Titlebook: Subsampling; Dimitris N. Politis,Joseph P. Romano,Michael Wolf Textbook 1999 Springer Science+Business Media New York 1999 Resampling.Subs

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Subsampling Stock Returnsempirical support for the use of the current dividend-price ratio, or dividend yield, as a measure of expected stock returns (see, for example, Rozeff, 1984; Campbell and Shiller, 1988b; Fama and French, 1988; Hodrick, 1992; and Nelson and Kim, 1993). The problem with such studies is that stock retu

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Subsampling for Random Fields of real numbers ℝ, or the set of integers ℤ; thus, the random field {X(t)} is allowed to “run” in either continuous or discrete “time.” Similarly, G. will denote ℝ. or ℤ. (the sets of positive real numbers and integers, respectively) according to whether G = ℝ or G = ℤ.

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Confidence Sets for General Parametersreason for considering such generality is to be able to consider the case when the parameter of interest is an unknown function, such as the marginal distribution of the process or the spectral distribution function of the process. Here, we need to extend the arguments of previous chapters to cover the more general case.

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Subsampling with Unknown Convergence Rate tends to infinity, where . and . are events in the σ-algebras generated by ., t < 0} and ., t ≥ .}, respectively; the case where .,…,. are independent, identically distributed (i.i.d.) is an important special case of the general scenario.

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0172-7397 hout imposing unrealistic or unverifiable assumptions about the data generating mechanism. The primary goal of this book is to lay some of the foundation for subsampling methodology and related methods.978-1-4612-7190-1978-1-4612-1554-7Series ISSN 0172-7397 Series E-ISSN 2197-568X

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Subsampling for Nonstationary Time Seriese for stationary observations. Many of the results derived under stationarity will be restated and reproven under weaker conditions. Much of what is presented is taken from Politis, Romano, and Wolf (1997).