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远期外汇市场效率的稳健检验：来自20世纪20年代的证据AI 解读

This paper provides a robust statistical approach to testing the unbiasedness hypothesis in forward exchange market efficiency studies. The methods we use allow us to work explicitly with levels rather than differenced data. They are statistically robust to data distributions with heavy tails, and they can be applied to data sets where the frequency of observation and the futures maturity do not coincide. In addition, our methods allow for stochastic trend non-stationarity…

用分数单整 ARFIMA–GARCH 模型分析通货膨胀AI 解读

This paper considers the application of long-memory processes to describing inflation for 10 countries. We implement a new procedure to obtain approximate maximum likelihood estimates of an ARFIMA-GARCH process; which is fractionally integrated I(d) with a superimposed stationary ARMA component in its conditional mean. Additionally, this long-memory process is allowed to have GARCH type conditional heteroscedasticity. On analysing monthly post-World War II CPI inflation for…

依赖数据密度估计中的带宽选择AI 解读

We address the empirical bandwidth choice problem in cases where the range of dependence may be virtually arbitrarily long. Assuming that the observed data derive from an unknown function of a Gaussian process, it is argued that, unlike more traditional contexts of statistical inference, in density estimation there is no clear role for the classical distinction between short- and long-range dependence. Indeed, the "boundaries" that separate different modes of behaviour for…

短程与长程相关误差下非参数回归的带宽选择AI 解读

We analyse methods based on the block bootstrap and leave-out cross-validation, for choosing the bandwidth in nonparametric regression when errors have an almost arbitrarily long range of dependence. A novel analytical device for modelling the dependence structure of errors is introduced. This allows a concise theoretical description of the way in which the range of dependence affects optimal bandwidth choice. It is shown that, provided block length or leave-out number,…

正值创新一阶自回归模型的 Bootstrap 推断AI 解读

In this article we consider statistical inference for the autoregressive parameter of a first-order autoregressive sequence with positive innovations via an extreme value estimator ϕ. We show that a bootstrap procedure correctly estimates the sampling distribution of an asymptotically pivotal quantity (whose distribution depends only on the exponent of regular variation of the innovation distribution) based on ϕ, provided that the ratio of the bootstrap sample size m and the…

周期成分动态线性模型的贝叶斯推断AI 解读

Dynamic linear models (DLM's) with time-varying cyclical components are developed for the analysis of time series with persistent though time-varying cyclical behavior. The development covers inference on wavelengths of possibly several persistent cycles in nonstationary time series, permitting explicit time variation in amplitudes and phases of component waveforms, decomposition of stochastic inputs into purely observational noise and innovations that impact on the waveform…

周期成分动态线性模型中的贝叶斯推断AI 解读

Dynamic linear models (DLM's) with time-varying cyclical components are developed for the analysis of time series with persistent though time-varying cyclical behavior. The development covers inference on wavelengths of possibly several persistent cycles in nonstationary time series, permitting explicit time variation in amplitudes and phases of component waveforms, decomposition of stochastic inputs into purely observational noise and innovations that impact on the waveform…

正创新一阶自回归模型的自助法推断AI 解读

In this article we consider statistical inference for the autoregressive parameter of a first-order autoregressive sequence with positive innovations via an extreme value estimator ϕ. We show that a bootstrap procedure correctly estimates the sampling distribution of an asymptotically pivotal quantity (whose distribution depends only on the exponent of regular variation of the innovation distribution) based on ϕ, provided that the ratio of the bootstrap sample size m and the…

可逆短记忆与长记忆 ARIMA 模型差分参数的最大似然估计AI 解读

SUMMARY In practical applications of Box-Jenkins autoregressive integrated moving average (ARIMA) models, the number of times that the observed time series must be differenced to achieve approximate stationarity is usually determined by careful, but mostly informal, analysis of the differenced series. For many time series, some differencing seems appropriate, but taking the first or the second difference may be too strong. As an alternative, Hosking, and Granger and Joyeux…

基于周期图的分形性质估计量AI 解读

We suggest an estimator, based on the periodogram, of the fractal index and fractal dimension of a continuous, stationary Gaussian process. We argue that the cosine part of the periodogram is more appropriate than the full periodogram for this application. The term "semiperiodogram" is used to describe the cosine component, and our estimator is based on simple linear regression of the logarithm of the semiperiodogram on the algorithm of frequency. Theoretical properties of…

不稳定长记忆过程的推断及其在分数单位根自回归中的应用AI 解读

An autoregressive time series is said to be unstable if all of its characteristic roots lie on or outside the unit circle, with at least one on the unit circle. This paper aims at developing asymptotic inferential schemes for an unstable autoregressive model generated by long-memory innovations. This setting allows both nonstationarity and long-memory behavior in the modeling of low-frequency phenomena. In developing these procedures, a novel weak convergence result for a…

长程依赖的高斯半参数估计AI 解读

Assuming the model $f(\lambda) \sim G\lambda^{1-2H}$, as $\lambda \rightarrow 0 +$, for the spectral density of a covariance stationary process, we consider an estimate of $H \in (0, 1)$ which maximizes an approximate form of frequency domain Gaussian likelihood, where discrete averaging is carried out over a neighbourhood of zero frequency which degenerates slowly to zero as sample size tends to infinity. The estimate has several advantages. It is shown to be consistent…

伦敦证券交易所隔夜与日间股票收益：“大爆炸”改革与1987年股灾的影响AI 解读

We explore the time series properties of stock returns on the London Stock Exchange around the 1986 market restructuring (Big Bang) and the 1987 stock-market crash using a modified generalized autoregressive conditional heteroscedasticity model. Using this general dynamic model, which allows (a) intradaily returns to have different impacts and persistence on stock-return volatility, (b) return effects on volatility to be asymmetric, and (c) intradaily returns to follow…

随机游走、趋势函数断点与货币流通速度的混沌结构AI 解读

This paper examines the times-series properties of U.S. velocity series, using E. Zivot and D. W. K. Andrews's (1992) variation of P. Perron's (1989) test. It also tests for deterministic noisy chaos using the Nychka, Ellner, Gallant, and McCaffrey (1992) nonparametric test for positivity of the maximum Lyapunov exponent. Comparisons are made among simple sum and Divisia aggregates using the Thornton and Yue (1992) series of Divisia monetary aggregates for an extended sample…

时间聚合与经济时间序列AI 解读

The authors examine the effects of temporal aggregation on the estimated time-series properties of economic data. Theory predicts temporal aggregation loses information about the underlying data processes. The authors find those losses to be substantial. Monthly and quarterly data are governed by complex time-series processes with much low-frequency cyclical variation, whereas annual data are governed by extremely simple processes with virtually no cyclical variation. Cycles…

经济冲击持续性的不确定性AI 解读

The behavior of time series of economic output has been characterized in terms of the persistence of shocks. For a series generated by an autoregressive integrated moving average model, persistence is a function of the model parameters. This paper demonstrates that a careful analysis, based on the likelihood, can lead to considerably more uncertainty about persistence than is implied by the confidence intervals that are usually calculated. The estimation of persistence is…

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近单位根解释变量模型中的推断AI 解读

This paper examines regression tests of whether x forecasts y when the largest autoregressive root of the regressor is unknown. It is shown that previously proposed two-step procedures, with first stages that consistently classify x as I(1) or I(0), exhibit large size distortions when regressors have local-to-unit roots, because of asymptotic dependence on a nuisance parameter that cannot be estimated consistently. Several alternative procedures, based on Bonferroni and…

混合单整过程的时间序列回归AI 解读

The paper develops a statistical theory for regressions with integrated regressors of unknown order and unknown cointegrating dimension. In practice, we are often unsure whether unit roots or cointegration is present in time series data, and we are also uncertain about the order of integration in some cases. This paper addresses issues of estimation and inference in cases of such uncertainty. Phillips (1995, Econometrica 63, 1023–1078) developed a theory for time series…

向量自回归中协整秩似然比检验的有限样本表现AI 解读

This paper investigates through Monte Carlo simulation the finite sample properties of likelihood ratio tests for cointegrating ranks that were proposed by Johansen (1991, Econometrica 59, 1551–1580). We transform the model into a canonical form so that the experiment is well controlled without loss of generality and then conduct a comprehensive simulation study. As expected, the test performance is very sensitive to the value of the stationary root(s) of the process. We…

方程系统中的协整检验AI 解读

This paper introduces various consistent tests for the null of cointegration against the alternative of noncointegration that can be applied to a system of equations as well as to a single equation. The tests are analogs of Choi and Ahn's (1993, Testing the Null of Stationarity for Multiple Time Series, working paper, The Ohio State University) multivariate tests for the null of stationarity and use Park's (1992, Econometrica 60, 119–143) canonical cointegrating regression…

稳健非平稳回归AI 解读

This paper provides a robust statistical approach to nonstationary time series regression and inference. Fully modified extensions of traditional robust statistical procedures are developed that allow for endogeneities in the nonstationary regressors and serial dependence in the shocks that drive the regressors and the errors that appear in the equation being estimated. The suggested estimators involve semiparametric corrections to accommodate these possibilities, and they…

渐近理论的若干方面及其在时间序列模型中的应用AI 解读

The primary purpose of this paper is to review a very few results on some basic elements of large sample theory in a restricted structural framework, as described in detail in the recent book by LeCam and Yang (1990, Asymptotics in Statistics: Some Basic Concepts . New York: Springer), and to illustrate how the asymptotic inference problems associated with a wide variety of time series regression models fit into such a structural framework. The models illustrated include…

重新思考单变量单位根检验：利用协变量提高功效AI 解读

In the context of testing for a unit root in a univariate time series, the convention is to ignore information in related time series. This paper shows that this convention is quite costly, as large power gains can be achieved by including correlated stationary covariates in the regression equation. The paper derives the asymptotic distribution of ordinary least-squares estimates of the largest autoregressive root and its t-statistic. The asymptotic distribution is not the…