A note on government spending on infrastructure in an endogenous growth model with finite horizon

By extending the work of Mourmouras and Lee (1999), we show that the growth-maximizing income tax rate maximizes also the utilities of not only all current generations but also all future generations. This result means that the statement that the growth-maximizing income tax rate is equivalent to the welfare maximizing one holds true in a stronger sense than Barro (1990) and Mourmouras and Lee (1999) have pointed out.     

基于离散傅里叶变换的高动态突发信号检测及频率估计

针对高动态突发通信应用环境,提出了一种新的基于频率域的突发信号检测及载波频偏估计算法,通过一次离散傅里叶变换（DFT）实现突发信号存在性检测及频率估计,并与经典Power-Law算法进行了比较。仿真结果表明：在低信噪比条件下,新算法检测信噪比门限改善超过1 dB,频率估计均方根误差小于符号率的1‰,并且对载波频偏及信号电平动态不敏感,实现结构简单,适合实时处理及工程应用。     

Smartphone preferences and brand loyalty: A discrete choice model reflecting the reference point and peer effect

Brand loyalty and interest have significant impacts on consumers' smartphone choices. What about brand loyalty and interest of smartphone in South Korea where Samsung originates from? This study investigates brand loyalty and interest and how they are affected by the satisfaction of innovative peers in South Korea. An asymmetric discrete choice model with reference-dependent preferences is applied for the analysis. The estimation results show that in South Korea the brand is the most important attribute of smartphone and Apple is the strongest in brand loyalty. Whether consumers who are currently owners of Apple smartphones continue to maintain the same brand in their next purchasing depends not only on their brand loyalty but also on the satisfaction of their highly innovative peers who currently own the Apple. On the other hand, Samsung's brand loyalty is lower than that of Apple, but the brand interest is the highest. Additionally, in all smartphone brands, satisfaction with smartphone brands owned by innovative peers has a significant impact on consumers' interest.     

Towards a brain-to-society systems model of individual choice

Canonical models of rational choice fail to account for many forms of motivated adaptive behaviors, specifically in domains such as food selections. To describe behavior in such emotion- and reward-laden scenarios, researchers have proposed dual-process models that posit competition between a slower, analytic faculty and a fast, impulsive, emotional faculty. In this paper, we examine the assumptions and limitations of these approaches to modeling motivated choice. We argue that models of this form, though intuitively attractive, are biologically implausible. We describe an approach to motivated choice based on sequential sampling process models that can form a solid theoretical bridge between what is known about brain function and environmental influences upon choice. We further suggest that the complex and dynamic relationships between biology, behavior, and environment affecting choice at the individual level must inform aggregate models of consumer choice. Models using agent-based complex systems may further provide a principled way to relate individual and aggregate consumer choices to the aggregate choices made by businesses and social institutions. We coin the term “brain-to-society systems” choice model for this broad integrative approach.     

Advances in computation,statistical methods,and testing of discrete choice models

This paper gives a brief overview of recent developments in computation, estimation, and statistical testing of choice models, with marketing applications. Topics include statistical models for discrete panel data with heterogeneous decision-makers, simulation methods for estimation of high-dimension multinomial probit models, specification tests for model structure and for brand and purchase clustering, and innovations in numerical analysis for estimation and forecasting. In collaboration with Denis Bolduc, David Bunch, Michael Keane, Don Kridel, and Steve Stern.     

Information processing analysis of a stochastic model of brand choice

A formal and detailed comparison between Morrison's brand loyal model and its information processing counterpart reveals serious internal inconsistencies and other shortcomings of this stochastic model.     

The choice of appropriate technology in a general equilibrium model

This paper analyzes the general equilibrium aspects related to the choice of technology. The paper studies the problem from the perspective of a small, labor abundant open economy that does not generate its own technology. The economic planners must choose between two technologies, one better geared toward the production of labor intensive goods, the other to capital intensive goods. It is shown that the choice of technology not only will affect the welfare level, but it can also reverse the pattern of international trade. It is also shown that the imposition of a tariff can lead the planner to choose a different technology from the one that is optimal under free trade.     

Exploring the connection between frustration and consumer choice behavior in a dynamic decision environment

This research proposes that frustration during the purchase process for high‐technology durable goods has a significant effect on the probability that consumers will commit to a technology and make a purchase. In order to explore the effects of consumer frustration on the purchase process, a scale is developed that reveals that frustration in high‐technology decision environments is composed of two dimensions, processing frustration and frustration with the pace of technological change. These dimensions of frustration have a significant effect on consumer choice behavior. While processing frustration significantly reduces the probability of commitment to a technology, the probability of making a decision is significantly lower when consumers are frustrated with the pace of technological change. © 2004 Wiley Periodicals, Inc.     

Issues in the estimation and application of latent structure models of choice

Our paper provides a brief review and summary of issues and advances in the use of latent structure and other finite mixture models in the analysis of choice data. Focus is directed to three primary areas: (1) estimation and computational issues, (2) specification and interpretation issues, and (3) future research issues. We comment on what latent structure models have promised, what has been, to date, delivered, and what we should look forward to in the future.     

A structural equation model of latent segmentation and product choice for cross-sectional revealed preference choice data

We consider the problem of simultaneously performing market segmentation and predicting product choice for a given population of consumers. This behaviour-based segmentation model uses several types of information characterizing individual consumers and their market context: sociodemographic, psychographic and product choice data. These data are combined in a normative conceptual framework that posits the formation of latent segments, each of which displays differing degrees of sensitivity to relevant product attributes. This permits results obtained by the proposed model structure to differ significantly from those of traditional segmentation techniques, which usually produce a set of groups characterized by smaller within-group than between-group variation in sociodemographic and/or psychographic variables. By making the segments behaviour-based, it is argued that the segments produced by the model will be more actionable and more directly relevant to management decision-making. The proposed model is applied to a data set involving choice of a beauty product offered by five national brands. The results show that the explanatory power of the latent segmentation and choice model is far greater than that of the MNL model, which is nested within our more general specification. The types of results available from the model are illustrated through the application.     

Bayesian estimation of an open economy DSGE model with incomplete pass-through

In this paper, we develop a dynamic stochastic general equilibrium (DSGE) model for an open economy, and estimate it on Euro area data using Bayesian estimation techniques. The model incorporates several open economy features, as well as a number of nominal and real frictions that have proven to be important for the empirical fit of closed economy models. The paper offers: i) a theoretical development of the standard DSGE model into an open economy setting, ii) Bayesian estimation of the model, including assessments of the relative importance of various shocks and frictions for explaining the dynamic development of an open economy, and iii) an evaluation of the model's empirical properties using standard validation methods.     

Identification and counterfactuals in dynamic models of market entry and exit

This paper addresses a fundamental identification problem in the structural estimation of dynamic oligopoly models of market entry and exit. Using the standard datasets in existing empirical applications, three components of a firm’s profit function are not separately identified: the fixed cost of an incumbent firm, the entry cost of a new entrant, and the scrap value of an exiting firm. We study the implications of this result on the power of this class of models to identify the effects of different comparative static exercises and counterfactual public policies. First, we derive a closed-form relationship between the three unknown structural functions and the two functions that are identified from the data. We use this relationship to provide the correct interpretation of the estimated objects that are obtained under the ‘normalization assumptions’ considered in most applications. Second, we characterize a class of counterfactual experiments that are identified using the estimated model, despite the non-separate identification of the three primitives. Third, we show that there is a general class of counterfactual experiments of economic relevance that are not identified. We present a numerical example that illustrates how ignoring the non-identification of these counterfactuals (i.e., making a ‘normalization assumption’ on some of the three primitives) generates sizable biases that can modify even the sign of the estimated effects. Finally, we discuss possible solutions to address these identification problems.     

A nested logit model of brand choice incorporating variety-seeking and marketing-mix variables

We model the effects of variety-seeking and marketing-mix variables on consumers' purchases of coffee using a nested logit model. We premise that on any given purchase occasion, the utilities of brands other than the one purchased on the previous occasion may be correlated due to the consumer's tendency to seek variety or to avoid variety. This results in a two-level hierarchical model where choice on any purchase occasion is conditioned on the brand purchased on the immediately preceding occasion. Such a structure accounts for variety seeking and inertia tendencies of consumers and is consistent with a hierarchical decision process, where consumers first decide whether or not to make a repeat purchase and then decide which brand size to purchase. The assumed hierarchical structure is shown to be consistent with observed coffee purchase behavior, and the model is shown to outperform a nonhierarchical logit model in predicting consumers' brand choices.     

Random disturbances and the choice of exchange regimes in an intergenerational model

An overlapping generational model is used to derive the consumption demand for individuals of each generation under expected utility-maximizing behavior: these demand functions are used to derive macroeconomic equilibrium under fixed and flexible exchange rates. The standard aggregate results concerning the insulating role of flexible rates for real external disturbances and fixed rates for internal disturbances are shown. However, the variability of an individual's utility need not be the same as the variability of aggregate variables. It is shown that the form of the utility function and the sources of disturbances are crucial in determining which exchange regime is preferable.     

A dynamic model of reference price and expected quality

A number of recent papers have developed normative implications of the concept of reference price. In this paper, we extend that literature to incorporate the relationship between expected quality and reference price. We consider the case of a monopolist who makes time-varying decisions regarding price and product quality. Our results suggest that when the effect of a loss (price greater than reference price and product quality less than expected quality) on demand is greater than or equal to that of a corresponding gain, it is optimal for a monopolist to have constant price and product quality levels. When the effect of a gain on demand is greater than that of a corresponding loss, however, we find that it is optimal to maintain cyclical pricing and product quality policies.     

Simulating evolution of interindustry linkages in endogenous dynamic IO model with layers of techniques

In this article, we propose a new version of dynamic input–output model in which both technological progress and deployment are endogenous, and where sector‐specific outlays on R&D speed up the development of new technologies and the installation of capital stock. In this two‐technology model, the new and old technical processes within a sector exchange their relative weights in production. We use the model to obtain projections of the interindustry linkages of sectors in the Polish economy over the next 50 years. The results of this simulation suggest an ongoing change of the composition of the set of key sectors of the Polish economy. In general, one may expect to see an ongoing drop in the importance of agriculture‐ and heavy‐industry‐related sectors on the one hand, and a rise in the importance of services‐related ones on the other.     

Illustrating a dynamic model of the mood‐updating process in consumer behavior

Previous research has examined mood as an antecedent of various consumption‐related outcomes and has explored how consumers react to their moods via attempts at self‐regulated consumption‐based mood management. However, little attention has addressed mood as an affective outcome that reflects the dynamic unfolding of consumer experiences over time. The present study explores the latter issue and proposes a model of the mood‐updating process wherein future mood depends on past mood as altered by evaluative judgments that are, in turn, influenced by consumption experiences, by predispositions, and by the previous mood itself. An empirical example illustrates this mood‐updating model in the case of listening to music. © 2000 John Wiley & Sons, Inc.     

Risk and the technology content of FDI: A dynamic model

This paper incorporates risk into the FDI decisions of firms. The risk of FDI failure increases with the gap between the South's technology frontier and the technology complexity of a firm's product. This leads to a double-crossing sorting pattern of FDI—firms of intermediate technology levels are more likely than others to undertake FDI. It is with the attempt to relax the upper bound of the technology content of FDI, we argue, that many FDI policies are created. The theory's predictions are consistent with the empirical patterns of FDI in China by US and Taiwanese manufacturing firms.     

Bayesian estimation of a simultaneous probit model using error augmentation: An application to multi-buying and churning behavior

Researchers in marketing are often interested in analyzing how an agent’s discrete choice decision affects a subsequent or concurrent discrete choice decision by the same or different agent. This analysis may necessitate the use of a simultaneous equations model with discrete and continuous endogenous variables as explanatory variables. In this paper, we offer an error augmentation approach to Hierarchical Bayesian estimation of a simultaneous bivariate probit model containing both discrete and continuous endogenous variables. We accomplish the error augmentation in our MCMC algorithm using a Metropolis-Hastings step that generates the error components of the latent variables in our model. Using simulated data, we demonstrate that our error augmentation algorithm recovers closely the true parameters of the simultaneous bivariate probit model. We then apply our algorithm to customer churn data from a wireless service provider. We formulate a simultaneous bivariate probit model to study the impact of a customer’s multiple product relationships with a firm (multi-buying) on the likelihood of churn by that customer. The empirical results show that the act of multi-buying significantly reduces churn even though the customers who are more predisposed to multi-buy have an inherently higher predisposition to churn.