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Testing Dependence Among Serially Correlated Multicategory Variables

具有序列相关的多类别变量之间的依赖检验

The contingency table literature on tests for dependence among discrete multicategory variables is extensive. Standard tests assume, however, that draws are independent and only limited results exist on the effect of serial dependency—a problem that is important in areas such as economics, finance, medical trials, and meteorology. This article proposes new tests of independence based on canonical correlations from dynamically augmented reduced rank regressions. The tests…

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Causality and forecasting in temporally aggregated multivariate GARCH processes

时间聚合后多元GARCH过程的因果关系与预测

This paper discusses the effects of temporal aggregation on causality and forecasting in multivariate GARCH processes. It is shown that spurious instantaneous causality in variance will only appear in degenerated cases, but that spurious Granger causality will be more common. For forecasting volatility, it is generally advisable to aggregate forecasts of the disaggregate series rather than forecasting the aggregated series directly, and unlike for vector autoregressive…

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A note on the stationary bootstrap’s variance

平稳自助法方差札记

Because the stationary bootstrap resamples data blocks of random length, this method has been thought to have the largest asymptotic variance among block bootstraps Lahiri [Ann. Statist. 27 (1999) 386–404]. It is shown here that the variance of the stationary bootstrap surprisingly matches that of a block bootstrap based on nonrandom, nonoverlapping blocks. This argument translates the variance expansion into the frequency domain and provides a unified way of determining…

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Consistencies and rates of convergence of jump-penalized least squares estimators

跳跃惩罚最小二乘估计的一致性与收敛速度

We study the asymptotics for jump-penalized least squares regression aiming at approximating a regression function by piecewise constant functions. Besides conventional consistency and convergence rates of the estimates in L2([0, 1)) our results cover other metrics like Skorokhod metric on the space of càdlàg functions and uniform metrics on C([0, 1]). We will show that these estimators are in an adaptive sense rate optimal over certain classes of “approximation spaces.”…

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Inference for the limiting cluster size distribution of extreme values

极值极限簇大小分布的推断

Any limiting point process for the time normalized exceedances of high levels by a stationary sequence is necessarily compound Poisson under appropriate long range dependence conditions. Typically exceedances appear in clusters. The underlying Poisson points represent the cluster positions and the multiplicities correspond to the cluster sizes. In the present paper we introduce estimators of the limiting cluster size probabilities, which are constructed through a recursive…

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The finite-sample effects of VAR dimensions on OLS bias, OLS variance, and minimum MSE estimators

VAR 维度对 OLS 偏差、方差与最小均方误差估计的有限样本影响

Vector autoregressions (VARs) are important tools in time series analysis. However, relatively little is known about the finite-sample behaviour of parameter estimators. We address this issue, by investigating ordinary least squares (OLS) estimators given a data generating process that is a purely nonstationary first-order VAR. Specifically, we use Monte Carlo simulation and numerical optimisation to derive response surfaces for OLS bias and variance, in terms of VAR…

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Functional-coefficient models for nonstationary time series data

非平稳时间序列的函数系数模型

This paper studies functional coefficient regression models with nonstationary time series data, allowing also for stationary covariates. A local linear fitting scheme is developed to estimate the coefficient functions. The asymptotic distributions of the estimators are obtained, showing different convergence rates for the stationary and nonstationary covariates. A two-stage approach is proposed to achieve estimation optimality in the sense of minimizing the asymptotic mean…

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BOOTSTRAPPING SYSTEMS COINTEGRATION TESTS WITH A PRIOR ADJUSTMENT FOR DETERMINISTIC TERMS

预先调整确定性项的系统协整检验自助法

In this paper we analyze bootstrap procedures for systems cointegration tests with a prior adjustment for deterministic terms suggested by Saikkonen et al. (2006, Econometric Theory 22, 15–68) and Saikkonen and Lütkepohl (2000, Journal of Time Series Analysis 21, 435–456). The asymptotic properties of the bootstrap test procedures are derived, and their small-sample properties are studied. The simulation study also considers the standard asymptotic test versions and the…

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A GENERALIZED PORTMANTEAU TEST FOR INDEPENDENCE BETWEEN TWO STATIONARY TIME SERIES

两个平稳时间序列独立性的广义综合检验

We propose generalized portmanteau-type test statistics in the frequency domain to test independence between two stationary time series. The test statistics are formed analogous to the one in the paper by Chen and Deo (2004, Econometric Theory 20, 382–416), who extended the applicability of the portmanteau goodness-of-fit test to the long memory case. Under the null hypothesis of independence, the asymptotic standard normal distributions of the proposed statistics are…

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ON THE LACK OF POWER OF OMNIBUS SPECIFICATION TESTS

综合设定检验局部功效不足的问题

Designed to have power against all alternatives, omnibus consistent tests are the primary econometric tools for testing the correct specification of parametric conditional means when there is no information about the possible alternative. The main purpose of this paper is to show that, contrary to what is generally believed, omnibus specification tests only have substantial local power against alternatives in a finite-dimensional space (usually unknown to the researcher). We…

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COVARIANCE-BASED ORTHOGONALITY TESTS FOR REGRESSORS WITH UNKNOWN PERSISTENCE

回归变量持续性未知时的协方差正交性检验

This paper develops a new test of orthogonality based on a zero restriction on the covariance between the dependent variable and the predictor. The test provides a useful alternative to regression-based tests when conditioning variables have roots close or equal to unity. In this case standard predictive regression tests can suffer from well-documented size distortion. Moreover, under the alternative hypothesis, they force the dependent variable to share the same order of…

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ON MARKOV-SWITCHING ARMA PROCESSES—STATIONARITY, EXISTENCE OF MOMENTS, AND GEOMETRIC ERGODICITY

马尔可夫转换 ARMA 过程的平稳性、矩存在与几何遍历性

The probabilistic properties of ℝ d -valued Markov-switching autoregressive moving average (ARMA) processes with a general state space parameter chain are analyzed. Stationarity and ergodicity conditions are given, and an easy-to-check general sufficient stationarity condition based on a tailor-made norm is introduced. Moreover, it is shown that causality of all individual regimes is neither a necessary nor a sufficient criterion for strict negativity of the associated…

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Confidence intervals for spectral mean and ratio statistics

谱均值及比率统计量的置信区间

We propose a new method, to construct confidence intervals for spectral mean and related ratio statistics of a stationary process, that avoids direct estimation of their asymptotic variances. By introducing a bandwidth, a self-normalization procedure is adopted and the distribution of the new statistic is asymptotically nuisance-parameter free. The bandwidth is chosen using information criteria and a moving average sieve approximation. Through a simulation study, we…

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Tapered empirical likelihood for time series data in time and frequency domains

时域与频域中时间序列数据的渐消加权经验似然

We investigate data tapering in two formulations of empirical likelihood for time series. One empirical likelihood is formed from tapered data blocks in the time domain and a second is based on the tapered periodogram in the frequency domain. Limiting distributions are provided for both empirical likelihood versions under tapering. Theoretical and simulation evidence indicates that a data taper improves the coverage accuracy of empirical likelihood confidence intervals for…

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Studying co-movements in large multivariate data prior to multivariate modelling

多变量建模前的大规模数据共同运动分析

For non-stationary vector autoregressive models (VAR hereafter, or VAR with moving average, VARMA hereafter), we show that the presence of common cyclical features or cointegration leads to a reduction of the order of the implied univariate autoregressive-integrated-moving average (ARIMA hereafter) models. This finding can explain why we identify parsimonious univariate ARIMA models in applied research although VAR models of typical order and dimension used in…

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Markov-switching models with endogenous explanatory variables II: A two-step MLE procedure

含内生解释变量的马尔可夫转换模型Ⅱ:两步极大似然估计

This paper proposes a two-step maximum likelihood estimation (MLE) procedure to deal with the problem of endogeneity in Markov-switching regression models. A joint estimation procedure provides us with an asymptotically most efficient estimator, but it is not always feasible, due to the 'curse of dimensionality' in the matrix of transition probabilities. A two-step estimation procedure, which ignores potential correlation between the latent state variables, suffers less from…

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Tests for changing mean with monotonic power

具有单调功效的均值变化检验

Several widely used tests for a changing mean exhibit nonmonotonic power in finite samples, due to "incorrect"Â estimation of nuisance parameters under the alternative. In this paper, we study the issue of nonmonotonic power in testing for changing mean. We investigate the asymptotic power properties of the tests, using a new framework where alternatives are characterized as having "large" changes. The asymptotic analysis provides a theoretical explanation to the power…

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Unit root tests allowing for a break in the trend function at an unknown time under both the null and alternative hypotheses

零假设与备择下均允许未知时点趋势突变的单位根检验

Perron [Perron, P., 1989. The great crash, the oil price shock and the unit root hypothesis. Econometrica 57, 1361-1401] introduced a variety of unit root tests that are valid when a break in the trend function of a time series is present. The motivation was to devise testing procedures that were invariant to the magnitude of the shift in level and/or slope. In particular, if a change is present it is allowed under both the null and alternative hypotheses. This analysis was…

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Valid Inference in Partially Unstable Generalized Method of Moments Models

部分不稳定广义矩模型中的有效推断

This paper considers time series Generalized Method of Moments (GMM) models where a subset of the parameters are time varying. We focus on an empirically relevant case with moderately large instabilities, which are well approximated by a local asymptotic embedding that does not allow the instability to be detected with certainty, even in the limit. We show that for many forms of the instability and a large class of GMM models, usual GMM inference on the subset of stable…

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How Useful are Historical Data for Forecasting the Long-Run Equity Return Distribution?

历史数据对预测股票收益长期分布有多大用处?

We provide an approach to forecasting the long-run (unconditional) distribution of equity returns making optimal use of historical data in the presence of structural breaks. Our focus is on learning about breaks in real time and assessing their impact on out-of-sample density forecasts. Forecasts use a probability-weighted average of submodels, each of which is estimated over a different history of data. The empirical results strongly reject ignoring structural change or…

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Comparing the Point Predictions and Subjective Probability Distributions of Professional Forecasters

专业预测者点预测与主观概率分布的比较

We use data from the Survey of Professional Forecasters (SPF) to compare point predictions of gross domestic product (GDP) growth and inflation with the subjective probability distributions held by forecasters. We find that most SPF point predictions are quite close to the central tendencies of forecasters subjective distributions tend to be asymmetric, with SPF forecasters tending to report point predictions that give a more favorable view of the economy than do their…

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International output convergence: evidence from an autocorrelation function approach

国际产出趋同:来自自相关函数方法的证据

Abstract This paper uses an autocorrelation function (ACF) approach to develop a new testing procedure for international output convergence. We define convergence in terms of sample ACFs of detrended output per capita, and construct an inference set‐up based on resampling and subsampling techniques for dependent data. Using per capita GDP for 15 OECD countries observed over a century, we find that the hypothesis of conditional convergence is unsupported; that, the USA apart,…

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Market fundamentals versus rational bubbles in stock prices: a Bayesian perspective

股票价格中的市场基本面与理性泡沫:贝叶斯视角

Abstract Using Bayesian Markov chain Monte Carlo methods, we decompose the log price‐dividend ratio into a market fundamentals component and a bubble component. The market fundamentals component depends on expectations of future dividend growth and required returns, while the bubble component is assumed to follow a Markov switching model that allows for the possibility of exploding and collapsing regimes. If prior beliefs allow for the possibility of persistent shocks to…

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Finite-sample distribution-free inference in linear median regressions under heteroscedasticity and non-linear dependence of unknown form

未知异方差与非线性依赖下线性中位数回归的有限样本无分布推断

We construct finite-sample distribution-free tests and confidence sets for the parameters of a linear median regression, where no parametric assumption is imposed on the noise distribution. The set-up studied allows for non-normality, heteroscedasticity, non-linear serial dependence of unknown forms as well as for discrete distributions. We consider a mediangale structure--the median-based analogue of a martingale difference--and show that the signs of mediangale sequences…

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