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Ejara, Demissew Diro; Boynton, Wentworth; Lu, Xinyi - 2022

We provide nonparametric quantile regressions to test for autocorrelation patterns for weekly and monthly stock returns … distribution for the best description of the autocorrelation properties …

Persistent link: https://www.econbiz.de/10013293358

Nonparametric Kernel Estimation of Evolutionary Autoregressive Processes

Kim, Woocheol - 2012

This paper develops a new econometric tool for evolutionary auto- regressive models, where the AR coefficients change smoothly over time. To estimate the unknown functional form of time-varying coefficients, we propose a modified local linear smoother. The asymptotic normality and variance of...

Persistent link: https://www.econbiz.de/10013097340

Semiparametric Estimation and Testing of Smooth Coefficient Spatial Autoregressive Models

Malikov, Emir - 2017

This paper considers a flexible semiparametric spatial autoregressive (mixed-regressive) model in which unknown coefficients are permitted to be nonparametric functions of some contextual variables to allow for potential nonlinearities and parameter heterogeneity in the spatial relationship....

Persistent link: https://www.econbiz.de/10012961818

Adapting to Unknown Disturbance Autocorrelation in Regression with Long Memory

Hidalgo, Javier S. - 2008

We show that it is possible to adapt to nonparametric disturbance auto-correlation in time series regression in the …

Persistent link: https://www.econbiz.de/10012771033

Nonparametric Inference of Jump Autocorrelation

Kwok, Simon - 2020

this paper, we introduce a nonparametric inference procedure for the presence of jump autocorrelation in the DGP. Our … toolkit includes (i) an omnibus test that jointly detect the autocorrelation of stationary jumps over all lags, and (ii) a … jump autocorrelogram that enables visualization and pointwise inference of jump autocorrelation. We establish asymptotic …

Persistent link: https://www.econbiz.de/10012824843

Indirect Inference for Locally Stationary Models

Frazier, David - 2020

We propose the use of indirect inference estimation to conduct inference in complex locally stationary models. We develop a local indirect inference algorithm and establish the asymptotic properties of the proposed estimator. Due to the nonparametric nature of locally stationary models, the...

Persistent link: https://www.econbiz.de/10012852112

Linear Programming-Based Estimators in Nonnegative Autoregression

Preve, Daniel P. A. - 2015

This note studies robust estimation of the autoregressive (AR) parameter in a nonlinear, nonnegative AR model driven by nonnegative errors. It is shown that a linear programming estimator (LPE), considered by Nielsen and Shephard (2003) among others, remains consistent under severe model...

Persistent link: https://www.econbiz.de/10013016613

Power Maximization and Size Control in Heteroskedasticity and Autocorrelation Robust Tests with Exponentiated Kernels

Sun, Yixiao - 2010

Using the power kernels of Phillips, Sun and Jin (2006, 2007), we examine the large sample asymptotic properties of the t-test for different choices of power parameter (rho). We show that the nonstandard fixed-rho limit distributions of the t-statistic provide more accurate approximations to the...

Persistent link: https://www.econbiz.de/10013148975

Quasi-maximum likelihood and the kernel block bootstrap for nonlinear dynamic models

Parente, Paulo M. D. C.; Smith, Richard J. - 2019 - This Draft: October 2019

This paper applies a novel bootstrap method, the kernel block bootstrap, to quasi-maximum likelihood estimation of dynamic models with stationary strong mixing data. The method first kernel weights the components comprising the quasi-log likelihood function in an appropriate way and then samples...

Persistent link: https://www.econbiz.de/10012115888

The Effect of Model-Selection Uncertainty on Autoregressive Models Estimates

Azzam, Islam - 2011

Model-selection uncertainty corresponds to the uncertainty about the true lag order of the autoregressive process that should be picked. This paper shows that all model-selection criteria perform poorly in small samples. Model-selection uncertainty adds to the bias and variability in the...

Persistent link: https://www.econbiz.de/10014178863