Self-exciting threshold models for time series of counts with a finite range

In this article, an integer-valued self-exciting threshold model with a finite range based on the binomial INARCH(1) model is proposed. Important stochastic properties are derived, and approaches for parameter estimation are discussed. A real-data example about the regional spread of public drunkenness in Pittsburgh demonstrates the applicability of the new model in comparison to existing models. Feasible modifications of the model are presented, which are designed to handle special features such as zero-inflation.     

Goal attainability and performance: Evidence from Boston marathon qualifying standards

In this paper we test if performance improves once goals become more attainable. Goal-setting literature suggests that workers respond to challenging but achievable goals with increased performance. Empirical evidence supports the notion of goals increasing performance; however the evidence on how attainability of goals affects performance is mixed. This paper tests whether efforts increase, improving performance as the goals become more attainable. We are employing a unique set of publicly available marathon data from 1970 to 2015 to directly analyze the effect of goal attainability on performance. With the probable goal of qualifying for the Boston Marathon, we test if runners increase their effort, and consequently improve their performance if they enter a new age group and as a result have a more attainable goal. We find that runners who enter a new age group perform better than the runners whose qualifying time did not change. This effect is seen with runners in younger age groups, but not found in the results of runners in more advanced years.     

Deriving Behavior Model Parameters from Survey Data: Self‐Protective Behavior Adoption During the 2009–2010 Influenza A(H1N1) Pandemic

In this paper, we demonstrate how public opinion surveys can be designed to collect information pertinent to computational behavior modeling, and we present the results of a public opinion and behavior survey conducted during the 2009–2010 H1N1 influenza pandemic. The results are used to parameterize the Health Belief Model of individual health‐protective decision making. Survey subjects were asked questions about their perceptions of the then‐circulating influenza and attitudes towards two personal protective behaviors: vaccination and avoidance of crowds. We empirically address two important issues in applying the Health Belief Model of behavior to computational infectious disease simulation: (1) the factors dynamically influencing the states of the Health Belief Model variables and (2) the appropriateness of the Health Belief Model in describing self‐protective behavior in the context of pandemic influenza.     

Comparison of computer programs for simple linear L 1 regression

A number of efficient computer codes are available for the simple linear L ₁ regression problem. However, a number of these codes can be made more efficient by utilizing the least squares solution. In fact, a couple of available computer programs already do so.

We report the results of a computational study comparing several openly available computer programs for solving the simple linear L ₁ regression problem with and without computing and utilizing a least squares solution.     

Adolescents,Online Marketing and Privacy: Predicting Adolescents’ Willingness to Disclose Personal Information for Marketing Purposes

The present study aims at exploring which factors predict the willingness of adolescents to disclose personal information in response to online marketing requests. Our analyses show that especially privacy concerns and perceived benefits explain a considerable portion of variance in both willingness to disclose profile data (e.g. age, hobbies, favourite products) and contact data (e.g. phone number, e‐mail address, home address). An interesting interrelationship was found between profile data and contact data disclosure. Contrary to our expectations, parental mediation strategies were only a weak predictor of teenagers’ willingness to disclose contact information. Policy implications that emerge from the central findings of this study are discussed.     

Second-Order Approximations to Two Classes of Sequential Estimation Procedures

For the model considered by Chaturvedi, Pandey and Gupta (1991), two classes of sequential procedures are developed to construct confidence regions (which may be interval, ellipsoidal or spherical) of ‘pre-assigned width and coverage probability’ for the parameters of interest and for the minimum risk point estimation (taking loss to be quadratic plus linear cost of sampling) of the nuisance parameter. Second-Order approximations are derived for the expected sample size, coverage probability and ‘regret’ associated with the two classes of sequential procedures. A simple and direct method of obtaining the asymptotic distribution of the stopping time is provided. By means of examples, it is illustrated that several estimation problems can be tackled with the help of proposed classes of sequential procedures.     

New Firms as Employers: The Wage Penalty for Voluntary and Involuntary Job Switchers

According to previous research, new firms pay lower wages. However, previous studies have been unable to control for the possibility that the opportunity costs of accepting employment at new firms may differ across individuals. In this paper, we investigate whether a wage penalty for being employed at a new firm exists if we take the individual employee's experience and status in the labour market into consideration. We focus on individuals who decide to switch jobs and use matched employee–employer data about all firms and employees in Sweden for the period 1998–2010. Our results show that the share of job transitions into lower wages are higher for those who switch to new firms compared with incumbent firms (40 per cent and 31 per cent, respectively). Our endogenous wage equation estimates indicate that being an involuntary job switcher has an equally negative effect on wages at both new and incumbent firms. However, the positive effect of education on wages is more pronounced for job switchers selecting into incumbent firms.     

THE BALANCED US PRESS

We measure the relative ideological positions of newspapers, voters, interest groups, and political parties, using data on ballot propositions. We exploit the fact that newspapers, parties, and interest groups take positions on these propositions, and the fact that citizens ultimately vote on them. We find that, on average, newspapers in the United States are located almost exactly at the median voter in their states—that is, they are balanced around the median voter. Still, there is a significant amount of ideological heterogeneity across newspapers, which is smaller than the one found for interest groups. However, when we group propositions by issue area, we find a sizable amount of ideological imbalance: broadly speaking, newspapers are to the left of the state‐level median voter on many social issues, and to the right on many economic issues. To complete the picture, we use two existing methods of measuring bias and show that the news and editorial sections of newspapers have almost identical partisan positions.     

MANAGED COMPETITION IN HEALTH INSURANCE

Rising healthcare costs have sparked debate about the best way to provide high‐quality affordable health insurance. We discuss the potential for regulated insurance markets to outperform single‐payer public insurance. We use as an example the private Medicare plans that now provide insurance to almost a third of seniors in the United States. The evidence suggests that private plans can limit costs and potentially appeal to enrollees, and that well‐designed risk adjustment can mitigate market failures due to adverse selection. However, fostering competition between insurers, especially in smaller markets, is difficult. We discuss how future research might illuminate the relative advantages of public and private health insurance.     

Risk aggregation with dependence and overdispersion based on the compound Poisson INAR(1) process

Abstract

This paper considers an extension of the classical discrete time risk model for which the claim numbers are assumed to be temporal dependence and overdispersion. The risk model proposed is based on the first-order integer-valued autoregressive (INAR(1)) process with discrete compound Poisson distributed innovations. The explicit expression for the moment generating function of the discounted aggregate claim amount is derived. Some numerical examples are provided to illustrate the impacts of dependence and overdispersion on related quantities such as the stop-loss premium, the value at risk and the tail value at risk.