Knowledge that Transforms

We derive a closed-form expression for the posterior probability of Granger noncausality in a gaussian vector autoregression with a conjugate prior. We also express in closed form the posterior probability of Granger causal priority, a more general relation that accounts for indirect effects between variables and therefore is suitable in a multivariate context. One can use these results to answer the classic question, “Is variable z relevant for variable x?” and to choose variables for a vector autoregression.

Empirical models for dyadic interactions between n agents often feature agent-specific parameters. Fixed-effect estimators of such models generally have bias of order n−1, which is nonnegligible relative to their standard error. Therefore, confidence sets based on the asymptotic distribution have incorrect coverage. This paper looks at models with multiplicative unobservables and fixed effects. We derive moment conditions that are free of fixed effects and use them to set up estimators that are n-consistent, asymptotically normally distributed, and asymptotically unbiased. We provide Monte Carlo evidence for a range of models. We estimate a gravity equation as an empirical illustration.

We develop an almost affine term-structure model with a closed-form solution for factor loadings in which the spot rate and the risk price are fractionally integrated processes with different integration orders. This model is used to explain two stylized facts. First, predictability of longterm excess bond returns requires sufficient volatility and persistence in the risk price. Second, the large volatility of long-term bond returns requires persistence in the spot rate. Decomposing long-term bond returns, we find that the expectations component from the level factor is more volatile than returns themselves and that the risk premium correlates negatively with level-factor innovations.

We use a tailor-made survey on a Swedish sample to investigate how individuals' relative income affects their demand for redistribution. We first document that a majority misperceive their position in the income distribution and believe that they are poorer, relative to others, than they actually are. We then inform a subsample about their true relative income and find that individuals who are richer than they initially thought demand less redistribution. This result is driven by individuals with prior right-of-center political preferences who view taxes as distortive and believe that effort, rather than luck, drives individual economic success.

Many markets exhibit price dispersion across suppliers of observationally identical goods. Statistical agencies typically assume this dispersion reflects unobserved quality, so standard price indexes do not incorporate price declines when buyers substitute toward lower-price suppliers. We show that long-run price differences across suppliers can be used to infer unobserved quality differences and propose an index that accommodates quality-adjusted price dispersion. Using transaction-level data on contract semiconductor manufacturing, we document substantial quality-adjusted price dispersion and confirm that a standard index is biased above our proposed index.

The English rule prescribes that the loser of a lawsuit pays the winner's litigation costs. Previous research on the English rule finds that plaintiffs win more often at trial, receive higher awards, and receive larger settlements. Theory predicts that the English rule discourages settlement by raising the threshold payment necessary for settlement. In this paper, we reexamine the Florida experiment with the English rule by placing bounds on the selection effects. We find that the mean and median settlement amount increases. Collectively these findings are consistent with the predictions of the simplest models of the English rule's impact.

We consider the dynamic factor model where the loading matrix, the dynamic factors, and the disturbances are treated as latent stochastic processes. We present empirical Bayes methods that enable the shrinkagebased estimation of the loadings and factors. We investigate the methods in a large Monte Carlo study where we evaluate the finite sample properties of the empirical Bayes methods for quadratic loss functions. Finally, we present and discuss the results of an empirical study concerning the forecasting of U.S. macroeconomic time series using our empirical Bayes methods.

This paper suggests an identification and estimation approach based on quantile regression to recover the underlying distribution of bidders’ private values in ascending auctions under the IPV paradigm. The quantile regression approach provides a flexible and convenient parameterization of the private values distribution, with an estimation methodology easy to implement and with several specification tests. The quantile framework provides a new focus on the quantile level of the private values distribution—in particular, the seller’s optimal screening level, which can be very useful for bidders and seller. An empirical application using data from the USFS timber auctions illustrates the methodology.

In this paper, we discuss the key conditions for the identification and estimation of the local average treatment effect (LATE, Imbens and Angrist, 1994): the valid instrument assumption (LI) and the monotonicity assumption (LM). We show that the joint assumptions of LI and LM have a testable implication that can be summarized by a sign restriction defined by a set of intersection bounds. We propose an easy-to-implement testing procedure that can be analyzed in the framework of Chernozhukov, Lee, and Rosen (2013) and implemented using the Stata package of Chernozhukov, Kim, Lee, and Rosen (2013). We apply the proposed tests to the "draft eligibility" instrument in Angrist (1991), the "college proximity" instrument in Card (1993) and the "same sex" instrument in Angrist and Evans (1998).

Numerous studies find that criminal court judges issue racially disparate sentences, but whether these patterns reflect tastes for discrimination remains unclear. An alternative explanation is statistical discrimination, which implies that judges rely on race to predict a felon’s latent criminality in the absence of perfect information. This paper uses an empirical approach that distinguishes between taste-based and statistical discrimination. The intuition is that if the rank order of judicial incarceration rates depends on race, then this is symptomatic of taste-based discrimination. The rank-order test results imply that we cannot reject the null hypothesis of no taste-based discrimination.