Organizations & Markets

This paper uses simulations to explore the properties of the HP filter of Hodrick and Prescott (1997), the BK filter of Baxter and King (1999), and the H filter of Hamilton (2018) that are designed to decompose a univariate time series into trend and cyclical components. Each simulated time series approximates the natural logarithms of U.S. Real GDP, and they are a random walk, an ARIMA model, two unobserved components models, and models with slowly changing nonstationary stochastic trends and definitive cyclical components.

Using a price-theoretic framework, we derive and empirically test a fundamental demand force shaping firms’ public disclosure decisions. Our framework suggests that the number of firms’ transacting stakeholders, not just their shareholders, is a major determinant of disclosure demand and, hence, firms’ decision to disclose publicly.

The dominant cultural valuation of stress is that it is “bad for me.” This valuation leads to regulatory goals of reducing or avoiding stress. In this article, we propose an alternative approach—stress optimization—which integrates theory and research on stress mindset (e.g., Crum, Salovey, & Achor, 2013) and stress reappraisal (e.g., Jamieson, Mendes, Blackstock, & Schmader, 2010) interventions. We further integrate these theories with the extended process model of emotion regulation (Gross, 2015).

Qualitative research is often used by marketers to develop new brand positionings. This case illustrates how two sequentially applied qualitative approaches were used to generate positionings for a pet food brand. The methods included psychologically oriented focus groups and anthropologically informed ethnographies. When implemented independently by a single market research company, the two approaches inspired highly distinctive brand positionings.

We examine the statistical term structure model of Cochrane and Piazzesi (2005) and its affine counterpart, developed in Cochrane and Piazzesi (2008), in several out-of-sample analyzes. The model's one-factor forecasting structure characterizes the term structures of additional currencies in samples ending in 2003. In post-2003 data one-factor structures again characterize each currency's term structure, but we reject equality of the coefficients across the two samples.

What does utility maximization subject to a budget constraint imply for intertemporal choice under uncertainty? Assuming consumers face a two period consumption-portfolio problem where asset markets are incomplete, we address this question following both the standard local infinitesimal and finite data approaches. To focus on the separate roles of time and risk preferences, individuals maximize KPS (Kreps-Porteus-Selden) preferences. The consumption-portfolio problem is decomposed into a one period portfolio problem and a two period certainty consumption-saving problem.

Our panel has been asked to consider the interaction of markets and policy. In my remarks today, I will specifically consider the interaction between the stock market and the monetary policy of the Federal Reserve, which is set to foster economic conditions that achieve the dual-mandate objectives of maximal employment and price stability (targeted as a 2 percent inflation rate). I want to reconcile seemingly conflicting evidence when we interpret the stock market in its aggregate and when we consider an individual firm that trades in the market.

We explored the balance between societal benefits that negatively affect firms' financial performance by eroding their competitive advantage and positive effects that enhanced their reputations as technological leaders to study the effects of forward citations upon firms' financial performance.

We examine the microeconomics of using algorithms to nudge decision-makers towards particular social outcomes. We refer to this as "algorithmic social engineering." In this article, we apply classic strategic communication models to this strategy. Manipulating predictions to express policy preferences strips the predictions of informational content and can lead decision-makers to ignore them. When social problems stem from decision-makers’ objectives (rather than their information sets), algorithmic social engineering exhibits clear limitations.