Will people use self-driving cars, virtual doctors, and other algorithmic decision-makers if they outperform humans? The answer depends on the uncertainty inherent in the decision domain. We propose that people have diminishing sensitivity to forecasting error, and that this preference results...

This paper assesses the relationship between the length of recall and nonrandom error in agricultural survey data. Using data from the World Bank's Living Standards Measurement Study–Integrated Surveys on Agriculture in Malawi and Tanzania, the paper shows that key input and output variables...

The tracking error is a ubiquitous tool among active and passive portfolio managers, used widely for fund selection, risk management, and manager compensation. In this paper we show that traditional measures of tracking error are incapable of detecting variations in higher order moments (e.g....

Kritzman (2006) asserts that the widely cited Michaud (1989) study characterizing the impact of estimation error on Markowitz (1952) mean-variance (MV) optimization as “error maximization” is hype. His paper consists of two examples of MV portfolio optimizations: an eight-country index asset...

The solution to dynamic portfolio choice models can be formulated in terms of a value function by the Bellman principle of optimality, which reduces the multi-period optimal policy choice problem to a sequence of one-period maximization problems. For two adjacent periods, economists compute the...

There is a divergence in the returns of top-performing (star) firms and the rest of the economy, especially in industries that rely on a skilled labor force, raising concerns about their market power. We show that the divergence is explained by the mis-measurement of intangible capital. While...

We study implications of unpriced "granular measurement errors" -- idiosyncratic shocks to large firms that aren't well-diversified in market indices -- for asset pricing tests and propose alternative tests insensitive to them. We find stronger evidence of an intertemporal relation between the...

Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to "mine" variables of interest from available data, followed by the inclusion of those...

Data snooping is a major concern in empirical asset pricing. We develop a new framework to rigorously perform multiple hypothesis testing in linear asset pricing models, while limiting the occurrence of false positive results typically associated with data snooping. By exploiting a variety of...

Although all types of accounting numbers are in theory prone to noise, the concept receives verylittle attention in cost accounting. Motivated by its potentially detrimental consequences fordecision making, we develop a numerical experiment that investigates how random measurementerrors...