Knowledge that Transforms

We survey and apply several techniques from the statistical and computer science literature to the problem of demand estimation. To improve out-of-sample prediction accuracy, we propose a method of combining the underlying models via linear regression. Our method is robust to a large number of regressors; scales easily to very large data sets; combines model selection and estimation; and can flexibly approximate arbitrary non-linear functions. We illustrate our method using a standard scanner panel data set and find that our estimates are considerably more accurate in out-of-sample predictions of demand than some commonly used alternatives.

I study an economy where asymmetric information about the quality of capital endogenously determines liquidity. Liquid funds are key to relaxing financial constraints on investment and employment. These funds are obtained by selling capital or using it as collateral. Liquidity is determined by balancing the costs of obtaining liquidity under asymmetric information against the benefits of relaxing financial constraints. Aggregate fluctuations follow increases in the dispersion of capital quality, which raise the cost of obtaining liquidity. An estimated version of the model can generate patterns for quantities and credit conditions similar to the Great Recession.

In Capital in the Twenty-First Century, Thomas Piketty uses the market value of tradable assets to measure both productive capital and wealth. As a measure of wealth this is problematic because it ignores the value of human capital and transfer wealth, which have grown enormously over the last 300 years. Thus the constancy of the wealth/income ratio as portrayed in his data is an illusion. Further, the types of wealth that he does not measure are more equally distributed than tradable assets. The approach also incorrectly identifies capital gains due to reduced discount rates as increases in the capital stock.

The ability of web-based retailers to learn about and provide targeted consumer experiences is touted as an important distinction from traditional retailers. In principal, web-based insurance exchanges could benefit from these advantages. Using data from a large-scale experiment by a private sector health insurance exchange we estimate the returns to experimentation and targeted messaging. We find significant improvements in conversions in one treatment tested. Underlying the average impact were both intertemporal and demographic heterogeneity. We estimate that learning and targeted messaging could increase insurance applications by approximately 13 percent of the baseline conversion rate.

Gary Becker was one of the greatest thinkers of the 20th century. He advanced social science by introducing economic thinking into areas that were thought to be off limits. Because his theory was motivated by his desire to explain the world, his analyses were highly policy relevant. His work on discrimination, deterrence of crime, fertility, human capital, and the family all produced implications that were testable and verified by his and others' empirical research. Equally important, each research area provided policy guidance and many of his ideas have been implemented by government and non-government organizations.

Andreoni and Sprenger (2012a,b) observe that utility functions are distinct for risk and time preferences, and show that their findings are consistent with a preference for certainty. We revisit this question in an enriched experimental setting in which subjects make intertemporal decisions under different risk conditions. The observed choice behavior supports a separation between risk attitude and intertemporal substitution rather than a preference for certainty. We further show that several models, including Epstein and Zin (1989); Chew and Epstein (1990); and Halevy (2008) exhibit such a separation and can account for the overall experimental findings.

We estimate the sensitivity of scrap decisions to changes in used car values and show how this “scrap elasticity” produces emissions leakage under fuel efficiency standards, a process known as the Gruenspecht effect. We first estimate the effect of gasoline prices on used vehicle values and scrappage of vehicles with different fuel economies. We then estimate the scrap elasticity itself, which we find to be −0.7. When applied in a model of fuel economy standards, 13–16 percent of the expected fuel savings leak away through the used vehicle market. This effect rivals or exceeds the importance of the often-cited mileage rebound effect.

This paper exploits a data rich environment to provide direct econometric estimates of time-varying macroeconomic uncertainty. Our estimates display significant independent variations from popular uncertainty proxies, suggesting that much of the variation in the proxies is not driven by uncertainty. Quantitatively important uncertainty episodes appear far more infrequently than indicated by popular uncertainty proxies, but when they do occur, they are larger, more persistent, and are more correlated with real activity. Our estimates provide a benchmark to evaluate theories for which uncertainty shocks play a role in business cycles.

We propose new indices to measure macroeconomic uncertainty. The indices measure how unexpected a realization of a representative macroeconomic variable is relative to the unconditional forecast error distribution. We use forecast error distributions based on the nowcasts and forecasts of the Survey of Professional Forecasters. We further compare the new indices with those proposed in the literature and assess their macroeconomic impact.

This paper considers the measurement of consumer loss aversion in product markets. We introduce a test based on a “substitution effect,” focusing on how the end of a sale affects sales not of the good itself, but a substitute good. Such an effect cannot be easily confounded with consumer stockpiling. Using a unique dataset from an online hardware retailer, we find evidence consistent with consumer loss aversion. Moreover, we find that less experienced consumers suffer a more prominent loss aversion bias compared to more experienced consumers.