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Praise for the First Edition of Quantitative Business Valuation. A Mathematical Approach for Today's Professionals. "Jay Abrams' book is close to the equivalent of several graduate dissertations rolled into one book. For each topic (covered), he presents a scholarly summary of past research, new empirical research of his own, and his conclusions. It is a well-documented contribution to in-depth understanding of important business valuation issues, and should not be overlooked by the serious practitioner."--Shannon Pratt, DBAManaging Director, Willamette Management AssociatesCoauthor

In the empirical literature, monetary policy shocks are commonly measured as an innovation to a short-term nominal interest rate. In contrast, the majority of monetary business cycle models treats a broad monetary aggregate as the central bank's policy measure. We try overcome this disparity and present a business cycle model which allows to examine the effects of innovations to a non-contingent nominal interest rate rule. To obtain unique rational expectations equilibria we assume that changes in money supply are brought about open market operations. In addition to working capital, we consider staggered prices which enables real marginal costs to vary. Consistent with the empirical findings of Barth and Ramey (2000), the model predicts that real marginal cost and inflation rise in response to positive interest rate innovations. The mechanism corresponds to their 'Cost Channel of Monetary Transmission' and replicates typical monetary VAR results, including the puzzling behavior of prices.

AbstractPopular game shows offer educators the opportunity to develop active‐learning exercises that provide students with a real‐world connection to analytical reasoning and methods. We describe a classroom assignment developed for quantitative business courses based on the Monty Hall Problem (MHP), a probability puzzle with ties to the long‐running television game showLet's Make a Deal. Through a holistic view of the MHP, we provide instructors with various avenues to use the MHP as a comprehensive experiential learning exercise with multiple opportunities for individual and class discussions and exercises. Instructors adopting our multifaceted approach to the MHP will expose students to myriad analytical tools including probability, decision trees, and Monte Carlo simulation as well as to cognitive biases that can affect the decision‐making process. Among the unique contributions of our work is its focus on the commonly held MHP assumptions. Specifically, we challenge students to articulate conditions inherent to the MHP solution and assess their appropriateness in context. By relaxing one or more of those assumptions, and observing differences in solutions, students become attuned to the importance of knowing and assessing conditions that underlie all quantitative decision‐making tools.