A fad or the future? Examining the effectiveness of virtual reality advertising in the hotel industry

New virtual reality technology presents hotels with wide-ranging marketing, training, and customer service opportunities. The purpose of this study was to explore both the immediate and delayed effects of hotel VR commercials as compared to traditional video commercials. Based on the perceptual load theory and elaboration likelihood model, this study designed and conducted a 2 × 2 laboratory experiment with two stages of data collection. The study demonstrated that VR commercials produced better immediate effects than the traditional commercials, especially for participants with high elaboration likelihood levels. However, the findings also revealed significant decreases in purchase intention among participants watching VR commercials and a significant improvement in attitudes among participants watching traditional commercials. Both theoretical and practical implications were discussed to leverage the power of VR in marketing.     

Antecedents and outcomes of restaurant employees’ food safety intervention behaviors

Restaurants are likely culprits of having foodborne illness outbreaks, causes of which are often linked to employees failing to control food threats during the flow of food. Research indicates that many food risks posed by restaurants could be thwarted if employees would intervene as threats are noticed. For unknown reasons, employees are not always motivated to reduced or eliminate threats when observing poor food safety behaviors, regardless of position, education, or experience in the industry.This study applies the elaboration likelihood model of persuasion (ELM) and protection motivation (PMT) theories to investigate employee reactions to food safety threats. Of keen interest was to identify employees of differing positions in the restaurant and to study how they react to food safety threats to either intervene, passively dismiss, or simply leave the location of the incident.     

Distribution of residual autocorrelations for multiplicative seasonal ARMA models with uncorrelated but nonindependent error terms

In this paper, we consider portmanteau tests for testing the adequacy of multiplicative seasonal autoregressive moving‐average models under the assumption that the errors are uncorrelated but not necessarily independent. We relax the standard independence assumption on the error terms in order to extend the range of applications of the seasonal autoregressive moving‐average models. We study the asymptotic distributions of residual and normalized residual empirical autocovariances and autocorrelations under weak assumptions on noise. We establish the asymptotic behavior of the proposed statistics. A set of Monte Carlo experiments and an application to monthly mean total sunspot number are presented.     

Euro area real-time density forecasting with financial or labor market frictions

We compare real-time density forecasts for the euro area using three DSGE models. The benchmark is the Smets and Wouters model, and its forecasts of real GDP growth and inflation are compared with those from two extensions. The first adds financial frictions and expands the observables to include a measure of the external finance premium. The second allows for the extensive labor-market margin and adds the unemployment rate to the observables. The main question that we address is whether these extensions improve the density forecasts of real GDP and inflation and their joint forecasts up to an eight-quarter horizon. We find that adding financial frictions leads to a deterioration in the forecasts, with the exception of longer-term inflation forecasts and the period around the Great Recession. The labor market extension improves the medium- to longer-term real GDP growth and shorter- to medium-term inflation forecasts weakly compared with the benchmark model.     

Incentives and teacher effort

Few would contest that teachers are a very important determinant of how much students learn in school, and how to improve teacher performance has been the focus of lively policy debate in both rich and poor countries. This paper examines how teacher incentives, both pecuniary and non‐pecuniary, correlate with teacher effort. Using school survey data from Lao PDR, we estimate measures of teacher effort, including the number of hours that teachers spend preparing for classes and teacher provision of private tutoring outside of class hours, which are not the typical measures used in previous research. Estimation results fit well under the standard labour supply framework and indicate that greater teacher effort is associated with non‐pecuniary incentives such as more teacher autonomy over teaching materials and monitoring as measured by the existence of an active parent – teacher association and the ability of school principals to dismiss teachers. Methodologically, this paper provides a detailed derivation of a simultaneous OLS‐probit model with school random effects that can jointly estimate teacher work hours and tutoring provision.     

Conditional empirical likelihood estimation and inference for quantile regression models

This paper considers two empirical likelihood-based estimation, inference, and specification testing methods for quantile regression models. First, we apply the method of conditional empirical likelihood (CEL) by Kitamura et al. [2004. Empirical likelihood-based inference in conditional moment restriction models. Econometrica 72, 1667–1714] and Zhang and Gijbels [2003. Sieve empirical likelihood and extensions of the generalized least squares. Scandinavian Journal of Statistics 30, 1–24] to quantile regression models. Second, to avoid practical problems of the CEL method induced by the discontinuity in parameters of CEL, we propose a smoothed counterpart of CEL, called smoothed conditional empirical likelihood (SCEL). We derive asymptotic properties of the CEL and SCEL estimators, parameter hypothesis tests, and model specification tests. Important features are (i) the CEL and SCEL estimators are asymptotically efficient and do not require preliminary weight estimation; (ii) by inverting the CEL and SCEL ratio parameter hypothesis tests, asymptotically valid confidence intervals can be obtained without estimating the asymptotic variances of the estimators; and (iii) in contrast to CEL, the SCEL method can be implemented by some standard Newton-type optimization. Simulation results demonstrate that the SCEL method in particular compares favorably with existing alternatives.     

Can Warren Buffett forecast equity market corrections?

Warren Buffett suggested that the ratio of the market value of all publicly traded stocks to the Gross National Product could identify potential overvaluations and undervaluations in the US equity market when this ratio deviates above 120% or below 80%. We investigate whether this ratio is a statistically significant predictor of equity market corrections and rallies. We find that Buffett's decision rule does not deliver satisfactory forecasts. However, when we adopt a time-varying decision rule, the ratio becomes a statistically significant predictor of equity market corrections. The two time-varying decision rules are: (i) predict an equity market correction when the ratio exceeds a 95% one-tail confidence interval based on a normal distribution, and (ii) predict an equity market correction when the ratio exceeds a threshold computed using Cantelli's inequality. These new decision rules are robust to changes in the two key parameters: the confidence level and the forecasting horizon. This paper also shows that the MV/GNP ratio performs relatively well against the four most popular equity market correction models, but the ratio is not a particularly useful predictor of equity market rallies.     

Recovering the real-world density and liquidity premia from option data

In this paper, we develop a methodology for simultaneous recovery of the real-world probability density and liquidity premia from observed S&P 500 index option prices. Assuming the existence of a numéraire portfolio for the US equity market, fair prices of derivatives under the benchmark approach can be obtained directly under the real-world measure. Under this modelling framework, there exists a direct link between observed call option prices on the index and the real-world density for the underlying index. We use a novel method for the estimation of option-implied volatility surfaces of high quality, which enables the subsequent analysis. We show that the real-world density that we recover is consistent with the observed realized dynamics of the underlying index. This admits the identification of liquidity premia embedded in option price data. We identify and estimate two separate liquidity premia embedded in S&P 500 index options that are consistent with previous findings in the literature.     

American-style options in jump-diffusion models: estimation and evaluation

We propose dynamic programming coupled with finite elements for valuing American-style options under Gaussian and double exponential jumps à la Merton [J. Financ. Econ., 1976, 3, 125–144] and Kou [Manage. Sci., 2002, 48, 1086–1101], and we provide a proof of uniform convergence. Our numerical experiments confirm this convergence result and show the efficiency of the proposed methodology. We also address the estimation problem and report an empirical investigation based on Home Depot. Jump-diffusion models outperform their pure-diffusion counterparts.     

Stable Asymptotics for M‐estimators

We review some first‐order and higher‐order asymptotic techniques for M‐estimators, and we study their stability in the presence of data contaminations. We show that the estimating function (ψ) and its derivative with respect to the parameter play a central role. We discuss in detail the first‐order Gaussian density approximation, saddlepoint density approximation, saddlepoint test, tail area approximation via the Lugannani–Rice formula and empirical saddlepoint density approximation (a technique related to the empirical likelihood method). For all these asymptotics, we show that a bounded ψ (in the Euclidean norm) and a bounded (e.g. in the Frobenius norm) yield stable inference in the presence of data contamination. We motivate and illustrate our findings by theoretical and numerical examples about the benchmark case of one‐dimensional location model.