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Canonical correlation statistics for testing the cointegration rank in a reversed order

Breitung, Jörg - 1998

In this paper a Canonical Correlation Analysis (CCA) is used to test the hypothesis r = r0 against the alternative r …

Persistent link: https://www.econbiz.de/10009578561

Estimating covariance matrices using estimating functions in nonparametric and semiparametric regression

Carroll, Raymond J.; Iturria, Stephen J.; Gutierrez, … - 1997

We use ideas from estimating function theory to derive new, simply computed consistent covariance matrix estimates in nonparametric regression and in a class of semiparametric problems. Unlike other estimates in the literature, ours do not require auxiliary or additional nonparametric...

Persistent link: https://www.econbiz.de/10009631747

Temporal aggregation and causality in multiple time series models

Breitung, Jörg; Swanson, Norman R. - 1998

the concept of contemporaneous correlation due to Swanson and Granger (1997) with that of Granger causality. Using … results allow us to differentiate between spurious contemporaneous correlation arising because of aggregation, and true … and finite aggregation intervals. -- Granger causality ; instantaneous causality ; contemporaneous correlation ; temporal …

Persistent link: https://www.econbiz.de/10009578029

The Beveridge-Nelson decomposition : a different perspective with new results

Gómez, Víctor; Breitung, Jörg - 1998

We show in the paper that the decomposition proposed by Beveridge and Nelson (1981) for models that are integrated of order one can be generalized to seasonal Arima models by means of a partial fraction decomposition. Two equivalent algorithms are proposed to optimally (in the mean squared...

Persistent link: https://www.econbiz.de/10009577456

Properties of the nonparametric autoregressive bootstrap

Franke, Jürgen; Kreiß, Jens-Peter; Mammen, Enno; … - 1998

We prove geometric ergodicity and absolute regularity of the nonparametric autoregressive bootstrap process. To this end, we revisit this problem for nonparametric autoregressive processes and give some quantitative conditions (i.e., with explicit constants) under which the mixing coefficients...

Persistent link: https://www.econbiz.de/10009578012

Semiparametric estimation and prediction for time series cross sectional data

Bunke, Olaf - 1998

This paper discusses a methodology which uses time series cross sectional datafor the estimation of a time dependent regression function depending on explanatory variables and for the prediction of values of the dependent variable. The methodology assumes independent observations and is based on...

Persistent link: https://www.econbiz.de/10009578017

Modeling panels of intercorrelated autoregressive time series

Hjellvik, Vidar; Tjostheim, Dag - 1998

We propose a method of modeling panel time series data with both inter- and intra-individual correlation, and of …, ignoring correlation, will become increasingly inferior. Asymptotic normality of estimators is established, and our results are …

Persistent link: https://www.econbiz.de/10009578021

Vector autoregressions

Lütkepohl, Helmut - 1999

Persistent link: https://www.econbiz.de/10009579182

Benkwitz, Alexander; Lütkepohl, Helmut; Neumann, Michael H. - 1997

Bootstrap confidence intervals for impulse responses computed from autoregressive processes are considered. A detailed analysis of the methods in current use shows that they are not very reliable in some cases. In particular, there are theoretical reasons for them to have actual coverage...

Persistent link: https://www.econbiz.de/10009660382

Nonparametric specification testing for continuous-time models with application to spot interest rates

Hong, Yongmiao; Li, Haitao - 2002

We propose two nonparametric transition density-based speciÞcation tests for continuous-time diffusion models. In contrast to marginal density as used in the literature, transition density can capture the full dynamics of a diffusion process, and in particular, can distinguish processes with...

Persistent link: https://www.econbiz.de/10009621413

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