Search Results

Showing results 1 to 10 of approximately 10.

(refine search)
SORT BY: PREVIOUS / NEXT
Keywords:Sampling (Statistics) 

Journal Article
Noteworthy: natural gas: glitches point to inflated output data
Natural gas production and consumption data have been drifting apart. Production should equal consumption plus increases or decreases in storage, but sampling and estimation errors typically result in slight discrepancies. Seeing these gaps rise, the Energy Information Administration (EIA) implemented a new methodology with the release of February's production data that should ensure greater accuracy. Estimates for the prior 12 months were revised as well.
AUTHORS: Thies, Jackson
DATE: 2010

Working Paper
Form invariance in biased sampling problems
AUTHORS: Madrigal, Vicente; Smith, Stephen D.
DATE: 1992

Working Paper
On the finite-sample accuracy of nonparametric resampling algorithms for economic time series
In recent years, there has been increasing interest in nonparametric bootstrap inference for economic time series. Nonparametric resampling techniques help protect against overly optimistic inference in time series models of unknown structure. They are particularly useful for evaluating the fit of dynamic economic models in terms of their spectra, impulse responses, and related statistics, because they do not require a correctly specified economic model. Notwithstanding the potential advantages of nonparametric bootstrap methods, their reliability in small samples is questionable. In this paper, we provide a benchmark for the relative accuracy of several nonparametric resampling algorithms based on ARMA representations of four macroeconomic time series. For each algorithm, we evaluate the effective coverage accuracy of impulse response and spectral density bootstrap confidence intervals for standard sample sizes. We find that the autoregressive sieve approach based on the encompassing model is most accurate. However, care must be exercised in selecting the lag order of the autoregressive approximation.
AUTHORS: Berkowitz, Jeremy; Biegean, Ionel; Kilian, Lutz
DATE: 1999

Working Paper
The power of long-run structural VARs
Are structural vector autoregressions (VARs) useful for discriminating between macro models? Recent assessments of VARs have shown that these statistical methods have adequate size properties. In other words, in simulation exercises, VARs will only infrequently reject the true data generating process. However, in assessing a statistical test, we often also care about power: the ability of the test to reject a false hypothesis. Much less is known about the power of structural VARs. ; This paper attempts to fill in this gap by exploring the power of long-run structural VARs against a set of DSGE models that vary in degree from the true data generating process. We report results for two tests: the standard test of checking the sign on impact and a test of the shape of the response. For the models studied here, testing the shape is a more powerful test than simply looking at the sign of the response. In addition, relative to an alternative statistical test based on sample correlations, we find that the shape-based tests have greater power. Given the results on the power and size properties of long-run VARs, we conclude that these VARs are useful for discriminating between macro models.
AUTHORS: Gust, Christopher J.; Vigfusson, Robert J.
DATE: 2009

Journal Article
How well do diffusion indexes capture business cycles? A spectral analysis
AUTHORS: Owens, Raymond E.; Sarte, Pierre-Daniel G.
DATE: 2005

Working Paper
Small sample properties of estimators of non-linear models of covariance structure
This study examines the small sample properties of GMM and ML estimators of non-linear models of covariance structure. The study focuses on the properties of parameter estimates and the Hansen (1982) and Newey (1985) model specification test. It use Monte Carlo simulations to consider the properties of estimates for some simple factor models, the Hall and Mishkin (1982) model of consumption and income changes, and a simple Bernanke (1986) decomposition model. This analysis establishes and seeks to explain a number of results. Most importantly, optimally weighted GMM estimation yields some biased parameter estimates, and GMM estimation yields a model specification test with size substantially greater than the asymptotic size.
AUTHORS: Clark, Todd E.
DATE: 1995

Report
Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments
Data augmentation and Gibbs sampling are two closely related, sampling-based approaches to the calculation of posterior moments. The fact that each produces a sample whose constituents are neither independent nor identically distributed complicates the assessment of convergence and numerical accuracy of the approximations to the expected value of functions of interest under the posterior. In this paper methods for spectral analysis are used to evaluate numerical accuracy formally and construct diagnostics for convergence. These methods are illustrated in the normal linear model with informative priors, and in the Tobit-censored regression model.
AUTHORS: Geweke, John F.
DATE: 1991

Report
A sampling-window approach to transactions-based Libor fixing
We examine the properties of a method for fixing Libor rates that is based on transactions data and multi-day sampling windows. The use of a sampling window may mitigate problems caused by thin transaction volumes in unsecured wholesale term funding markets. Using two partial data sets of loan transactions, we estimate how the use of different sampling windows could affect the statistical properties of Libor fixings at various maturities. Our methodology, which is based on a multiplicative estimate of sampling noise that avoids the need for interest rate data, uses only the timing and sizes of transactions. Limitations of this sampling-window approach are also discussed.
AUTHORS: Duffie, Darrell; Skeie, David R.; Vickery, James
DATE: 2013

Report
Multiple ratings and credit standards: differences of opinion in the credit rating industry
Rating-dependent financial regulators assume that the same letter ratings from different agencies imply the same levels of default risk. Most "third" agencies, however, assign significantly higher ratings on average than Moody's and Standard & Poor's. We show that, contrary to the claims of some rating industry professionals, sample selection bias can account for at most half of the observed average difference in ratings. We also investigate the economic rationale for using multiple rating agencies. Among the many variables considered, only size and bond-issuance history are consistently related to the probability of an issuer seeking third ratings. The probability ties to improve their standing under rating-dependent regulations.
AUTHORS: Packer, Frank; Cantor, Richard
DATE: 1996

Report
An introduction to the FRBNY Consumer Credit Panel
In this paper, we introduce the FRBNY Consumer Credit Panel, a new longitudinal database with detailed information on consumer debt and credit. The panel uses a unique sample design and information derived from consumer credit reports to track individuals? and households? access to and use of credit at a quarterly frequency. In any given quarter ranging from the first quarter of 1999 to the present, the panel can be used to compute nationally representative estimates of the levels and changes in various aspects of individual and household liabilities. In addition to describing the sample design, the use of sample weights, and the credit report information included in the database, we provide some comparisons of population statistics and consumer debt estimates derived from our panel with those based on data from the American Community Survey and the Flow of Funds Accounts of the United States.
AUTHORS: Lee, Donghoon; Van der Klaauw, Wilbert
DATE: 2010

FILTER BY year

FILTER BY Content Type

PREVIOUS / NEXT