• East Asian Economic Review
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• v.23 no.4
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• pp.333-351
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• 2019

We use a data-mining bootstrap procedure to investigate the predictability test in the eight Asia-Pacific regional stock markets using in-sample and out-of-sample forecasting models. We address ourselves to the data-mining bias issues by using the data-mining bootstrap procedure proposed by Inoue and Kilian and applied to the US stock market data by Rapach and Wohar. The empirical findings show that stock returns are predictable not only in-sample but out-of-sample in Hong Kong, Malaysia, Singapore, and Korea with a few exceptions for some forecasting horizons. However, we find some significant disparity between in-sample and out-of-sample predictability in the Korean stock market. For Hong Kong, Malaysia, and Singapore, stock returns have predictable components both in-sample and out-of-sample. For the US, Australia, and Canada, we do not find any evidence of return predictability in-sample and out-of-sample with a few exceptions. For Japan, stock returns have a predictable component with price-earnings ratio as a forecasting variable for some out-of-sample forecasting horizons.

• Communications for Statistical Applications and Methods
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• v.22 no.5
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• pp.463-473
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• 2015

We consider a nonparametric AR(1) model with nonparametric ARCH(1) errors. In order to estimate the unknown function of the ARCH part, we apply the stationary bootstrap procedure, which is characterized by geometrically distributed random length of bootstrap blocks and has the advantage of capturing the dependence structure of the original data. The proposed method is composed of four steps: the first step estimates the AR part by a typical kernel smoothing to calculate AR residuals, the second step estimates the ARCH part via the Nadaraya-Watson kernel from the AR residuals to compute ARCH residuals, the third step applies the stationary bootstrap procedure to the ARCH residuals, and the fourth step defines the stationary bootstrapped Nadaraya-Watson estimator for the ARCH function with the stationary bootstrapped residuals. We prove the asymptotic validity of the stationary bootstrap estimator for the unknown ARCH function by showing the same limiting distribution as the Nadaraya-Watson estimator in the second step.

• Communications for Statistical Applications and Methods
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• v.20 no.1
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• pp.85-96
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• 2013

A new bootstrap method combined with the stationary bootstrap of Politis and Romano (1994) and the classical residual-based bootstrap is applied to stationary autoregressive (AR) time series models. A stationary bootstrap procedure is implemented for the ordinary least squares estimator (OLSE), along with classical bootstrap residuals for estimated errors, and its large sample validity is proved. A finite sample study numerically compares the proposed bootstrap estimator with the estimator based on the classical residual-based bootstrapping. The study shows that the proposed bootstrapping is more effective in estimating the AR coefficients than the residual-based bootstrapping.

• Proceedings of the Korea Association of Information Systems Conference
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• 1996.11a
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• pp.205-210
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• 1996

The bootstrap procedure is suggested as a useful method for point and interval estimation of system availability. Its validity and robustness has been shown in special, but representative case, by various sampling experiments. Alternative to the bootstrap suggest themselves e.g. a variation of the 'F'technique, but remain to be evaluated, as do variations on the bootstrap itself.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 1996.10a
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• pp.205-210
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• 1996

The bootstrap procedure is suggested as a useful method for point and interval estimation of system availability . Its validity and robustness has been shown in special , but representative case, by various sampling experiments. Alternative to the bootstrap suggest themselves (e.g. a variation of the 'F' technique, but remain to be evaluated, as do variations on the bootstrap itself.

• Journal of the Korean Statistical Society
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• v.22 no.2
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• pp.361-368
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• 1993

In recent years, kernel type estimates are abundant. In this paper, we propose a bandwidth selection method for kernel regression of fixed design based on bootstrap procedure. Mathematical properties of proposed bootstrap-based bandwidth selection method are discussed. Performance of the proposed method for small sample case is compared with that of cross-validation method via a simulation study.

• Communications for Statistical Applications and Methods
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• v.15 no.5
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• pp.643-653
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• 2008

Application of the bootstrap to problems in multistage inference procedures are discussed in normal and other related models. After a general introduction to these procedures, here we explore in multistage fixed precision inference in models. We present numerical comparisons of these procedures based on bootstrap critical points for small and moderate sample sizes obtained via extensive sets of simulated experiments. It is expected that the procedure based on bootstrap leads to better results.

• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.263-273
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• 1998

In this paper, we consider some approximate testings for the reliability of the stress-strength model when the stress X and strength Y each depends linearly on some explanatory variables z and w, respectively. We construct a bootstrap procedure for testing for various values of the reliability and compare the power of the bootstrap test with the test based on Mann-Whitney type estimator by Park et.al.(1996) for small and moderate sample size.

• Journal of the Korean Statistical Society
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• v.29 no.4
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• pp.423-433
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• 2000

In this paper, we consider applying the bootstrap method to the median test procedures for right censored data. For doing this, we show that the median test statistics can be represented by the differences of two sampler medians. Then we review to the re-sampling methods for censored dta and propose the test procedures under the location translation assumption and Behrens-Fisher problem. Also we compare our procedures with other re-sampling method, which is so-called permutation test through an example. Finally we show the validity of bootstrap median test procedure in the appendix.

• Communications for Statistical Applications and Methods
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• v.12 no.2
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• pp.521-529
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• 2005

In this paper, we consider a bootstrap method to the 'row and column effects model' (RC model) to analyze a contingency table with ordered variables. We propose a bootstrap procedure for testing of independence, equality of intervals, and goodness of fit in the RC model. A real data example is included.