Decision Making & Negotiations

Not only are subjective feelings an integral part of many judgments and decisions, they can even lead to improved decisions and better predictions. Individuals who have learned to trust their feelings performed better in economic-negotiation games than their rational-thinking opponents. But emotions are not just relevant in negotiations and decisions. They also play a decisive role in forecasting future events. Candidates who trusted their feelings made better predictions than people with less emotional confidence.

We model the multifaceted impact of pricing decisions in B2B contexts and show how a seller can develop optimal inter-temporal targeted pricing strategies to maximize long-term customer value. We empirically model the B2B customer's purchase decisions in an integrated fashion. In order to facilitate targeting and to capture the short and long-term dynamics of B2B customer purchasing, our modeling framework weaves together in a hierarchical Bayesian manner, multivariate copulas, a non-homogeneous hidden Markov model, and control functions for price endogeneity.

Using panel data from a large hospital system, this paper presents estimates of the productivity effects of human capital in a team production environment. Proxying nurses' general human capital by education and their unit-specific human capital by experience on the nursing unit, we find that greater amounts of both types of human capital significantly improve patient outcomes.

This paper finds statistically and economically significant out-of-sample portfolio benefits for an investor who uses models of return predictability when forming optimal portfolios. The key is that investors must incorporate an ensemble of important features into their optimal portfolio problem, including time-varying volatility, and time-varying expected returns driven by improved predictors such as measures of yield that include share repurchase and issuance in addition to cash payouts. Moreover, investors need to account for estimation risk when forming optimal portfolios.

This paper focuses on add-on sales to determine both their value per se and their value as a reflexive measure of brand equity. Specifically, this paper examines the "accessory premium" for automobiles, i.e., accessories installed by dealers at the time of sale. Using J.D. Power Data, the authors find that higher add-on accessory sales accrue to higher equity brands which make accessory sales a potentially useful measure of brand equity (J. Marketing 67: 1?17, 2003). In addition, the authors examine how the revenue premium metric varies by age cohort.

This paper presents a comprehensive analysis of mortgage delinquency between 2004 and 2008 using a unique loan-level dataset from a major national mortgage bank. Our analysis highlights two major problems underlying the mortgage crisis: a heavy reliance on mortgage brokers who tend to originate lower quality loans, and a high prevalence of low-documentation loans—known in the industry as "liars' loans"—which results in information falsification by borrowers.

The regulation of bank capital as a means of smoothing the credit cycle is a central element of forthcoming macro-prudential regimes internationally. For such regulation to be effective in controlling the aggregate supply of credit it must be the case that: (i) changes in capital requirements affect loan supply by regulated banks, and (ii) unregulated substitute sources of credit are unable to offset changes in credit supply by affected banks. This paper examines micro evidence—lacking to date—on both questions, using a unique data set.

We use panel data to examine the prediction of Merton's intertemporal CAPM that time varying risk premiums arise from the conditional covariances of returns on assets with the return on the market. We find a positive and significant risk-return tradeoff that is driven by the time series variation in the conditional covariances, and the risk-premium on the market remains positive and significant after controlling for additional state-variables. Our estimation method allows us to estimate the risk-return tradeoff in the ICAPM using a large number of test assets.

We propose an approach for using individual-level data on social interactions (e.g., number of recommendations received by consumers, number of recommendations given by adopters, number of social ties) to improve the aggregate penetration forecasts made by extant diffusion models. We capture social interactions through an individual-level hazard rate in such a way that the resulting aggregate penetration process is available in closed form and nests extant diffusion models.