A bivariate Poisson count data model using conditional probabilities

The applied econometrics of bivariate count data predominantly focus on a bivariate Poisson density with a correlation structure that is very restrictive. The main limitation is that this bivariate distribution excludes zero and negative correlation. This paper introduces a new model which allows for a more flexible correlation structure. To this end the joint density is decomposed by means of the multiplication rule in marginal and conditional densities. Simulation experiments and an application of the model to recreational data are presented.     

Incentive effects in the demand for health care: a bivariate panel count data estimation

This paper contributes in three dimensions to the literature on health care demand. First, it features the first application of a bivariate random effects estimator in a count data setting, to permit the efficient estimation of this type of model with panel data. Second, it provides an innovative test of adverse selection and confirms that high‐risk individuals are more likely to acquire supplemental add‐on insurance. Third, the estimations yield that in accordance with the theory of moral hazard, we observe a much lower frequency of doctor visits among the self‐employed, and among mothers of small children. Copyright © 2002 John Wiley & Sons, Ltd.     

A latent class poisson regression model for heterogeneous count data

In this paper an approach is developed that accommodates heterogeneity in Poisson regression models for count data. The model developed assumes that heterogeneity arises from a distribution of both the intercept and the coefficients of the explanatory variables. We assume that the mixing distribution is discrete, resulting in a finite mixture model formulation. An EM algorithm for estimation is described, and the algorithm is applied to data on customer purchases of books offered through direct mail. Our model is compared empirically to a number of other approaches that deal with heterogeneity in Poisson regression models.     

Big data from dynamic pricing: A smart approach to tourism demand forecasting

Suppliers of tourist services continuously generate big data on ask prices. We suggest using this information, in the form of a price index, to forecast the occupation rates for virtually any time-space frame, provided that there are a sufficient number of decision makers “sharing” their pricing strategies on the web. Our approach guarantees great transparency and replicability, as big data from OTAs do not depend on search interfaces and can facilitate intelligent interactions between the territory and its inhabitants, thus providing a starting point for a smart decision-making process. We show that it is possible to obtain a noticeable increase in the forecasting performance by including the proposed leading indicator (price index) into the set of explanatory variables, even with very simple model specifications. Our findings offer a new research direction in the field of tourism demand forecasting leveraging on big data from the supply side.     

Repeat tourism in Uruguay: modelling truncated distributions of count data

This paper studies the determinants of repeat visiting in Uruguay, where loyal visitors are a relevant part of the total. From a statistical point of view, the number of times a visitor has been to a place constitutes count data. In this regard available information on Uruguay presents relevant limitations. Count data is in fact reported only for those who visited the country up to five times, whereas records about the most frequent visitors are collapsed into one residual category. This implies that the classic models for count data such as Poisson or negative binomial cannot be put into consideration. The paper suggests instead modelling the available part of the empirical distribution through quantile count data regression. It is a model based on measures of location rather than mean values, which allows estimating tourists’ behaviour as the number of visits increases. A set of explanatory variables related to budgetary constraints, socioeconomic, trip-related and psychographic characteristics are taken as regressors to the considered count data.     

Inferring trader behavior from transaction data: A trade count model

We present a model that uses trade counts to infer the arrival of private news and the probability of informed trading (PIN). Although similar in approach, our model avoids problems with factor-driven biases and standard errors associated with estimating the buy-sell model of Easley et al. (1996). In particular, tests using the probability of informed trading may suffer from spurious correlations between the Easley et al. (1996) PIN and firm or market characteristics. Results for our model suggest that trade counts, independent of trade direction, are able to capture important features of a firm’s information environment. (JEL C51, D82)     

Bequest motives and household money demand

The bequest motive is an important motive determining intergenerational transfers of income, saving, and money. However, it has received little or no attention from past studies on money demand. This study utilizes panel data to show that the bequest motive is positively related to money demand and interacts with the life-cycle motive during various stages of an individual’s life. Householders with bequest motives are more likely to transfer a greater proportion of their permanent incomes to monetary assets than those without bequest motives.     

Estimating household demand for housing attributes in rent-controlled markets

We estimate the household’s marginal willingness to pay for housing attributes in the rent-controlled sector, so where rents are not freely market determined. The application of hedonic price approaches to obtain estimates of the household’s value of housing characteristics is then invalid. We apply an alternative estimation approach based on residential mobility. In our application, we focus on the households’ willingness to pay for number of rooms as well as the willingness to pay to avoid a long commuting distance. Our estimates appear plausible. For example, for households in the rent-controlled sector are willing to pay about 7% of their household income for an additional room. The implied marginal costs of commuting are about €0.17–€0.23 per (one-way) kilometre.     

A note on forecasting international tourism demand in Spain

This paper considers the extent to which price and income proxy variables help in forecasting tourist demand in Spain. Contrary to some recent studies, we found that the inputs' contribution in terms of fitting and forecasting is nil when compared with alternative univariate models. Whether these findings are the results of the restrictions embedded in building the proxy inputs or in a poor specification of the dynamics of these models remains to be seen. We also contend that when dealing with medium, long-term forecasting comparisons, the use of the traditional aggregate accuracy measures like RMSE and MAPE help very little in discriminating among competing models. In these situations, predicted annual growth rates may be a better alternative.     

A model of local demand for education

A careful modeling of the spending behavior of local school districts in New Jersey is presented. The theoretical model relies on the common hypothesis of political competition leading to a median outcome as well as the assumption that school superintendents act as budget maximizers. The model is estimated for a sample of 177 school districts under three different aid formulas. Pooling of the cross sections suggests that the response of school expenditures to changes in variables other than those associated with school aid parameters has remained constant over time. The results are discussed in the context of recent theories regarding the effect of intergovernmental aid on the level of public expenditure.     

中国城乡居民国内旅游需求预测

依据1989-2006年统计数据,采用Cobb-Douglas生产函数和对数直线模型,分析了人均收入驱动下的我国城市和农村居民国内旅游客流量及出游率的变化,为定量预测城市和农村居民国内旅游提供了科学依据.     

Intervention analysis of SARS on Japanese tourism demand for Taiwan

Japan was Asia’s leading generator of international tourism in the 1980s and 1990s. Japanese tourists make up over 30% of all international tourists to Taiwan and they have been the highest ranking tourist source market since the early stages of the island’s tourism development in the 1970s. However, the Severe Acute Respiratory Syndrome (SARS) outbreak in 2003, the most catastrophic disaster in the past 100 years in Taiwan, had a huge impact on Japanese inbound tourism to the island. The purpose of this study is to evaluate how Japanese inbound arrivals have been affected by the SARS outbreak. A SARIMA with intervention model is used to assess the impact of the epidemic on inbound tourism from Japan to Taiwan in the aftermath of the SARS outbreak. The empirical results indicated that inbound tourism from Japan was devastated by the crisis, particularly during the first 5 months after the SARS outbreak. This study provides some helpful insight for the tourism industry to respond to the impact of exogenous shock.     

A simple,flexible estimator for count and other ordered discrete data

This paper examines a flexible way to model empirically discrete data outcomes using ‘hazard rate’ decompositions. It presents a general data‐generating mechanism based on potential outcomes to describe why the approach should work for almost any discrete distribution. Monte Carlo evidence indicates that these models estimate well the impacts of covariates on expected counts when the data follow a Poisson distribution. With data from more complex processes, these estimators continue to perform well. Since most economic count outcomes arise from occurrence‐dependent behavioral processes, using flexibly estimated distributions should reduce the dependence of results on convenient but invalid assumptions. Copyright © 2010 John Wiley & Sons, Ltd.     

The demand for computer services: A disaggregate decision model

Pricing schedules for computer resources have traditionally been based on ‘cost-recovery’ principles. While economists have begun to address pricing based on marginal congestion costs, most models take demand to be exogenous and given. Discrete alternatives are inadequately treated, and aggregation of data precludes any assessment of the impact of transaction size on consumers' decisions. Using disaggregated data, this paper derives empirical results confirming that consumers are strongly influenced by transaction sizes. Simulation experiments demonstrate that price incentives designed to modify the use of computer resources are considerably more effective if the distribution of demand is weighted towards large transactions.     

A dynamic model for disaster response considering prioritized demand points

This paper addresses the problem of distributing relief supplies after the occurrence of a disaster. We develop a dynamic model to serve demand, while prioritizing the response, according to the level of urgency of demand points. Our model is thought to be applied during a planning horizon and it considers dynamic demand, capacity constraints and priorities. To evaluate the applicability of our model, we use a real case study of a flood occurred in Colombia. We also test the computational solvability of our model and we propose and test different solution methodologies for solving larger instances of our problem.     

A gravity model analysis of the demand for mass communications

The gravity model specification is shown to be applicable to a study of the demand for a mass communication medium. The data are taken from newspaper circulations in the San Francisco metropolitan area. The demand equations have properties different from those for either private communication or travel demands.     

Robust likelihood inferences about regression parameters for general bivariate continuous data

This paper introduces a way of modifying the bivariate normal likelihood function. One can use the adjusted likelihood to generate valid likelihood inferences for the regression parameter of interest, even if the bivariate normal assumption is fallacious. The retained asymptotic legitimacy requires no knowledge of the true underlying joint distributions so long as their second moments exist. The extension to the multivariate situations is straightforward in theory and yet appears to be arduous computationally. Nevertheless, it is illustrated that the implementation of this seemingly sophisticated procedure is almost effortless needing only outputs from existing statistical software. The efficacy of the proposed parametric approach is demonstrated via simulations.     

Optimal automobile emission control: A household utility model

Traditionally, benefit-cost studies value mortality changes using equivalent capital approaches. That is, the social benefit of decreased mortality is related to an increased income stream. A utility model which accounts for mortality changes as changes in individual or household risks is proposed, it is then employed to analyze automotive emission control. The utility model selects optimal national emission control levels for individual households by maximizing a combination of risk and consumption. The social optimum is taken as the median levels of all households. The control levels for three pollutants are more stringent than those calculated by a more typical benefit-cost method but less strict than the present statutory standards.

The data has been taken for several sources. The risk data is from an epidemiology study that associates age specific mortality rates with various factors including pollution levels. Economic benefits are derived from several estimates of the association of pollution with vegetation and material damages and soiling. Automobile control costs are from EPA and auto company estimates.     

Mining big data in tourism

Quality & Quantity - Knowledge discovery from various sources of information based on different data types for decision and accurate prediction can be rather complex and costly without a...     

A spatial model for multivariate lattice data

In this article, we develop Markov random field models for multivariate lattice data. Specific attention is given to building models that incorporate general forms of the spatial correlations and cross-correlations between variables at different sites. The methodology is applied to a problem in environmental equity. Using a Bayesian hierarchical model that is multivariate in form, we examine the racial distribution of residents of southern Louisiana in relation to the location of sites listed with the U.S. Environmental Protection Agency's Toxic Release Inventory.