Search Results

(refine search)

SORT BY: PREVIOUS / NEXT

All:
Title:
Author:
Abstract:
Keywords:

+ Add Search Parameter

Working Paper
The Income-Achievement Gap and Adult Outcome Inequality
Nielsen, Eric R. (2015-05-14)
This paper discusses various methods for assessing group differences in academic achievement using only the ordinal content of achievement test scores. Researchers and policymakers frequently draw conclusions about achievement differences between various populations using methods that rely on the cardinal comparability of test scores. This paper shows that such methods can lead to erroneous conclusions in an important application: measuring changes over time in the achievement gap between youth from high- and low-income households. Commonly-employed, cardinal methods suggest that this ...
Finance and Economics Discussion Series , Paper 2015-41

Discussion Paper
Estimating the output gap in real time
Cheremukhin, Anton A. (2013-12)
I propose a novel method of estimating the potential level of U.S. GDP in real time. The proposed wage-based measure of economic potential remains virtually unchanged when new data are released. The distance between current and potential output ? the output gap ? satisfies Okun?s law and outperforms many other measures of slack in forecasting inflation. Thus, I provide a robust statistical tool useful for understanding current economic conditions and guiding policymaking.
Staff Papers , Issue Dec

Working Paper
In Search of Lost Time Aggregation
Crawley, Edmund S. (2019-11-01)
In 1960, Working noted that time aggregation of a random walk induces serial correlation in the first difference that is not present in the original series. This important contribution has been overlooked in a recent literature analyzing income and consumption in panel data. I examine Blundell, Pistaferri and Preston (2008) as an important example for which time aggregation has quantitatively large effects. Using new techniques to correct for the problem, I find the estimate for the partial insurance to transitory shocks, originally estimated to be 0.05, increases to 0.24. This larger ...
Finance and Economics Discussion Series , Paper 2019-075

Working Paper
Explaining Machine Learning by Bootstrapping Partial Marginal Effects and Shapley Values
Cook, Thomas R.; Modig, Zach; Palmer, Nathan M. (2024-09-20)
Machine learning and artificial intelligence are often described as “black boxes.” Traditional linear regression is interpreted through its marginal relationships as captured by regression coefficients. We show that the same marginal relationship can be described rigorously for any machine learning model by calculating the slope of the partial dependence functions, which we call the partial marginal effect (PME). We prove that the PME of OLS is analytically equivalent to the OLS regression coefficient. Bootstrapping provides standard errors and confidence intervals around the point ...
Finance and Economics Discussion Series , Paper 2024-075

Working Paper
Learning About Consumer Uncertainty from Qualitative Surveys: As Uncertain As Ever
Sharp, Robert; Sarte, Pierre-Daniel G.; Pinto, Santiago (2015-09-03)
We study diffusion indices constructed from qualitative surveys to provide real-time assessments of various aspects of economic activity. In particular, we highlight the role of diffusion indices as estimates of change in a quasi extensive margin, and characterize their distribution, focusing on the uncertainty implied by both sampling and the polarization of participants' responses. Because qualitative tendency surveys generally cover multiple questions around a topic, a key aspect of this uncertainty concerns the coincidence of responses, or the degree to which polarization comoves, across ...
Working Paper , Paper 15-9

Working Paper
The Impact of Market Factors on Racial Identity: Evidence from Multiracial Survey Respondents
Jackson, Osborne (2024-10-01)
This paper examines the reported race of multiracial persons in the US Current Population Survey (CPS) before 2003, when limited response options exogenously constrained respondents to identify as a single race. Using this survey attribute and the 16-month longitudinal design of the basic monthly CPS, I explore whether market factors help causally determine racial identity. I find that pre-2003 race responds to state-level (1) racial composition, due largely to household composition, and (2) unemployment rates and wages by race. Although these findings suggest potential endogeneity of race, ...
Working Papers , Paper 24-13

Working Paper
Estimating (Markov-Switching) VAR Models without Gibbs Sampling: A Sequential Monte Carlo Approach
Bognanni, Mark; Herbst, Edward (2015-12-18)
Vector autoregressions with Markov-switching parameters (MS-VARs) fit the data better than do their constant-parameter predecessors. However, Bayesian inference for MS-VARs with existing algorithms remains challenging. For our first contribution, we show that Sequential Monte Carlo (SMC) estimators accurately estimate Bayesian MS-VAR posteriors. Relative to multi-step, model-specific MCMC routines, SMC has the advantages of generality, parallelizability, and freedom from reliance on particular analytical relationships between prior and likelihood. For our second contribution, we use SMC's ...
Finance and Economics Discussion Series , Paper 2015-116

Working Paper
Latent Variables Analysis in Structural Models: A New Decomposition of the Kalman Smoother
Chung, Hess T.; Fuentes-Albero, Cristina; Paustian, Matthias; Pfajfar, Damjan (2020-12-04)
This paper advocates chaining the decomposition of shocks into contributions from forecast errors to the shock decomposition of the latent vector to better understand model inference about latent variables. Such a double decomposition allows us to gauge the inuence of data on latent variables, like the data decomposition. However, by taking into account the transmission mechanisms of each type of shock, we can highlight the economic structure underlying the relationship between the data and the latent variables. We demonstrate the usefulness of this approach by detailing the role of ...
Finance and Economics Discussion Series , Paper 2020-100

Working Paper
Explaining Machine Learning by Bootstrapping Partial Dependence Functions and Shapley Values
Palmer, Nathan M.; Modig, Zach; Gupton, Greg; Cook, Thomas R. (2021-11-15)
Machine learning and artificial intelligence methods are often referred to as “black boxes” when compared with traditional regression-based approaches. However, both traditional and machine learning methods are concerned with modeling the joint distribution between endogenous (target) and exogenous (input) variables. Where linear models describe the fitted relationship between the target and input variables via the slope of that relationship (coefficient estimates), the same fitted relationship can be described rigorously for any machine learning model by first-differencing the partial ...
Research Working Paper , Paper RWP 21-12

Report
On binscatter
Cattaneo, Matias D.; Crump, Richard K.; Farrell, Max H.; Feng , Yingjie (2019-02-01)
Binscatter is a popular method for visualizing bivariate relationships and conducting informal specification testing. We study the properties of this method formally and develop enhanced visualization and econometric binscatter tools. These include estimating conditional means with optimal binning and quantifying uncertainty. We also highlight a methodological problem related to covariate adjustment that can yield incorrect conclusions. We revisit two applications using our methodology and find substantially different results relative to those obtained using prior informal binscatter methods. ...
Staff Reports , Paper 881

show more (38)

PREVIOUS / NEXT

Fed in Print

Search Results

FILTER BY year

FILTER BY Bank

FILTER BY Series

FILTER BY Content Type

FILTER BY Author

FILTER BY Jel Classification

FILTER BY Keywords