Home  About us  Contact
 

Moment Conditions (moment + condition)

Distribution by Scientific Domains
Mathematics and Statistics50%
Business, Economics, Finance and Accounting50%

Selected Abstracts

Generalized Method of Moments With Many Weak Moment Conditions

ECONOMETRICA, Issue 3 2009
Whitney K. Newey
Using many moment conditions can improve efficiency but makes the usual generalized method of moments (GMM) inferences inaccurate. Two-step GMM is biased. Generalized empirical likelihood (GEL) has smaller bias, but the usual standard errors are too small in instrumental variable settings. In this paper we give a new variance estimator for GEL that addresses this problem. It is consistent under the usual asymptotics and, under many weak moment asymptotics, is larger than usual and is consistent. We also show that the Kleibergen (2005) Lagrange multiplier and conditional likelihood ratio statistics are valid under many weak moments. In addition, we introduce a jackknife GMM estimator, but find that GEL is asymptotically more efficient under many weak moments. In Monte Carlo examples we find that t -statistics based on the new variance estimator have nearly correct size in a wide range of cases. [source]

WEIGHTED SUMS OF NEGATIVELY ASSOCIATED RANDOM VARIABLES

AUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS, Issue 1 2006
Han-Ying Liang
Summary In this paper, we establish strong laws for weighted sums of negatively associated (NA) random variables which have a higher-order moment condition. Some results of Bai Z.D. & Cheng P.E. (2000)[Marcinkiewicz strong laws for linear statistics. Statist. and Probab. Lett.43, 105,112,] and Sung S.K. (2001)[Strong laws for weighted sums of i.i.d. random variables, Statist. and Probab. Lett.52, 413,419] are sharpened and extended from the independent identically distributed case to the NA setting. Also, one of the results of Li D.L. et al. (1995)[Complete convergence and almost sure convergence of weighted sums of random variables. J. Theoret. Probab.8, 49,76,] is complemented and extended. [source]

Generalized Method of Moments With Many Weak Moment Conditions

ECONOMETRICA, Issue 3 2009
Whitney K. Newey
Using many moment conditions can improve efficiency but makes the usual generalized method of moments (GMM) inferences inaccurate. Two-step GMM is biased. Generalized empirical likelihood (GEL) has smaller bias, but the usual standard errors are too small in instrumental variable settings. In this paper we give a new variance estimator for GEL that addresses this problem. It is consistent under the usual asymptotics and, under many weak moment asymptotics, is larger than usual and is consistent. We also show that the Kleibergen (2005) Lagrange multiplier and conditional likelihood ratio statistics are valid under many weak moments. In addition, we introduce a jackknife GMM estimator, but find that GEL is asymptotically more efficient under many weak moments. In Monte Carlo examples we find that t -statistics based on the new variance estimator have nearly correct size in a wide range of cases. [source]

Stability of nonlinear AR-GARCH models

JOURNAL OF TIME SERIES ANALYSIS, Issue 3 2008
Mika Meitz
Abstract., This article studies the stability of nonlinear autoregressive models with conditionally heteroskedastic errors. We consider a nonlinear autoregression of order p [AR(p)] with the conditional variance specified as a nonlinear first-order generalized autoregressive conditional heteroskedasticity [GARCH(1,1)] model. Conditions under which the model is stable in the sense that its Markov chain representation is geometrically ergodic are provided. This implies the existence of an initial distribution such that the process is strictly stationary and , -mixing. Conditions under which the stationary distribution has finite moments are also given. The results cover several nonlinear specifications recently proposed for both the conditional mean and conditional variance, and only require mild moment conditions. [source]

Averaged Periodogram Spectral Estimation with Long-memory Conditional Heteroscedasticity

JOURNAL OF TIME SERIES ANALYSIS, Issue 4 2001
Marc Henry
The empirical relevance of long-memory conditional heteroscedasticity has emerged in a variety of studies of long time series of high frequency financial measurements. A reassessment of the applicability of existing semiparametric frequency domain tools for the analysis of time dependence and long-run behaviour of time series is therefore warranted. To that end, in this paper the averaged periodogram statistic is analysed in the framework of a generalized linear process with long-memory conditional heteroscedastic innovations according to a model specification first proposed by Robinson (Testing for strong serial correlation and dynamic conditional heteroscedasticity in multiple regression. J. Economet. 47 (1991), 67,84). It is shown that the averaged periodogram estimate of the spectral density of a short-memory process remains asymptotically normal with unchanged asymptotic variance under mild moment conditions, and that for strongly dependent processes Robinson's averaged periodogram estimate of long memory (Semiparametric analysis of long memory time series. Ann. Stat. 22 (1994), 515,39) remains consistent. [source]

Model selection tests for nonlinear dynamic models

THE ECONOMETRICS JOURNAL, Issue 1 2002
Douglas Rivers
This paper generalizes Vuong (1989) asymptotically normal tests for model selection in several important directions. First, it allows for incompletely parametrized models such as econometric models defined by moment conditions. Second, it allows for a broad class of estimation methods that includes most estimators currently used in practice. Third, it considers model selection criteria other than the models' likelihoods such as the mean squared errors of prediction. Fourth, the proposed tests are applicable to possibly misspecified nonlinear dynamic models with weakly dependent heterogeneous data. Cases where the estimation methods optimize the model selection criteria are distinguished from cases where they do not. We also consider the estimation of the asymptotic variance of the difference between the competing models' selection criteria, which is necessary to our tests. Finally, we discuss conditions under which our tests are valid. It is seen that the competing models must be essentially nonnested. [source]