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We consider cross-sectional data that exhibit no spatial correlation, but are feared to be spatially dependent. We demonstrate that a spatial version of the stochastic volatility model of financial econometrics, entailing a form of spatial autoregression, can explain such behaviour. The parameters are estimated by pseudo-Gaussian maximum likelihood based on log-transformed squares, and consistency and asymptotic normality are established. Asymptotically valid tests for…

平稳系统中的多参数局部 Whittle 估计AI 解读

Moving from univariate to bivariate jointly dependent long-memory time series introduces a phase parameter (γ), at the frequency of principal interest, zero; for short-memory series γ=0 automatically. The latter case has also been stressed under long memory, along with the “fractional differencing” case γ=(δ2−δ1)π/2, where δ1, δ2 are the memory parameters of the two series. We develop time domain conditions under which these are and are not relevant, and relate the…

Instrumental variables estimation is classically employed to avoid simultaneous equations bias in a stable environment. Here we use it to improve upon ordinary least-squares estimation of cointegrating regressions between non-stationary and/or long memory stationary variables where the integration orders of regressor and disturbance sum to less than 1, as happens always for stationary regressors, and sometimes for mean-reverting non-stationary ones. Unlike in the classical…

平稳与非平稳分数阶时间序列推断的效率改进AI 解读

We consider a time series model involving a fractional stochastic component, whose integration order can lie in the stationary/invertible or nonstationary regions and be unknown, and an additive deterministic component consisting of a generalized polynomial. The model can thus incorporate competing descriptions of trending behavior. The stationary input to the stochastic component has parametric autocorrelation, but innovation with distribution of unknown form. The model is…

积分阶数未知的分数系统中的协整AI 解读

Cointegrated bivariate nonstationary time series are considered in a fractional context, without allowance for deterministic trends. Both the observable series and the cointegrating error can be fractional processes. The familiar situation in which the respective integration orders are 1 and 0 is nested, but these values have typically been assumed known. We allow one or more of them to be unknown real values, in which case Robinson and Marinucci (2001, 2003) have justified…

The behavior of averaged periodograms and cross-periodograms of a broad class of nonstationary processes is studied. The processes include nonstationary ones that are fractional of any order, as well as asymptotically stationary fractional ones. The cross-periodogram can involve two nonstationary processes of possibly different orders, or a nonstationary and an asymptotically stationary one. The averaging takes place either over the whole frequency band, or over one that…

极点频率未知时参数谱密度的高斯估计AI 解读

We consider a parametric spectral density with power-law behavior about a fractional pole at the unknown frequency $\omega$. The case of known $\omega$, especially $\omega =0$, is standard in the long memory literature. When $omega$ is unknown, asymptotic distribution theory for estimates of parameters, including the (long) memory parameter, is significantly harder. We study a form of Gaussian estimate. We establish $n$-consistency of the estimate of $\omega$, and discuss…

I(1) 过程统计推断的有限样本改进AI 解读

Abstract Robinson and Marinucci ( 1998 ) investigated the asymptotic behaviour of a narrow‐band semiparametric procedure termed Frequency Domain Least Squares (FDLS) in the broad context of fractional cointegration analysis. Here we restrict discussion to the standard case when the data are I(1) and the cointegrating errors are I(0), proving that modifications of the Fully Modified Ordinary Least Squares (FM‐OLS) procedure of Phillips and Hansen ( 1990 ) which use the FDLS…

英国与日本消费和收入的季节分数阶单整检验AI 解读

Abstract The seasonal structure of quarterly UK and Japanese consumption and income is examined by means of fractionally based tests proposed by Robinson ( 1994 ). These series were analysed from an autoregressive unit root viewpoint by Hylleberg, Engle, Granger and Yoo (HEGY, 1990 ) and Hylleberg, Engle, Granger and Lee (HEGL, 1993 ). We find that seasonal fractional integration, with amplitudes possibly varying across frequencies, is an alternative plausible way of…

半参数平均导数估计的 Edgeworth 展开AI 解读

A valid Edgeworth expansion is established for the limit distribution of density-weighted semiparametric averaged derivative estimates of single index models. The leading term that corrects the normal limit varies in magnitude, depending on the choice of bandwidth and kernel order. In general this term has order larger than the n−1/2 that prevails in standard parametric problems, but we find circumstances in which it is O(n−1/2), thereby extending the achievement of an n−1/2…

存在非参数自相关时的无平滑推断AI 解读

The author gives conditions under which nonparametric autocorrelation-consistent variance estimation is possible without smoothing. The conditions are relevant to inference on slope parameters in models with an intercept and strictly exogenous regressors, and allow regressors and disturbances to collectively have considerable stationary long memory and to satisfy only mild, in some cases minimal, moment conditions. His estimate dominates smoothed ones in the sense that it…

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相关误差下非参数回归的大样本推断AI 解读

A central limit theorem is given for certain weighted partial sums of a covariance stationary process, assuming it is linear in martingale differences, but without any restriction on its spectrum. We apply the result to kernel nonparametric fixed-design regression, giving a single central limit theorem which indicates how error spectral behavior at only zero frequency influences the asymptotic distribution and covers long-range, short-range and negative dependence. We show…

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具有长程依赖的时间序列回归AI 解读

A central limit theorem is established for time series regression estimates which include generalized least squares, in the presence of long-range dependence in both errors and stochastic regressors. The setting and results differ significantly from earlier work on regression withlong-range-dependent errors. Spectral singularities are permitted at any frequency. When sufficiently strong spectral singularities in the error and a regressor coincide at the same frequency, least…

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…

长程依赖时间序列的对数周期图回归AI 解读

This paper discusses the estimation of multiple time series models which allow elements of the spectral density matrix to tend to infinity or zero at zero frequency and be unrestricted elsewhere. A form of log-periodogram regression estimate of differencing and scale parameters is proposed, which can provide modest efficiency improvements over a previously proposed method (for which no satisfactory theoretical justification seems previously available) and further…

半参数平均导数估计量的正态近似AI 解读

With the same normalization as that for standard parametric statistics, and centered at a parameter of interest, many semiparametric estimates based on n observations have been shown to be root-n-consistent and asymptotically normal. In the context of semiparametric averaged derivative estimates, the author goes further by showing that the rate of convergence of the finite-sample distribution to the normal limit distribution can equal that of standard parametric statistics.…

非平稳假设的高效检验AI 解读

This article proposes tests for unit root and other forms of nonstationarity that are asymptotically locally most powerful against a certain class of alternatives and have asymptotic critical values given by the chi-squared distribution. Many existing unit root tests do not share these properties. The alternatives include fractionally and seasonally fractionally differenced processes. There is considerable flexibility in our choice of null hypothesis, which can entail one or…

长记忆时间序列的半参数分析AI 解读

We study problems of semiparametric statistical inference connected with long-memory covariance stationary time series, having spectrum which varies regularly at the origin: There is an unknown self-similarity parameter, but elsewhere the spectrum satisfies no parametric or smoothness conditions, it need not be in $L_p$, for any $p > 1$, and in some circumstances the slowly varying factor can be of unknown form. The basic statistic of interest is the discretely averaged…

极度不显著的 F 比率AI 解读

OCCASIONALLY IN EMPIRICAL WORK, a highly insignificant test statistic is encountered. A rule that the null hypothesis not be rejected when the value of a statistic falls below a threshold which is given by the tester's desired Type I error probability 8 < implies a fortiori that the null is not rejected when the probability that the statistic is smaller than the realized value is less than 8. We suggest an interpretation of this outcome in time series problems. For ease of…

半参数与非参数模型的自动频域推断AI 解读

The author considers frequency domain time series analysis, where smoothing in nonparametric spectrum estimation is data-dependent. Uniform convergence of spectrum estimates is established and applied to a semiparametric model, parameterized over possibly only a subset of the frequencies, in which disturbances have nonparametric autocorrelation. Optimal instruments depend on the disturbance spectrum and frequency response function, which is nonparametric in incomplete…

若干计量模型的最优非线性三阶段最小二乘估计AI 解读

A method is presented for estimating nonlinear simultaneous equations and transformation models in the presence of disturbance distribution of unknown form. It asymptotically achieves the lower variance bound for instrumental variables estimates. The author avoids smoothed nonparametric estimation, his instruments averaging over the unsmoothed empirical distribution of preliminary residuals. He allows for stationary serial dependence. In various settings, estimates are…