Econometric Time Series
http://lists.repec.orgmailman/listinfo/nep-ets
Econometric Time Series
2017-10-15
Bayesian Parametric and Semiparametric Factor Models for Large Realized Covariance Matrices
http://d.repec.org/n?u=RePEc:pra:mprapa:81920&r=ets
This paper introduces a new factor structure suitable for modeling large realized covariance matrices with full likelihood based estimation. Parametric and nonparametric versions are introduced. Due to the computational advantages of our approach we can model the factor nonparametrically as a Dirichlet process mixture or as an infinite hidden Markov mixture which leads to an infinite mixture of inverse-Wishart distributions. Applications to 10 assets and 60 assets show the models perform well. By exploiting parallel computing the models can be estimated in a matter of a few minutes.
Jin, Xin
Maheu, John M
Yang, Qiao
infinite hidden Markov model, Dirichlet process mixture, inverse-Wishart, predictive density, high-frequency data
2017-10-12
Inference for Impulse Responses under Model Uncertainty
http://d.repec.org/n?u=RePEc:unm:umagsb:2017022&r=ets
In many macroeconomic applications, impulse responses and their (bootstrap) confidence intervals are constructed by estimating a VAR model in levels - thus ignoring uncertainty regarding the true (unknown) cointegration rank. While it is well known that using a wrong cointegration rank leads to invalid (bootstrap) inference, we demonstrate that even if the rank is consistently estimated, ignoring uncertainty regarding the true rank can make inference highly unreliable for sample sizes encountered in macroeconomic applications. We investigate the effects of rank uncertainty in a simulation study, comparing several methods designed for handling model uncertainty. We propose a new method - Weighted Inference by Model Plausibility (WIMP) - that takes rank uncertainty into account in a fully data-driven way and outperforms all other methods considered in the simulation study. The WIMP method is shown to deliver intervals that are robust to rank uncertainty, yet allow for meaningful inference, approaching fixed rank intervals when evidence for a particular rank is strong. We study the potential ramifications of rank uncertainty on applied macroeconomic analysis by re-assessing the effects of fiscal policy shocks based on a variety of identification schemes that have been considered in the literature. We demonstrate how sensitive the results are to the treatment of the cointegration rank, and show how formally accounting for rank uncertainty can affect the conclusions.
Lieb, Lenard
Smeekes, Stephan
Impulse response analysis, cointegration, model uncertainty, bootstrap inference, fiscal policy shocks
2017-10-03
Estimation for time-invariant effects in dynamic panel data models with application to income dynamics
http://d.repec.org/n?u=RePEc:lsu:lsuwpp:2017-12&r=ets
A two-step estimation procedure is proposed to estimate the time-invariant effects, i.e., the slopes of the time-invariant regressors, in dynamic panel data models. In the first step, generalized method of moments (GMM) is used to estimate the time-varying effects, and the second step is to run cross-sectional OLS regression of the time series average of the residuals from the GMM estimation on the time-invariant regressors to estimate the time-invariant effects. It is shown that the OLS estimator of time-invariant effects is pN-consistent and asymptotically normally distributed. A consistent estimator for the asymptotic variance of the estimator is also provided, which is robust to errors with heteroscedasticity and works well even if the errors are serially correlated. Monte Carlo simulations confirm the theoretical findings. Application to income dynamics highlights the importance of estimating time- invariant effects such as education, race and gender in return to schooling.
Yonghui Zhang
Qiankun Zhou
Dynamic panel, GMM, OLS, Time-invariant effects, Return to schooling.
2017-10