Journal of Business & Economic Statistics articles from January 2001

Journal of Business & Economic Statistics archives from January 2001

Editorial Announcement.(Editorial)(Brief Article)
January 1, 2001... With this issue, Eric Ghysels and Alastair R. Hall begin their terms as coeditors of JBES. Starting January 1, 2001, manuscripts should be sent to Editorial Office Journal of Business & Economic Statistics Department of Economics...

Estimation of Limited Dependent Variable Models With Dummy Endogenous Regressors: Simple Strategies for Empirical Practice.
January 1, 2001... Editor's Note. This article is the JBES 1999 Invited Lecture at the Joint Statistical Meetings, August 1999, in Baltimore, Maryland. Applied economists have long struggled with the question of how to accommodate binary endogenous regressors...

Tests for Forecast Encompassing When Forecasts Depend on Estimated Regression Parameters.
January 1, 2001... This article presents analytical and simulation results on the properties of two tests for forecast encompassing, allowing throughout for dependence of the forecasts on estimated regression parameters. One test, which was intended for forecasts...

Testing for Forecast Consensus.
January 1, 2001... A panel of forecasts may be defined to be in consensus when individual forecasters place identical weights on a common latent variable. We suggest this definition and formulate a dynamic latent-variable model to test for consensus. This method...

On the Normal Inverse Gaussian Stochastic Volatility Model.
January 1, 2001... In this article, the normal inverse Oaussian stochastic volatility model of Barndorff-Nielsen is extended. The resulting model has a more flexible lag structure than the original one. In addition, the second- and fourth-order moments, important...

Influence Diagnostics and Estimation Algorithms for Powell's SCLS.
January 1, 2001... This article studies influence diagnostics and estimation algorithms for Powell's symmetrically censored least squares estimator. The proposed measures of influence are based on one-step approximations to the analogous deletion diagnostics used...

Bootstrap Testing Linear Restrictions on Cointegrating Vectors.
January 1, 2001... We consider a computer-intensive method for inference on cointegrating vectors in maximum likelihood cointegration analysis. Simulation studies show that the size distortion for the asymptotic likelihood ratio test can be considerable for small...

A Time-Varying Parameter Model to Test for Predictability and Integration in the Stock Markets of Transition Economies.
January 1, 2001... This article introduces a model, based on the Kalman-filter framework, that allows for time-varying parameters, latent factors, and a general generalized autoregressive conditional heteroscedasticity (GARCH) structure for the residuals. With...

Explaining Long-and Short-Run Interactions in Time Series Data.
January 1, 2001... In this article, I extend the concept of separate cointegration to include the common-feature trend-cycle decomposition approach. This combined approach operates a reduction of the parameter space and permits the identification of the time...

Forecasting an Accumulated Series Based on Partial Accumulation: A Bayesian Method for Short Series With Seasonal Patterns.
January 1, 2001... We present a Bayesian solution to forecasting a time series when few observations are available. The quantity to predict is the accumulated value of a positive, continuous variable when partially accumulated data are observed. These conditions...

Volatility of Stock-Market Indexes--An Analysis Based on SEMIFAR Models.
January 1, 2001... By applying SEMIFAR models, we examine "long memory" in the volatility of worldwide stock-market indexes. Our analysis yields strong evidence of "long memory" in stock-market volatility, either in terms of stochastic long-range dependence or in...

Bootstrap-After-Bootstrap Prediction Intervals for Autoregressive Models.
January 1, 2001... The use of the Bonferroni prediction interval based on the bootstrap-after-bootstrap is proposed for autoregressive (AR) models. Monte Carlo simulations are conducted using a number of AR models including stationary, unit-root, and...