Taehwan Kim

We present the asymptotic properties of double‐stage quantile regression estimators with random regressors, where the first stage is based on quantile regressions with the same quantile as in the second stage, which ensures robustness of the estimation procedure. We derive invariance properties with respect to the reformulation of the dependent variable. We propose a consistent estimator of the variance–covariance matrix of the new estimator. Finally, we investigate finite sample properties of t

To date, the literature on quantile regression and least absolute deviation regression has assumed either explicitly or implicitly that the conditional quantile regression model is correctly specified. When the model is misspecified, confidence intervals and hypothesis tests based on the conventional covariance matrix are invalid. Although misspecification is a generic phenomenon and correct specification is rare in reality, there has to date been no theory proposed for inference when a conditio

For both the academic and the financial communities it is a familiar stylized fact that stock market returns have negative skewness and excess kurtosis. This stylized fact has been supported by a vast collection of empirical studies. Given that the conventional measures of skewness and kurtosis are computed as an average and that averages are not robust, we ask, "How useful are the measures of skewness and kurtosis used in previous empirical studies?" To answer this question we provide a survey

This paper offers a new approach that estimates the response of interest rates to inflation and the output gap at various points (quantiles) on the conditional distribution of interest rates. This offers an improvement on empirical estimates conducted only at the mean and also allows us to test the propositions that policy shows greater aggression to inflation in the reaction function in terms of a greater response coefficient as interest rates reach low levels, and increasing aggression as the

This study considers the possibility of estimating a Dickey-Fuller regression, constraining the autoregressive parameter to be at most one, and imposing prior knowledge of the sign of the drift parameter. In spite of apparently encouraging asymptotic results, it emerges that no feasible test of the unit root null hypothesis with superior finite sample properties follows from such inequality-restricted estimation.

In this paper, we concentrate on the case of an exogeneously chosen break date, but entertain the possibility that an incorrect choice is made. In fact, the Perron test statistics considered are invariant to any break in the generating process at the assumed break date. Our results therefore apply equally to the case of a generating process with two breaks, only one of which is specifically accounted for in the analysis. As in Leybourne et al . (1998), we find that a neglected relatively early b

Abstract. We analyse the case where a unit‐root test is based on a Dickey–Fuller regression the only deterministic term of which is a fixed intercept. Suppose, however, as could well be the case, that the actual data‐generating process includes a broken linear trend. It is shown theoretically, and verified empirically, that under the I (1) null and I (0) alternative hypotheses the Dickey–Fuller test can display a wide range of different characteristics depending on the nature and location of the

As the demands for implementing High Performance Computing (HPC) increase rapidly, the bandwidth and capacity required for High Bandwidth Memory (HBM) are expected to increase by two to three times per generation. Owing to these increased requirements, the power of the next HBM is expected to exceed 30W, and the number of stacks of the HBM leads to a high-level stack demand of 12 and beyond, which also increases the physical thermal resistance of the HBM. Therefore, it is inevitable to strive to