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We propose new over-identifying restriction (OIR) tests that are robust to heteroskedasticity and serial correlations of unknown form. The proposed tests do not require consistent estimation of the asymptotic covariance matrix and hence avoid choosing the bandwidth in nonparametric kernel...
Persistent link: https://www.econbiz.de/10010785290
Motivated by Chaudhuri's work (1996) on unconditional geometric quantiles, we explore the asymptotic properties of sample geometric conditional quantiles, defined through kernel functions, in high dimensional spaces. We establish a Bahadur type linear representation for the geometric conditional...
Persistent link: https://www.econbiz.de/10005137392
In many situations, we want to verify the existence of a relationship between multivariate time series. Here, we propose a semiparametric approach for testing the independence between two infinite order vector autoregressive (VAR()) series which is an extension of Hong's (1996a) univariate...
Persistent link: https://www.econbiz.de/10005417571
The problem of estimating an unknown density function has been widely studied. In this paper we present a convolution estimator for the density of the responses in a nonlinear regression model. The rate of convergence for the variance of the convolution estimator is of order 1/n. This is faster...
Persistent link: https://www.econbiz.de/10005645051
This paper extends Kiefer, Vogelsang, and Bunzel (2000) and Kiefer and Vogelsang (2002b) to propose a class of over-identifying restrictions (OIR) tests that are robust to heteroskedasticity and serial correlations of unknown form. These OIR tests do not require consistent estimation of the...
Persistent link: https://www.econbiz.de/10010739165
This paper investigates the forecasting power of stock prices using two methods, namely, the random walk and the non-parametric methods. Using daily prices of the FTSE/JSE All Share index it is found that non-parametric methodology reveals distributional behaviour in the time series that is not...
Persistent link: https://www.econbiz.de/10008914366
This paper proposes an asymptotic one-sided N(0, 1) test for independence between two stationary time series using the empirical characteristic function. Unlike the tests based on the cross-correlation function (e.g. Haugh, 1976; Hong, 1996; Koch & Yang 1986), the proposed test has power against all...
Persistent link: https://www.econbiz.de/10009145677