Results 11  20
of
321
2003): “Forecast uncertainties in macroeconometric modelling: an application to the UK economy
 Journal of the American Statistical Association
"... This paper argues that probability forecasts convey information on the uncertainties that surround macroeconomic forecasts in a straightforward manner which is preferable to other alternatives, including the use of confidence intervals. Probability forecasts obtained using a small benchmark macroec ..."
Abstract

Cited by 44 (14 self)
 Add to MetaCart
This paper argues that probability forecasts convey information on the uncertainties that surround macroeconomic forecasts in a straightforward manner which is preferable to other alternatives, including the use of confidence intervals. Probability forecasts obtained using a small benchmark macroeconometric model as well as a number of other alternatives are presented and evaluated using recursive forecasts generated over the period 1999q12001q1. Out of sample probability forecasts of inflation and output growth are also provided over the period 2001q22003q1, and their implications discussed in relation to the Bank of England’s inflation target and the need to avoid recessions, both as separate events and jointly. The robustness of the results to parameter and model uncertainties is also investigated by a pragmatic implementation of the Bayesian model averaging approach.
Resampling fewer than n observations: gains, losses, and remedies for losses
 Statist. Sinica
, 1997
"... this paper we ..."
Mechanism Choice and Strategic Bidding in Divisible Good Auctions: An Empirical Analysis of the Turkish Treasury Auction Market
, 2002
"... ..."
A Threestep Method for Choosing the Number of Bootstrap Repetitions
 Econometrica
, 2000
"... This paper considers the problem of choosing the number of bootstrap repetitions B for bootstrap standard errors, confidence intervals, confidence regions, hypothesis tests, pvalues, and bias correction. For each of these problems, the paper provides a threestep method for choosing B to achieve a ..."
Abstract

Cited by 36 (1 self)
 Add to MetaCart
This paper considers the problem of choosing the number of bootstrap repetitions B for bootstrap standard errors, confidence intervals, confidence regions, hypothesis tests, pvalues, and bias correction. For each of these problems, the paper provides a threestep method for choosing B to achieve a desired level of accuracy. Accuracy is measured by the percentage deviation of the bootstrap standard error estimate, confidence interval length, test’s critical value, test’s pvalue, or biascorrected estimate based on B bootstrap simulations from the corresponding ideal bootstrap quantities for which B��. The results apply quite generally to parametric, semiparametric, and nonparametric models with independent and dependent data. The results apply to the standard nonparametric iid bootstrap, moving block bootstraps for time series data, parametric and semiparametric bootstraps, and bootstraps for regression models based on bootstrapping residuals. Monte Carlo simulations show that the proposed methods work very well.
Testing for Linearity
 Journal of Economic Surveys
, 1999
"... Abstract. The problem of testing for linearity and the number of regimes in the context of selfexciting threshold autoregressive (SETAR) models is reviewed. We describe leastsquares methods of estimation and inference. The primary complication is that the testing problem is nonstandard, due to th ..."
Abstract

Cited by 35 (1 self)
 Add to MetaCart
Abstract. The problem of testing for linearity and the number of regimes in the context of selfexciting threshold autoregressive (SETAR) models is reviewed. We describe leastsquares methods of estimation and inference. The primary complication is that the testing problem is nonstandard, due to the presence of parameters which are only defined under the alternative, so the asymptotic distribution of the test statistics is nonstandard. Simulation methods to calculate asymptotic and bootstrap distributions are presented. As the sampling distributions are quite sensitive to conditional heteroskedasticity in the error, careful modeling of the conditional variance is necessary for accurate inference on the conditional mean. We illustrate these methods with two applications Ð annual sunspot means and monthly U.S. industrial production. We find that annual sunspots and monthly industrial production are SETAR(2) processes. Keywords. SETAR models; Thresholds; Nonstandard asymptotic theory; Bootstrap
Consumption over the Life Cycle: Facts from the Consumer Expenditure Survey Data
, 2006
"... This paper uses Consumer Expenditure Survey data and a seminonparametric statistical model to estimate lifecycle profiles of consumption, controlling for demographics, cohort, and time effects. We construct age profiles for total and nondurable consumption as well as expenditure patterns for consum ..."
Abstract

Cited by 33 (1 self)
 Add to MetaCart
This paper uses Consumer Expenditure Survey data and a seminonparametric statistical model to estimate lifecycle profiles of consumption, controlling for demographics, cohort, and time effects. We construct age profiles for total and nondurable consumption as well as expenditure patterns for consumer durables. Special emphasis is placed on the comparison of different approaches to control for changes in demographics over the life cycle. We find significant humps over the life cycle for total, nondurable, and durable expenditures. Changes in household
Bootstrap confidence intervals: when, which, what? A practical guide for medical statisticians
, 2000
"... Since the early 1980s, a bewildering array of methods for constructing bootstrap confidence intervals have been proposed. In this article, we address the following questions. First, when should bootstrap confidence intervals be used. Secondly, which method should be chosen, and thirdly, how should i ..."
Abstract

Cited by 32 (0 self)
 Add to MetaCart
Since the early 1980s, a bewildering array of methods for constructing bootstrap confidence intervals have been proposed. In this article, we address the following questions. First, when should bootstrap confidence intervals be used. Secondly, which method should be chosen, and thirdly, how should it be implemented. In order to do this, we review the common algorithms for resampling and methods for constructing bootstrap confidence intervals, together with some less well known ones, highlighting their strengths and weaknesses. We then present a simulation study, a flow chart for choosing an appropriate method and a survival analysis example.
Consumption over the Life Cycle: Some Facts from Consumer Expenditure Survey Data
, 2002
"... This paper uses a seminonparametric model and Consumer Expenditure Survey data to estimate life cycle profiles of consumption, controlling for demographics, cohort and time effects. In addition to documenting profiles for total and nondurable consumption, we devote special attention to the age ex ..."
Abstract

Cited by 29 (0 self)
 Add to MetaCart
This paper uses a seminonparametric model and Consumer Expenditure Survey data to estimate life cycle profiles of consumption, controlling for demographics, cohort and time effects. In addition to documenting profiles for total and nondurable consumption, we devote special attention to the age expenditure pattern for consumer durables. We find humpshaped paths over the life cycle for total, for nondurable and for durable expenditures. Changes in household size account for roughly half of these humps. The other half remains unaccounted for by the standard complete markets life cycle model. Our results imply that households do not smooth consumption over their lifetimes. This is especially true for services from consumer durables. Bootstrap simulations suggest that our empirical estimates are tight and sensitivity analysis indicates that the computed profiles are robust to a large number of different specifications.
Approximately unbiased tests of regions using multistepmultiscale bootstrap resampling
 Annals of Statistics
, 2004
"... Approximately unbiased tests based on bootstrap probabilities are considered for the exponential family of distributions with unknown expectation parameter vector, where the null hypothesis is represented as an arbitraryshaped region with smooth boundaries. This problem has been discussed previously ..."
Abstract

Cited by 28 (9 self)
 Add to MetaCart
Approximately unbiased tests based on bootstrap probabilities are considered for the exponential family of distributions with unknown expectation parameter vector, where the null hypothesis is represented as an arbitraryshaped region with smooth boundaries. This problem has been discussed previously in Efron and Tibshirani [Ann. Statist. 26 (1998) 1687–1718], and a corrected pvalue with secondorder asymptotic accuracy is calculated by the twolevel bootstrap of Efron, Halloran and Holmes [Proc. Natl. Acad. Sci. U.S.A. 93 (1996) 13429–13434] based on the ABC bias correction of Efron [J. Amer. Statist. Assoc. 82 (1987) 171–185]. Our argument is an extension of their asymptotic theory, where the geometry, such as the signed distance and the curvature of the boundary, plays an important role. We give another calculation of the corrected pvalue without finding the “nearest point ” on the boundary to the observation, which is required in the twolevel bootstrap and is an implementational burden in complicated problems. The key idea is to alter the sample size of the replicated dataset from that of the observed dataset.
Statistical Inference via Bootstrapping for Measures of Inequality
, 1995
"... In this paper we consider the use of bootstrap methods to compute interval estimates and perform hypothesis tests for decomposable measures of economic inequality. The bootstrap potentially represents a significant gain over available asymptotic intervals because it provides an easily implemented so ..."
Abstract

Cited by 27 (0 self)
 Add to MetaCart
In this paper we consider the use of bootstrap methods to compute interval estimates and perform hypothesis tests for decomposable measures of economic inequality. The bootstrap potentially represents a significant gain over available asymptotic intervals because it provides an easily implemented solution to the BehrensFisher problem. Two applications of this approach, using the PSID (for the study of taxation) and the XLSY (for the study of youth inequality), to the Gini coefficient and Theil’s entropy measures of inequality, are provided. The results suggest that (i) statistical inference is essential even when large samples are available, and (ii) the bootstrap appears to perform well in this setting.