian purchases. Because consumers feel guilty about hedonic purchases, they engage in motivated information processing to perceive greater value from their hedonic purchase when item (benefit) information is presented first and price (cost) information is presented later. Perceiving greater value reduces guilt, which consequently increases hedonic purchases. In contrast, the order effect is not observed for utilitarian purchases that do not elicit guilt. When a price discount is offered, the order effect is reversed because actual savings justify hedonic purchases better than perceived savings resulting from motivated information processing.

When promoting hedonic products, marketers are recommended to present item information before price information, unless a price discount is offered, in which case the price should be presented first.

This research introduces a novel moderator for the presentation order effect and a novel underlying mechanism, driven by the motivation to alleviate guilt associated with hedonic purchases.

This study analyses the effect of fuel efficiency increase on travel demand in the city of Berlin. Vehicle technologies such as advanced driver assistance systems can help drivers to save fuel and thus lower exhaust emissions on a network level. In order to obtain high political endorsement among different stakeholders, the analysis of such effects which have an impact on overall fuel and emission savings are highly relevant. Recent testing of so called advanced driver assistance systems showed their ability to reduce fuel consumption and lower traffic emissions by giving driving recommendations to drivers.

Two effects on driving were simulated using a travel demand model: the increase in fuel prices which will take place in the coming years and a possible increase in vehicle fuel efficiency. Comparing these scenarios allowed us to calculate the effect of price change and the rebound effect of fuel efficiency gains using standard methods for transport elasticities. The simulation was run with the travel demand model TAPAS and the city of Berlin was the network used as a case study.

As fuel prices increase over time, driving tends to decrease. Driving increases, however, if vehicles become more fuel efficient and the result is the observed rebound effect. On a city network level, this also translates to lower emission savings than expected from the vehicle fuel efficiency gains. The rebound effect which we estimated matches similar findings in the literature, specifically in terms of their magnitude.

We used a simulation to compare scenarios of city travel demand. The result allowed us to estimate changes to the desired variables of fuel efficiency and fuel prices. For those interested in the effects of vehicle efficiency gains on city level these results are highly recommended for consideration.

The proposed framework for analysing rebound effects helped to assess the impacts of energy efficiency technologies on a city level.

Many jurisdictions fine illegal cartels using penalty guidelines that presume an arbitrary 10% overcharge. This article surveys more than 700 published economic studies and judicial decisions that contain 2,041 quantitative estimates of overcharges of hard-core cartels. The primary findings are: (1) the median average long-run overcharge for all types of cartels over all time periods is 23.0%; (2) the mean average is at least 49%; (3) overcharges reached their zenith in 1891–1945 and have trended downward ever since; (4) 6% of the cartel episodes are zero; (5) median overcharges of international-membership cartels are 38% higher than those of domestic cartels; (6) convicted cartels are on average 19% more effective at raising prices as unpunished cartels; (7) bid-rigging conduct displays 25% lower markups than price-fixing cartels; (8) contemporary cartels targeted by class actions have higher overcharges; and (9) when cartels operate at peak effectiveness, price changes are 60–80% higher than the whole episode. Historical penalty guidelines aimed at optimally deterring cartels are likely to be too low.

The study aims to address a critical gap in existing healthcare payment schemes and care service pricing by recognizing the influential role of patients' decisions on self-management efforts. These decisions not only impact health outcomes but also shape the demand for care, subsequently influencing care costs. Despite the significance of this interplay, current payment schemes often overlook these dynamics. The research focuses on investigating the implications of a novel behavior-based payment scheme, designed to align incentives and establish a direct connection between patients' decisions and care costs. The primary objective is to comprehensively understand whether and how this innovative payment scheme structure influences key stakeholders, including patients, care providers, insurers and overall social welfare.

In this paper, we propose a game-theoretical model to incorporate the performance of self-management with the demand for healthcare service, compare the patient's effort decision for self-management and provider's price decision for healthcare service under a behavior-based scheme with that under two implemented widely payment schemes, that is, co-payment scheme and co-insurance scheme.

Our findings confirm that the behavior-based scheme incentives patient self-management more than current schemes while reducing their possibility of seeking healthcare service, which indirectly induces the provider to lower the price of the service. The stakeholders' utility under various payment schemes is sensitive to the cost of treatment and the perceived health utility of patients. Especially, patient health awareness is not always benefited provider profit, as it motivates patient self-management while diminishing the demand for care.

We provide a novel framework for characterizing behavior-based payment schemes. Our results confirm the need for modification of the current payment scheme to incentivize patient self-management.

The purpose of this paper is to investigate whether stock price predictions and investment decisions improve by exposure to increasing price series.

The authors conducted three laboratory experiments in which undergraduates were asked to role‐play being investors buying and selling stock shares. Their task was to predict an unknown closing price from an opening price and to choose the number of stocks to purchase to the opening price (risk aversion) or the closing price (risk taking). In Experiment 1 stock prices differed in volatility for increasing, decreasing or no price trend. Prices were in different conditions provided numerically for 15 trading days, for the last 10 trading days, or for the last five trading days. In Experiment 2 the price series were also visually displayed as scatter plots. In Experiment 3 the stock prices were presented for the preceding 15 days, only for each third day (five days) of the preceding 15 days, or as five prices, each aggregated for three consecutive days of the preceding 15 days. Only numerical price information was provided.

The results of Experiments 1 and 2 showed that predictions were not markedly worse for shorter than longer price series. Possibly because longer price series increase information processing load, visual information had some influence to reduce prediction errors for the longer price series. The results of Experiment 3 showed that accuracy of predictions increased for less price volatility due to aggregation, whereas again there was no difference between five and 15 trading days. Purchase decisions resulted in better outcomes for the aggregated prices.

Investorś performance in stock markets may not improve by increasing the length of evaluation intervals unless the quality of the information is also increased. The results need to be verified in actual stock markets.

The results have bearings on the design of bonus systems.

The paper shows how stock price predictions and buying and selling decisions depend on amount and quality of information about historical prices.

The purpose of this paper is to design a parking space management platform to alleviate the parking problem and a two-stage solution for sharing and allocating parking spaces.

The market design mechanism and auction mechanism are integrated to solve the problem of parking space sharing and allocation. In the first stage, the market design mechanism with two rules is applied for making the good use of idle parking spaces. In the second stage, two sequential auction mechanisms are designed by extending first/second-price sealed bid auction mechanism to allocate both private and public parking spaces, which are received in previous stage and owned by the platform. Two stages are connected through a forecasted price which is calculated through the exponential smoothing method.

First, we prove three important properties of the proposed sequential auction mechanisms, namely, incentive compatibility, revenue equivalence and individual rationality. Second, a simulation study is used to verify the effectiveness of the mechanisms through numerical analysis. The impact of the system on three parts, namely, agents (private parking space suppliers), bidders (parking space customers) and the platform, is examined. Third, the results show that the sharing mechanism with monetrary incentive will attract a number of agents to join in the platform. The bidders are also able to obtain considerable utility, as compared with the (average) market parking fees. The platform can thus effectively allocate parking spaces with reasonable prices.

This paper combines the classical sequential auction mechanisms with the market design mechanism for the parking space sharing and allocation problem. The modeling and analysis method can also be used to address the similar allocation and pricing problems of other resources like bicycle sharing.