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利用指数平滑预测具有复杂季节模式的时间序列AI 解读

An innovations state space modeling framework is introduced for forecasting complex seasonal time series such as those with multiple seasonal periods, high-frequency seasonality, non-integer seasonality, and dual-calendar effects. The new framework incorporates Box–Cox transformations, Fourier representations with time varying coefficients, and ARMA error correction. Likelihood evaluation and analytical expressions for point forecasts and interval predictions under the…

多变量时间序列的动态正交成分AI 解读

We introduce dynamic orthogonal components (DOC) for multivariate time series and propose a procedure for estimating and testing the existence of DOCs for a given time series. We estimate the dynamic orthogonal components via a generalized decorrelation method that minimizes the linear and quadratic dependence across components and across time. We then use Ljung–Box type statistics to test the existence of dynamic orthogonal components. When DOCs exist, univariate analysis…

利用高维生物标志物轨迹预测病毒感染AI 解读

There is often interest in predicting an individual's latent health status based on high-dimensional biomarkers that vary over time. Motivated by time-course gene expression array data that we have collected in two influenza challenge studies performed with healthy human volunteers, we develop a novel time-aligned Bayesian dynamic factor analysis methodology. The time course trajectories in the gene expressions are related to a relatively low-dimensional vector of latent…

局部平稳因子模型：识别与非参数估计AI 解读

In this paper we propose a new approximate factor model for large cross-section and time dimensions. Factor loadings are assumed to be smooth functions of time, which allows considering the model as locally stationary while permitting empirically observed time-varying second moments. Factor loadings are estimated by the eigenvectors of a nonparametrically estimated covariance matrix. As is well known in the stationary case, this principal components estimator is consistent…

非线性 AR–GARCH 模型的参数估计AI 解读

This paper develops an asymptotic estimation theory for nonlinear autoregressive models with conditionally heteroskedastic errors. We consider a general nonlinear autoregression of order p (AR( p )) with the conditional variance specified as a general nonlinear first-order generalized autoregressive conditional heteroskedasticity (GARCH(1,1)) model. We do not require the rescaled errors to be independent, but instead only to form a stationary and ergodic martingale…

平稳与单位根自回归中的一致渐近正态性AI 解读

While differencing transformations can eliminate nonstationarity, they typically reduce signal strength and correspondingly reduce rates of convergence in unit root autoregressions. The present paper shows that aggregating moment conditions that are formulated in differences provides an orderly mechanism for preserving information and signal strength in autoregressions with some very desirable properties. In first order autoregression, a partially aggregated estimator based…

自协方差矩阵的带状正则化：时间序列参数估计与预测AI 解读

Summary The paper addresses a ‘large p–small n’ problem in a time series framework and considers properties of banded regularization of an empirical autocovariance matrix of a time series process. Utilizing the banded autocovariance matrix enables us to fit a much longer auto-regressive AR(p) model to the observed data than typically suggested by the Akaike information criterion, while controlling how many parameters are to be estimated precisely and the level of accuracy.…

含内生变量的平滑转换回归模型矩估计AI 解读

Nonlinear regression models have been widely used in practice for a variety of time series and cross-section datasets. For purposes of analyzing univariate and multivariate time series data, in particular, smooth transition regression (STR) models have been shown to be very useful for representing and capturing asymmetric behavior. Most STR models have been applied to univariate processes, and have made a variety of assumptions, including stationary or cointegrated…

平稳过程的控制变量降方差方法AI 解读

The sample mean is one of the most natural estimators of the population mean based on independent identically distributed sample. However, if some control variate is available, it is known that the control variate method reduces the variance of the sample mean. The control variate method often assumes that the variable of interest and the control variable are i.i.d. Here we assume that these variables are stationary processes with spectral density matrices, i.e. dependent.…

非线性因果关系的一致非参数检验：时间序列回归设定AI 解读

Since the pioneering work by Granger (1969), many authors have proposed tests of causality between economic time series. Most of them are concerned only with “linear causality in mean”, or if a series linearly affects the (conditional) mean of the other series. It is no doubt of primary interest, but dependence between series may be nonlinear, and/or not only through the conditional mean. Indeed conditional heteroskedastic models are widely studied recently. The purpose of…

局部投影脉冲响应估计量有多可靠？AI 解读

We compare the finite-sample performance of impulse response confidence intervals based on local projections (LPs) and vector autoregressive (VAR) models in linear stationary settings. We find that in small samples, the asymptotic LP interval often is less accurate than the bias-adjusted bootstrap VAR interval, notwithstanding its excessive average length. Although the asymptotic LP interval has adequate coverage in sufficiently large samples, its average length still far…

构成变化的预测者面板中宏观预测时间变化的评估AI 解读

Abstract This paper calls attention to the problem of changing panel composition in surveys of forecasters and documents the problem in the Survey of Professional Forecasters. To study the temporal variation of forecasts, we recommend analysis of the time series of predictions made by individual forecasters. This makes transparent the heterogeneity of the panel and avoids improper inferences due to changing panel composition. In the absence of knowledge of the process…

次贷危机期间金融泡沫起止时间的测定AI 解读

A new recursive regression methodology is introduced to analyze the bubble characteristics of various financial time series during the subprime crisis. The methods modify a technique proposed in Phillips, Wu, and Yu (2011) and provide a technology for identifying bubble behavior with consistent dating of their origination and collapse. The tests serve as an early warning diagnostic of bubble activity and a new procedure is introduced for testing bubble migration across…

美国经济对能源价格上涨与下跌的反应是否不对称？AI 解读

How much does real gross domestic product (GDP) respond to unanticipated changes in the real price of oil? Commonly used censored oil price vector autoregressive models suggest a substantial decline in real GDP in response to unexpected increases in the real price of oil, yet no response to unexpected declines. We show that these estimates are invalid. Based on a structural model that encompasses both symmetric and asymmetric models as special cases, correctly computed…

高维回归分析中的因子模型与变量选择AI 解读

The paper considers linear regression problems where the number of predictor variables is possibly larger than the sample size. The basic motivation of the study is to combine the points of view of model selection and functional regression by using a factor approach: it is assumed that the predictor vector can be decomposed into a sum of two uncorrelated random components reflecting common factors and specific variabilities of the explanatory variables. It is shown that the…

时变参数模型中的内生性：联合估计与两步估计AI 解读

In dealing with the problem of endogeneity in a time‐varying parameter model, we develop the joint and two‐step estimation procedures based on the control function approach. We show that a key to the success of the joint estimation procedure is in an appropriate state‐space representation of the model. On the other hand, a correct treatment of the problem of generated regressors plays an important role in our two‐step estimation procedure. Monte Carlo experiments confirm…

扩散模型的半非参数估计与设定错误检验AI 解读

Novel transition-based misspecification tests of semiparametric and fully parametric univariate diffusion models based on the estimators developed in [Kristensen, D., 2010. Pseudo-maximum likelihood estimation in two classes of semiparametric diffusion models. Journal of Econometrics 156, 239-259] are proposed. It is demonstrated that transition-based tests in general lack power in detecting certain departures from the null since they integrate out local features of the…

采用级数方差估计与检验最优平滑参数的稳健趋势推断AI 解读

The paper develops a novel testing procedure for hypotheses on deterministic trends in a multivariate trend stationary model. The trends are estimated by the OLS estimator and the long run variance (LRV) matrix is estimated by a series type estimator with carefully selected basis functions. Regardless of whether the number of basis functions K is fixed or grows with the sample size, the Wald statistic converges to a standard distribution. It is shown that critical values…

横截面相关大面板中共同确定性时间趋势断点的估计AI 解读

This paper develops an estimation procedure for a common deterministic time trend break in large panels. The dependent variable in each equation consists of a deterministic trend and an error term. The deterministic trend is subject to a change in the intercept, slope or both, and the break date is common for all equations. The estimation method is simply minimizing the sum of squared residuals for all possible break dates. Both serial and cross sectional correlations are…

多变量连续时间模型的广义谱检验AI 解读

We develop an omnibus specification test for multivariate continuous-time models using the conditional characteristic function, which often has a convenient closed-form or can be accurately approximated for many multivariate continuous-time models in finance and economics. The proposed test fully exploits the information in the joint conditional distribution of underlying economic processes and hence is expected to have good power in a multivariate context. A class of…

单整但不协整变量相关性的半参数度量AI 解读

Many macroeconomic and financial variables are integrated of order one (or I(1)) processes and are correlated with each other but not necessarily cointegrated. In this paper, we propose to use a semiparametric varying coefficient approach to model/capture such correlations. We propose two consistent estimators to study the dependence relationship among some integrated but not cointegrated time series variables. Simulations are used to examine the finite sample performances…

多重结构变化下多元模型的模型选择准则AI 解读

This paper considers the issue of selecting the number of regressors and the number of structural breaks in multivariate regression models in the possible presence of multiple structural changes. We develop a modified Akaike information criterion (AIC), a modified Mallows' Cp criterion and a modified Bayesian information criterion (BIC). The penalty terms in these criteria are shown to be different from the usual terms. We prove that the modified BIC consistently selects the…

非平稳半参数模型的数据驱动带宽选择AI 解读

This article extends the asymptotic results of the traditional least squares cross-validatory (CV) bandwidth selection method to semiparametric regression models with nonstationary data. Two main findings are that (a) the CV-selected bandwidth is stochastic even asymptotically and (b) the selected bandwidth based on the local constant method converges to 0 at a different speed than that based on the local linear method. Both findings are in sharp contrast to existing results…

自助法辅助的 ARFIMA 模型设定检验AI 解读

This paper proposes bootstrap assisted specification tests for the autoregressive fractionally integrated moving average model based on the Bartlett T p -process with estimated parameters whose limiting distribution under the null depends on the estimated model and the estimation method employed. The computation of the asymptotic critical values is not easy if at all possible under these circumstances. To circumvent this problem Delgado, Hidalgo, and Velasco (2005, Annals of…