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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 ...
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 ...
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 ...
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 ...
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 ...
Form invariance in biased sampling problems
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 ...
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 ...
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.