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时间序列分析在天文学和气象学中的应用AI 解读

Part 1 Time series - theory and methodology: A short history of time series, M.B. Priestley Frequency estimation, E.J. Hannan et al Asymptotic expansion of estimators for diffusions with small noises, N. Yoshida Spatial-temporal spectral analysis with random sampling, I-Shang Chow, Ken-Shin Lii Detection of periodicities, M.B. Priestley Spectral and multivariate probability density estimation of continuous time stationary processes from randomly sampled data, Elias Masry…

随机波动率中的长记忆检测与估计AI 解读

We propose a new time series representation of persistence in conditional variance called a long memory stochastic volatility (LMSV) model. The LMSV model is constructed by incorporating an ARFIMA process in a standard stochastic volatility scheme. Strongly consistent estimators of the parameters of the model are obtained by maximizing the spectral approximation to the Gaussian likelihood. The finite sample properties of the spectral likelihood estimator are analyzed by…

非平稳 VAR 中脉冲响应与预测误差方差的渐近性质AI 解读

Estimated impulse responses and forecast error decompositions are shown to be inconsistent at long horizons in unrestricted VARs with some unit roots. Predictions from unrestricted VARs also do not converge to the optimal predictors over long forecast horizons. In contrast, reduced rank regressions produce impulse responses and forecast error variance estimates that are consistent and predictions that are asymptotically optimal, provided the cointegrating rank is correctly…

理性预期、通货膨胀与名义利率AI 解读

There is a substantial empirical literature, beginning with Fama (1975), that utilizes regressions of the inflation rate in a given period on initial interest rates (or inflation differentials on the slope of the initial yield curve) to test the Fisher hypothesis and/or to provide forecasts of inflation. Both uses depend critically on the maintained hypothesis that asset market prices fully incorporate all relevant current information about future yields. This paper will…

少量理论约束下的商业周期分析：审视结构 VARAI 解读

This paper examines the usefulness of applying structural vector autoregressions (SVARs) to the study of business cycles. The SVAR approach aims to provide robust inferences, by imposing only weak theoretical restrictions. We illustrate that the robustness of conclusions drawn from SVAR exercises are questionable. We also examine the problem of identification failure in structural VAR models.

非平稳时间序列模型中后验密度的大样本性质、贝叶斯信息准则与似然原则AI 解读

Asymptotic normality of posterior is a well understood result for dynamic as well as non-dynamic models based on sets of abstract conditions that are hard to verify especially for the case of nonstationarity. In this paper the authors provide a set of conditions by which they can relatively easily prove the asymptotic posterior normality under quite general situations of possible nonstationarity. This result reinforces and generalizes the validity of inference based on the…

弱依赖数据下的筛极值估计AI 解读

Many non/semiparametric time series estimates may be regarded as different forms of sieve extremum estimates. For stationary absolute regular mixing observations, the authors obtain convergence rates of sieve extremurn estimates and root-n asymptotic normality of 'plug-in' sieve extremum estimates of smooth functionals. As applications to time series models, they give convergence rates for nonparametric ARX(p,q) regression via neural networks, splines, wavelets; root-n…

周期性状态转换模型的贝叶斯推断AI 解读

We present a general class of nonlinear time-series Markov regime-switching models for seasonal data which may exhibit periodic features in the hidden Markov process as well as in the laws of motion in each of the regimes. This class of models allows for non-trivial dependencies between seasonal, cyclical and long-term patterns in the data. To overcome the computational burden we adopt a Bayesian approach to estimation and inference. This paper contains two empirical…

含 GARCH 误差的不稳定 ARMA 时间序列最大似然估计量的极限分布AI 解读

This paper investigates the maximum likelihood estimator (MLE) for unstable autoregressive moving-average (ARMA) time series with the noise sequence satisfying a general autoregressive heteroscedastic (GARCH) process. Under some mild conditions, it is shown that the MLE satisfying the likelihood equation exists and is consistent. The limiting distribution of the MLE is derived in a unified manner for all types of characteristic roots on or outside the unit circle and is…

非平稳时间序列平滑的筛自助法AI 解读

We propose a sieve bootstrap procedure for time series with a deterministic trend. The sieve for constructing the bootstrap is based on nonparametric trend estimation and autoregressive approximation for some noise process. The bootstrap scheme itself does i.i.d. resampling of estimated innovations from fitted autoregressive models. We show the validity and indicate second-order correctness of such sieve bootstrap approximations for the limiting distribution of nonparametric…

局部平稳过程的自适应协方差估计AI 解读

It is shown that the covariance operator of a locally stationary process has approximate eigenvectors that are local cosine functions. We model locally stationary processes with pseudo-differential operators that are time-varying convolutions. An adaptive covariance estimation is calculated by searching first for a "best" local cosine basis which approximates the covariance by a band or a diagonal matrix. The estimation is obtained from regularized versions of the diagonal…

摘要暂缺

误差修正模型的自适应估计AI 解读

This paper considers adaptive maximum likelihood estimation of reduced rank vector error correction models. It is shown that such models can be asymptotically efficiently estimated even in the absence of knowledge of the shape of the density function of the innovation sequence, provided that this density is symmetric. The construction of the estimator, involving the nonparametric kernel estimation of the unknown density using the residuals of a consistent preliminary…

未知形式异方差下的自相关检验AI 解读

This paper develops a test of autocorrelation in the presence of heteroskedasticity of unknown form in the nonlinear regression model. The test statistic is based on the sample autocovariance of the residuals standardized by a nonparametric kernel estimate of the unknown heteroskedasticity function. Under appropriate conditions, the test statistic is shown to have a limiting chi-square distribution. Local power and consistency results for the test are also established. Monte…

一般协方差结构线性模型的残差AI 解读

Summary A general theory is presented for residuals from the general linear model with correlated errors. It is demonstrated that there are two fundamental types of residual associated with this model, referred to here as the marginal and the conditional residual. These measure respectively the distance to the global aspects of the model as represented by the expected value and the local aspects as represented by the conditional expected value. These residuals may be…

摘要暂缺

存在序列相关时的趋势函数假设检验AI 解读

Test statistics are proposed for testing hypotheses about the parameters of the deterministic trend function of a univariate time series. The tests are valid for general forms of serial correlation in the errors and do not require estimates (parametric or nonparametric) of serial correlation parameters. The tests are valid for stationary and unit root errors. Allowable trend functions include linear polynomials of time that may have structural change. Asymptotic results are…

一阶自回归分布滞后模型的精确推断方法AI 解读

Exact tests and confidence sets are obtained for general transformations of the coefficients in linear first-order autoregressive models with exogenous variables and i.i.d. disturbances. The tests proposed have known level and are either similar (constant rejection probability under all processes consistent with the null hypothesis) or use bounds which are free of nuisance parameters. Correspondingly, the confidence sets are either similar with known size or conservative.…

含多重结构变化的线性模型：估计与检验AI 解读

This paper develops the statistical theory for testing and estimating multiple change points in regression models. The rate of convergence and limiting distribution for the estimated parameters are obtained. Several test statistics are proposed to determine the existence as well as the number of change points. A partial structural change model is considered. The authors study both fixed and shrinking magnitudes of shifts. In addition, the models allow for serially correlated…

会计约束下的测量误差：潜在数据的点与区间估计及英国 GDP 应用AI 解读

An econometric methodology is proposed for reconciling inaccurate measures of latent data which are subject to accounting constraints. The method deals with the case in which the measurement errors are serially correlated, generalizing previous contributions. A class of efficient estimators are derived for the latent data. Consistent estimators for the weight matrices applied to the observed information based on a linear regression procedure are obtained together with…

丹麦制造业出口价格与数量关系的建模AI 解读

This article develops a structural econometric model for Danish manufacturing exports. The cointegrating relations of the model are determined to be a markup pricing relation and an export demand relation, whereas the short-run structure shows pricing-to-market effects. Both long-run and short-run pricing relations are found to be empirically constant during a period of large exchange-rate variations. The model isolates the main interactions between the foreign-trade sector,…

无需估计序列相关参数的均值突变检验AI 解读

Tests for detecting a shift in the mean of univariate time series that do not require estimation of serial-correlation parameters are proposed. The statistics are valid whether the errors are stationary or have a unit root. The date of the shift may be known or unknown. The statics are based on a simple transformation of the data and are functions of partial sums of the data. These so-called partial sum statistics are shown to be asymptotically invariant to…

动态二元混合模型：价格与交易量行为建模AI 解读

Bivariate mixture models have been used to explain the stochastic behavior of daily price changes and trading volume on financial markets. In this class of models, price changes and volume follow a mixture of bivariate distributions with the unobservable number of price-relevant information serving as the mixing variable. The time series behavior of this mixture variable determines the dynamics of the price-volume system. In this article, bivariate mixture specifications…

以样本外预测表现检验时间序列非线性AI 解读

This article uses a local-information, near-neighbor forecasting methodology as a prediction test for evidence of a noisy, chaotic data-generating process underlying the Divisia monetary-aggregate series. Using a nonparametric method known to perform well with low-dimensional chaotic processes infected by noise, accompanied by a robust test of forecast performance evaluation, the authors compare out-of-sample forecasting accuracy from the local-information method to…