Knowledge that Transforms

An approximate factor model of high dimension has two key features. First, the idiosyncratic errors are correlated and heteroskedastic over both the cross-section and time dimensions; the correlations and heteroskedasticities are of unknown forms. Second, the number of variables is comparable or even greater than the sample size. Thus, a large number of parameters exist under a high-dimensional approximate factor model. Most widely used approaches to estimation are principal component based. This paper considers the maximum likelihood–based estimation of the model. Consistency, rate of convergence, and limiting distributions are obtained under various identification restrictions. Monte Carlo simulations show that the likelihood method is easy to implement and has good finite sample properties.

We examine how the confluence of competition and upstream innovation influences downstream firms’ profit-maximizing strategies. We focus on personal computers and use two novel data sets to describe the dramatic fall in both price (27% at an annual rate) and sales of a computer over its product cycle. Further, we document that computers are typically sold for only four months before being replaced by a higher-quality product. To explain these facts, we develop and calibrate a vintage capital model that combines a competitive market structure with an exogenous rapid rate of innovation.

This paper explores the effects of changes in lending standards on racial segregation within metropolitan areas. Such changes affect neighborhood choices as well as aggregate prices and quantities in the housing market. Using the credit boom of 2000 to 2006 as a large-scale experiment, we put forward an IV strategy that predicts the relaxation of credit standards as the result of a credit supply shock predominantly affecting liquidity-constrained banks. The relaxed lending standards led to significant outflows of whites from black and racially mixed neighborhoods. Without such a credit supply shock, black households would have had between 2.3 and 5.1 percentage points more white neighbors in 2010.

We find using laboratory experiments that primes that make religion salient cause subjects to identify more with their religion and affect their economic choices. The effect on choices varies by religion. For example, priming causes Protestants to increase contributions to public goods, whereas Catholics decrease contributions to public goods, expect others to contribute less to public goods, and become less risk averse. A simple model implies that priming effects reveal the sign of the marginal impact of religious norms on preferences. We find no evidence of religious priming effects on disutility of work effort, discount rates, or dictator game generosity.

We discuss the tension between “what we can get” (identification) and “what we want” (parameters of interest) in models of policy choice (treatment assignment). Our nonstandard empirical object of interest is the ranking of counterfactual policies. Partial identification of treatment effects maps into a partial welfare ranking of treatment assignment policies. We characterize the identified ranking and show how the identifiability of the ranking depends on identifying assumptions, the feasible policy set, and distributional preferences. An application to the project STAR experiment illustrates this dependence. This paper connects the literatures on partial identification, robust statistics, and choice under Knightian uncertainty.

This paper proposes consistent testing methods for examining the effect of a policy treatment on the whole distribution of a response outcome within the setting of a regression discontinuity design. These methods are particularly useful when a policy is expected to produce treatment effects that are heterogeneous along some unobserved characteristics. The test statistics are Kolmogorov-Smirnov-type and are asymptotically distribution free when the data are i.i.d. The proposed tests are applied to three seminal RD studies (Pop-Eleches & Urquiola, 2013; Abdulkadiroglu, Angrist, & Pathak, 2014; and Battistin et al., 2009).

Corruption estimates rely largely on self-reports of affected individuals and officials. Yet survey respondents are often reticent to tell the truth about sensitive subjects, leading to downward biases in surveybased corruption estimates. This paper develops a method to estimate the prevalence of reticent behavior and reticence-adjusted rates of corruption using survey responses to sensitive questions. A statistical model captures how respondents answer a combination of conventional and randomresponse questions, allowing identification of the effect of reticence. GMM and maximum likelihood estimates are obtained for ten countries. Adjusting for reticence dramatically alters the perceptions of the extent of corruption.

In this paper, we relax the assumption of constant regime-specific mean growth rates in Hamilton's (1989) two-state Markov-switching model of the business cycle. We introduce a random walk hierarchy prior for each regime-specific mean growth rate and impose a cointegrating relationship between the mean growth rates in recessionary and expansionary periods. By applying the proposed model to postwar U.S. real GDP growth (1947:Q4–2011:Q3), we uncover the evolving nature of the regime-specific mean growth rates of real output in the U.S. business cycle. Additional features of the postwar U.S. business cycle that we uncover include a steady decline in the long-run mean growth rate of real output over the postwar sample and an asymmetric error-correction mechanism when the economy deviates from its long-run equilibrium.

I estimate the effect of removing children with disabilities from Supplemental Security Income (SSI) on parental earnings and household disability receipt. Using administrative data from the Social Security Administration (SSA), I implement regression discontinuity and differencein- differences designs based on changes in SSA's budget for child medical reviews. I find that parents fully offset the SSI loss with increased earnings, and the loss of the child's SSI payment reduces disability applications by parents and siblings but does not reduce their actual disability receipt. I examine alternative hypotheses for the large parental earnings response.

We consider the revision properties of Federal Reserve Board staff estimates of the output gap after the mid-1990s and examine the usefulness of these estimates for inflation forecasting. Over this period, we find that the Federal Reserve's output gap is more reliably estimated in real time than previous studies have documented for earlier periods and alternative estimation techniques. In contrast to previous work, we also find no deterioration in forecast performance when inflation projections are conditioned on real-time rather than on final estimates of the output gap.