Repeated Dichotomous Choice Formats for Elicitation of Willingness to Pay: Simultaneous Estimation and Anchoring Effect

Repeated dichotomous choice contingent valuation data are generated from responses to a succession of binary questions regarding alternative prices for an environmental good. In this paper we propose a simultaneous equation model that allows for endogeneity and error correlation across the responses at each stage of the bidding process. The model allows us to study the evolution of anchoring effects after the second dichotomous choice question. Estimation involves the Bayesian techniques of Gibbs sampling and data augmentation, and the application focuses on the preservation value of a natural area. The results for a data set involving up to four successive dichotomous choice questions show that restricted multiple-bounded models are rejected by the data with the general model. In addition, willingness to pay tends to stabilize after the second stage in the elicitation process for the general unrestricted model. When taking anchoring effects into consideration, it is revealed that individuals’ responses in the latter stages are influenced by the sequence of bid prices offered in earlier questions. Nevertheless, they do not have a significant effect on welfare estimates.      

Models for count data with endogenous participation

We present several extensions of the Poisson and negative binomial models for count data based on the lognormal model for latent heterogeneity in the conditional means. The lognormal model provides a versatile specification that is more flexible than the familiar log gamma form, and provides a platform for several “two part” extensions that have appeared in the literature, including zero inflation, hurdle and sample selection models. We then extend these received two- part models by allowing for endogeneity of the participation equation. We conclude with a detailed application using the data employed in a recent study of the German health care system.      

Forecasting exchange rates using panel model and model averaging

We propose to produce accurate point and interval forecasts of exchange rates by combining a number of well known fundamental based panel models. Combination of each model utilizes a set of weights computed using a linear mixture of experts's framework, where weights are determined by log scores assigned to each model's predictive performance. As well as model uncertainty, we take potential structural break in the parameters of the models into consideration. In our application, to quarterly data for ten currencies (including the Euro) for the period 1990q1–2008q4, we show that the forecasts from ensemble models produce mean and interval forecasts that outperform equal weight, and to a lesser extent random walk benchmark models. The gain from combining forecasts is particularly pronounced for longer-horizon forecasts for central forecasts, but much less so for interval forecasts. Calculations of the probability of the exchange rate rising or falling using the combined or ensemble model show a good correspondence with known events and potentially provide a useful measure for uncertainty of whether the exchange rate is likely to rise or fall.     

A Behavioural Approach Towards Futures Contract Usage

We propose a behavioural decision-making model to investigate what factors, observable as well as unobservable, owner-managers consider regarding futures contract usage. The conceptual model consists of two phases, reflecting the two-stage decision structure of manager's use of futures. In the first phase owner-managers consider whether futures are within the market choice set for the enterprise. In the second phase the owner-manager decides whether or not to initiate a futures position when confronted with a concrete choice situation. In both phases owner-manager's beliefs and perceptions play an important role. The proposed model is tested on a data set of Dutch farmers, based on computer-assisted personal interviews. Because we incorporate latent variables (e.g., perceptions and beliefs) in both phases, we propose an estimation procedure that takes the measurement error of these latent variables explicitly into account. The implications of the behavioural decision-making model for futures contract design are derived.     

Improving forecast accuracy of financial vulnerability: PLS factor model approach

We present a factor augmented forecasting model for assessing the financial vulnerability in Korea. Dynamic factor models often extract latent common factors from a large panel of time series data via the method of the principal components (PC). Instead, we employ the partial least squares (PLS) method that estimates target specific common factors, utilizing covariances between predictors and the target variable. Applying PLS to 198 monthly frequency macroeconomic time series variables and the Bank of Korea's Financial Stress Index (KFSTI), our PLS factor augmented forecasting models consistently outperformed the random walk benchmark model in out-of-sample prediction exercises in all forecast horizons we considered. Our models also outperformed the autoregressive benchmark model in short-term forecast horizons. We expect our models would provide useful early warning signs of the emergence of systemic risks in Korea's financial markets.     

Modelling preference heterogeneity for theatre tickets: a discrete choice modelling approach on Royal Danish Theatre booking data

This article analyses the behavioural choice for theatre tickets using a rich data set for 2010–2013 from the sale system of the Royal Danish National Theatre. A consumer who decides to attend a theatre production faces multiple sources of price variation that involves a choice by the consumer among different ticket alternatives. Three modelling approaches are proposed in order to model ticket purchases: conditional logit with socio-demographic characteristics, nested logit and latent class. These models allow us explicitly to take into account consumers’ preference heterogeneity with respect to the attributes associated with each ticket alternative (quality of the seat and day of the performance). In addition, the willingness to pay of choice attributes is estimated. Final results suggest that customers’ characteristics in terms of age and frequency of theatre attendance characterize different patterns of behaviour in the choice of theatre ticket.     

Is cannabis a gateway to hard drugs?

The gateway hypothesis proposes that use of cannabis directly increases the risk of consuming hard drugs. We test this controversial, but influential, hypothesis on a sample of cannabis users, exploiting a unique set of drug price data. A flexible approach is developed to identify the causal gateway effect using a bivariate survival model with shared frailty estimated using a latent class approach. The model suggests two distinct groups; a smaller group of “troubled youths” for whom there is a statistically significant gateway effect that more than doubles the hazard of starting to use hard drugs and a larger fraction of youths for whom previous cannabis use has less impact.     

Forecast accuracy of small and large scale dynamic factor models in developing economies

Developing economies usually present limitations in the availability of economic data. This constraint may affect the capacity of dynamic factor models to summarize large amounts of information into latent factors that reflect macroeconomic performance. This paper addresses this issue by comparing the accuracy of two kinds of dynamic factor models at GDP forecasting for six Latin American countries. Each model is based on a dataset of different dimensions: a large dataset composed of series belonging to several macroeconomic categories (large scale dynamic factor model) and a small dataset with a few prescreened variables considered as the most representative ones (small scale dynamic factor model). Short‐term pseudo real time out‐of‐sample forecast of GDP growth is carried out with both models reproducing the real time situation of data accessibility derived from the publication lags of the series in each country. Results (i) confirm the important role of the inclusion of latest released data in the forecast accuracy of both models, (ii) show better precision of predictions based on factors with respect to autoregressive models and (iii) identify the most adequate model for each country according to availability of the observed data.     

Hurdle and Latent Class Approaches to Serial Non-Participation in Choice Models

In repeated choice modelling studies, it is often the case that individuals always select the status quo option. Although this pattern may reflect considered choices, they may also be the result of alternative decisions about whether to participate in the choice process at all. Alternative methods of dealing with this behaviour, each with associated implications for estimates of economic values, are presented. In particular we consider the alternative strategies of excluding such individuals from the data, using hurdle models to explicitly model this group, and propose the use of latent class models to endogenously allow for different preference structures. An advantage of the latent class approach is that the form of the non-participation need not be defined in advance. These approaches are considered using UK choice experiment data on food choices where the attributes include genetic modification of food. The latent class approach reveals the presence of two forms of non-participation in the data. This paper draws work commissioned by UK Department of Environment Food and Rural Affairs (DEFRA). The views presented in this paper are those of the authors alone and should not be regarded as those of DEFRA or of individuals within DEFRA.     

The existence of optimal consumption policies in optimal economic growth models with nonconvex technologies

An existence theorem for a class of continuous time infinite horizon optimal growth models is developed. The underlying technology set is not assumed to be convex, instead the “slices” of the technology set corresponding to a fixed capital stock vector are assumed convex and compact in the consumption and net investment variables. This allows consideration of the case of increasing returns to scale. Existence of an optimal capital stock and consumption policy is proved directly without consideration of the underlying Hamiltonian dynamical system that arises from applying Pontryagin's maximum principle.     

Spatial regulations and endogenous consideration sets in fisheries

The implementation of spatial regulations has become a mainstay in fisheries management. These regulations have generated a sizable economics literature focused on the spatial behavior of fishermen. Fundamental to these studies is the consideration set (spatial alternatives) assumed by the researcher to be possessed by the decision agent. Often times this consideration set is assumed to be the entire spatial extent of the fishery. This research proposes the use of finite mixture modeling to endogenously estimate the formation of consideration sets and the method is applied to a unique spatial decision environment, the Atka mackerel fishery in the Aleutian Islands. Consideration sets are modeled using different macro-definitions of spatial regions to focus the micro-level spatial decision making within the fishery and to investigate the sensitivity of the results to alternative macro-level spatial definitions. Results illustrate the biases associated with traditional consideration set assumptions by estimating fishermen's valuations for different high-value sites within the Aleutian Islands under alternative consideration set assumptions. Furthermore, our results demonstrate how a model that assumes some structure on potential consideration sets reduces the dimensionality problems associated with other endogenous approaches to choice set definition.     

违约风险与贷款定价:一个基于期权方法和软预算约束的新模型

由于忽视了软预算约束导致的"优先原则"不成立以及由此产生的还贷道德风险等现实问题,贷款定价传统期权方法在中国的适用性受到了影响。通过引入信贷合同效率,本文构造了二维违约风险,并据此建立了贷款定价的新模型。新模型解决了上述问题,得到以下结论:第一类与第二类违约风险的联动对贷款定价的影响是不确定的,第一类与第二类违约风险相关度越高则贷款定价越低,贷款期限与贷款定价之间的关系受违约风险构成的影响等。     

Efficiency measurement using a latent class stochastic frontier model

Efficiency estimation in stochastic frontier models typically assumes that the underlying production technology is the same for all firms. There might, however, be unobserved differences in technologies that might be inappropriately labeled as inefficiency if such variations in technology are not taken into account. We address this issue by estimating a latent class stochastic frontier model in a panel data framework. An application of the model is presented using Spanish banking data. Our results show that bank-heterogeneity can be fully controlled when a model with four classes is estimated. This paper was written during Luis Oreas visit to Binghamton University in the summer of 2002. We would like to thank an associate editor of the journal and two anonymous referees for their detailed comments. However, we alone are responsible for any remaining errors.     

ECONOMIES OF SCALE AND SCOPE IN POSTAL OUTLETS UNDER CONSIDERATION OF UNOBSERVED HETEROGENEITY

ABSTRACT: We analyze economies of scale and scope in Swiss Post's post offices and franchised postal agencies under consideration of different underlying production technologies and unobserved factors using a latent class model. The results confirm the existence of unobserved heterogeneity and of increasing economies of scale and scope in the Swiss Post's outlet network.     

Small-Sample Forecasting Regression or Arima Models?

Univariate models offer the most convenient options for forecasting and ARIMA models are still the most popular among them. The ARIMA modelling, however, requires long data series. This paper shows that a regression model may be estimated with a far greater efficiency in very small samples compared to the corresponding ARIMA model. As a result the larger information set used in a regression model may compensate for the small sample size and improve the forecast efficiency substantially. Three applications which utilize autoregressive forecasts on the exogenous variables highlight the gains in forecast efficiency in small samples from regressions over the ARIMA models.

     

品牌进入消费者考虑集的决策规则——基于牙膏产品品牌的实证研究

消费者考虑集是消费者决策过程中的重要阶段,品牌只有进入考虑集才有可能被最终购买。本研究在上海以牙膏作为测试对象检验了牙膏品牌进入考虑集时消费者所依据的决策规则。实证结果显示,消费者主要使用排除式决策规则、非权重的补偿式决策规则和权重的补偿式决策规则。     

Using Angler Characteristics and Attitudinal Data to Identify Environmental Preference Classes: A Latent-Class Model

A latent-class model of environmental preference groups is developed and estimated with only the answers to a set of attitudinal questions. Economists do not typically use this type of data in estimation. Group membership is latent/unobserved. The intent is to identify and characterize heterogeneity in the preferences for environmental amenities in terms of a small number of preference groups. The application is to preferences over the fishing characteristics of Green Bay. Anglers answered a number of attitudinal questions, including the importance of boat fees, species catch rates, and fish consumption advisories on site choice. The results suggest that Green Bay anglers separate into a small number of distinct classes with varying preferences and willingness to pay for a PCB-free Green Bay. The probability that an angler belongs to each class is estimated as function of observable characteristics of the individual. Estimation is with the expectation–maximization (E–M) algorithm, a technique new to environmental economics that can be used to do maximum-likelihood estimation with incomplete information. As explained, a latent-class model estimated with attitudinal data can be melded with a latent-class choice model. Edward Morey and Jennifer Thacher are equal authors and rotate authorship across articles.     

Vertical Relationships between Manufacturers and Retailers: Inference with Limited Data

In this paper, different models of vertical relationships between manufacturers and retailers in the supermarket industry are compared. Demand estimates are used to compute price-cost margins for retailers and manufacturers under different supply models when wholesale prices are not observed. The purpose is to identify the set of margins compatible with the margins obtained from estimates of cost and to select the model most consistent with the data among non-nested competing models. The models considered are (1) a simple linear pricing model; (2) a vertically integrated model; and (3) a variety of alternative (strategic) supply scenarios that allow for collusion, non-linear pricing, and strategic behaviour with respect to private label products. Using data on yogurt sold in several stores in a large urban area of the U.S. the results imply that wholesale prices are close to marginal cost and that retailers have pricing power in the vertical chain. This is consistent with non-linear pricing by the manufacturers or high bargaining power of the retailers.     

A model selection approach for multiple indicators multiple causes model

This study proposes a model selection approach for determining the inclusion or exclusion of a latent variable when two exogenous and two endogenous variables are provided. The models compared are the multivariate regression model without latent variables (MR model) and the multiple indicators multiple causes model (MIMIC model). The inclusion of a latent variable in the MR model yields the MIMIC model. In the proposed approach, an information criterion is used to select the best model of the two. The efficacy of the proposed approach is examined through two types of simulation studies and empirical analyses of the shadow economy and the fiscal illusion.