National Bureau of Economic Research

About: National Bureau of Economic Research is a nonprofit organization based out in Cambridge, Massachusetts, United States. It is known for research contribution in the topics: Monetary policy & Population. The organization has 2626 authors who have published 34177 publications receiving 2818124 citations. The organization is also known as: NBER & The National Bureau of Economic Research.

Seigniorage and Political Instability

Abstract: The importance of seigniorage relative to other sources of government revenue differs markedly across countries. This paper tries to explain this regularity by studying a political model of tax reform. The model implies that countries with a more unstable and polarized political system will have more inefficient tax structures and, thus, will rely more heavily on seigniorage. This prediction of the model is tested on cross-sectional data for 79 countries. We find that, after controlling for other variables, political instability is positively associated with seigniorage. (JEL E52, E62, F41)

Monetary Policy in a Data-Rich Environment

Abstract: Most empirical analyses of monetary policy have been confined to frameworks in which the Federal Reserve is implicitly assumed to exploit only a limited amount of information, despite the fact that the Fed actively monitors literally thousands of economic time series. This article explores the feasibility of incorporating richer information sets into the analysis, both positive and normative, of Fed policymaking. We employ a factor-model approach, developed by Stock and Watson (1999a,b), that permits the systematic information in large data sets to be summarized by relatively few estimated factors. With this framework, we reconfirm Stock and Watson's result that the use of large data sets can improve forecast accuracy, and we show that this result does not seem to depend on the use of finally revised (as opposed to 'real-time') data. We estimate policy reaction functions for the Fed that take into account its data-rich environment and provide a test of the hypothesis that Fed actions are explained solely by its forecasts of inflation and real activity. Finally, we explore the possibility of developing an 'expert system' that could aggregate diverse information and provide benchmark policy settings.

Much Ado About Two: Reconsidering Retransformation and the Two-Part Model in Health Economics

Abstract: In health economics applications involving outcomes (y) and covariates (x), it is often the case that the central inferential problems of interest involve E[y|x] and its associated partial effects or elasticities. Many such outcomes have two fundamental statistical properties: yo0; and the outcome y=0 is observed with sufficient frequency that the zeros cannot be ignored econometrically. Common approaches to estimation in such instances include Tobit, selection, and two-part models. This paper (1) describes circumstances where the standard two-part model with homoskedastic retransformation will fail to provide consistent inferences about important policy parameters; and (2) demonstrates some alternative approaches that are likely to prove helpful in applications.

Output Effects of Government Purchases

Abstract: Because of a small direct negative effect on private spending, temporary variations in government purchases as in wartime, would have a strong positive effect on aggregate demand. Intertemporal substitution effects would direct work and production toward these periods where output was valued unusually highly. Defense purchases are divided empirically into "permanent" and "temporary" components by considering the role of (temporary) wars. Shifts in non-defense purchases are mostly permanent. Empirical results verify a strong expansionary effect on output of temporary purchases, but contradict some more specific expectational propositions.

Multi-trait analysis of genome-wide association summary statistics using MTAG.

Abstract: We introduce multi-trait analysis of GWAS (MTAG), a method for joint analysis of summary statistics from genome-wide association studies (GWAS) of different traits, possibly from overlapping samples. We apply MTAG to summary statistics for depressive symptoms (N eff = 354,862), neuroticism (N = 168,105), and subjective well-being (N = 388,538). As compared to the 32, 9, and 13 genome-wide significant loci identified in the single-trait GWAS (most of which are themselves novel), MTAG increases the number of associated loci to 64, 37, and 49, respectively. Moreover, association statistics from MTAG yield more informative bioinformatics analyses and increase the variance explained by polygenic scores by approximately 25%, matching theoretical expectations.