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非平凡协整的检验AI 解读

Cointegration is pivotal in analyzing long-run equilibrium relationships among economic variables. Traditional cointegration models have been effective in handling mixed-order integrated variables, but they can lead to misleading conclusions from an equilibrium perspective if trend stationary observables are involved. This type of variable leads to the so-called trivial cointegration, which might falsely suggest a long-run relationship where none exists. Testing for…

无条件异方差下基于 LAD 的自适应 Bootstrap 单位根检验AI 解读

This paper studies unit root testing based on least absolute deviations (LAD) regression under unconditional heteroscedasticity. We first derive asymptotic properties of the LAD estimator under both unit root and local-to-unity settings in the presence of unconditional heteroscedasticity and weak dependence. The results show that the limiting distribution of the LAD estimator (and thus the derived test statistics) is closely associated with unknown heteroscedasticity. To…

单一积分型尾部形状参数下极端 VaR 与 ES 的联合动态AI 解读

We propose a robust semi-parametric framework for persistent time-varying extreme tail behavior, including extreme Value-at-Risk (VaR) and Expected Shortfall (ES). The framework builds on Extreme Value Theory and uses a conditional version of the Generalized Pareto Distribution (GPD) for peaks-over-threshold (POT) dynamics. Unlike earlier approaches, our model (i) has unit root-like, that is, integrated autoregressive dynamics for the GPD tail shape, and (ii) re-scales POTs…

函数型时间序列共同趋势的统计推断AI 解读

We study statistical inference on unit roots and cointegration for time series in a Hilbert space. We develop statistical inference on the number of common stochastic trends embedded in the time series, that is, the dimension of the nonstationary subspace. We also consider tests of hypotheses on the nonstationary and stationary subspaces themselves. The Hilbert space can be of an arbitrarily large dimension, and our methods remain asymptotically valid even when the time…

局部单位根回归的自加权估计及应用AI 解读

A new self-weighted (SW) least squares (LS) estimation approach is developed for local unit root (LUR) autoregression with conditionally heteroskedastic innovations. Asymptotically controlled trimming of the most extreme observations in LS regression is shown to lead to mixed Gaussian limit theory and studentization produces standard normality free of the unknown LUR parameter. The estimator is super consistent with a convergence rate slightly below the O P ( n ) $O_P(n)$ upp…

多交易场所资产的积分方差估计AI 解读

In this paper, we show that the availability of multiple price series for the same asset can be exploited to estimate its integrated variance. We use a vector error correction model for those prices and its common trend representation to estimate the efficient price of the asset. Because the estimated efficient price satisfies the martingale property, we can then compute the sum of its squared intra-day returns to obtain an unbiased and efficient realized variance estimator.…

高维预测回归中的 LASSO 推断AI 解读

LASSO inflicts shrinkage bias on estimated coefficients, which undermines asymptotic normality and invalidates standard inferential procedures based on the t -statistic. Given cross sectional data, the desparsified LASSO has emerged as a well-known remedy for correcting the shrinkage bias. In the context of high dimensional predictive regression, the desparsified LASSO faces an additional challenge: the Stambaugh bias arising from nonstationary regressors modeled as local…

若干长期宏观预测的实证评价AI 解读

We use long-run annual cross-country data for 10 macroeconomic variables to evaluate the long-horizon forecast distributions of six forecasting models. The variables we use range from ones having little serial correlation to ones having persistence consistent with unit roots. Our forecasting models include simple time series models and frequency domain models developed in Müller and Watson (2016). For plausibly stationary variables, an AR(1) model and a frequency domain…

高维时间序列预测回归中有效协整秩的确定AI 解读

This article proposes a new approach to identifying the effective cointegration rank in high-dimensional unit-root (HDUR) time series from a prediction perspective using reduced-rank regression. For a HDUR process xt∈RN and a stationary series yt∈Rp of interest, our goal is to predict future values of yt using xt and lagged values of yt. The proposed framework consists of a two-step estimation procedure. First, the Principal Component Analysis is used to identify all…

高维协整的迹检验AI 解读

This paper studies Johansen’s (J. Econom. Dynam. Control 12 (1988) 231–254) trace test for cointegration in high-dimensional data. We show that when both cross-sectional and temporal dimension of the data go to infinity proportionally, the shifted and scaled modified trace statistic converges to a Gaussian random variable. We give explicit formulae for the shift and scale parameters as well as for the mean and variance of the Gaussian limit. Monte Carlo analysis shows…

函数系数回归与气候敏感性分析的新渐近理论AI 解读

A general asymptotic theory is established for sample cross moments of nonstationary time series, allowing for long-range dependence and local unit roots. The theory provides a substantial extension of earlier results on nonparametric regression that include near-cointegrated nonparametric regression as well as spurious nonparametric regression. Many new models are covered by the limit theory, among which are functional coefficient regressions in which both regressors and…

非平稳时间序列非线性泛函的一般极限理论AI 解读

New limit theory is provided for a wide class of sample variance and covariance functionals involving both nonstationary and stationary time series. Sample functionals of this type commonly appear in regression applications and the asymptotics are particularly relevant to estimation and inference in nonlinear nonstationary regressions that involve unit root, local unit root, or fractional processes. The limit theory is unusually general in that it covers both parametric and…

具有不可观测分组异质性的面板自回归模型统一推断AI 解读

This paper considers unified estimation and inference in panel autoregressive (PAR) models. The PAR coefficients are assumed to contain a latent group structure that allows the degree of persistence for each time series to be heterogeneous and unknown. We propose a novel penalized weighted least squares approach to simultaneously identify the unknown group membership and consistently estimate the PAR coefficients, regardless of whether the underlying PAR process is…

气候敏感度是否因地区而异？变系数方法AI 解读

Global mean surface temperature has been increasing in response to growing greenhouse gas concentrations (IPCC, 2021). While Earth is getting warmer overall, regions that have different local geographical features experience unequal increases in temperature. In this paper, we develop a varying-coefficient dynamic panel data model and use it to measure local climate sensitivity, defined as the increase in temperature in a given location following a doubling of CO2…

AR(1) 模型的局部投影残差自助法AI 解读

This article proposes a local projection (LP) residual bootstrap method to construct confidence intervals for impulse response coefficients of AR(1) models. Our bootstrap method is based on the LP approach and involves a residual bootstrap procedure applied to AR(1) models. We present theoretical results for our bootstrap method and proposed confidence intervals. First, we prove the uniform consistency of the LP-residual bootstrap over a large class of AR(1) models that…

偶尔约束生效情形下的协整AI 解读

In the literature on nonlinear cointegration, a long-standing open problem relates to how a (nonlinear) vector autoregression, which provides a unified description of the short- and long-run dynamics of a vector of time series, can generate ‘nonlinear cointegration’ in the profound sense of those series sharing common nonlinear stochastic trends. We consider this problem in the setting of the censored and kinked structural VAR (CKSVAR), which provides a flexible yet…

通过特征值分析识别高维时间序列结构AI 解读

Cross-sectional structures and temporal tendency are important features of high-dimensional time series. Based on eigen-analysis on sample covariance matrices, we propose a novel approach to identifying four popular structures of high-dimensional time series, which are grouped in terms of factor structures and stationarity. The proposed three-step method includes a ratio statistic of empirical eigenvalues;a projected Augmented Dickey-Fuller Test;a new unit-root test based on…

时变向量误差修正模型：估计与推断AI 解读

This paper considers a time-varying vector error-correction model that allows for different time series behaviors (e.g., unit-root and locally stationary processes) to interact with each other and co-exist. From a practical perspective, this framework can be used to estimate shifts in the predictability of non-stationary variables, and test whether economic theories hold periodically, etc. We first develop a time-varying Granger Representation Theorem, which facilitates the…

具有局部单位根的数据中的伪因子AI 解读

This paper extends the spurious factor analysis of Onatski and Wang (2021, Spurious factor analysis. Econometrica , 89(2), 591–614.) to high-dimensional data with heterogeneous local-to-unit roots. We find a spurious factor phenomenon similar to that observed in the data with unit roots. Namely, the “factors” estimated by the principal components analysis converge to principal eigenfunctions of a weighted average of the covariance kernels of the demeaned Ornstein–Uhlenbeck…

时变因子模型的区分检验AI 解读

Time-varying factor models have been widely used to model changing relationships among economic and financial variables. The existing literature usually specifies the time-varying factor loadings as deterministic functions of time or unit root processes. This article proposes two consistent tests to distinguish between these two specifications based on a randomization approach. By setting the null hypothesis as either specification, we show that the proposed test statistics…

协整多项式回归：完全修正最小二乘法的稳健性AI 解读

Cointegrating polynomial regressions (CPRs) include deterministic variables, integrated variables, and their powers as explanatory variables. Based on a novel kernel-weighted limit result and a novel functional central limit theorem, this paper shows that the fully modified ordinary least squares (FM-OLS) estimator of Phillips and Hansen (1990, Review of Economic Studies 57, 99–125) is robust to being used in CPRs. Being used in CPRs refers to a widespread empirical practice…

允许单整回归量的函数系数模型局部多项式估计极限理论AI 解读

Limit theory for functional coefficient cointegrating regression was recently found to be considerably more complex than earlier understood. The issues were explained and correct limit theory derived for the kernel weighted local level estimator in Phillips and Wang (2023b). The present paper provides complete limit theory for the general kernel weighted local pth order polynomial estimators of the functional coefficient and the coefficient derivatives. Both stationary and…

非协整函数系数回归的极限理论与推断AI 解读

Functional coefficient (FC) cointegrating regressions offer empirical investigators flexibility in modeling economic relationships by introducing covariates that influence the direction and intensity of comovement among nonstationary time series. FC regression models are also useful when formal cointegration is absent, in the sense that the equation errors may themselves be nonstationary, but where the nonstationary series display well-defined FC linkages that can be…

具有一般因子与解释变量的交互效应面板模型AI 解读

This paper considers a model with general regressors and unobservable common factors. An estimator based on iterated principal component analysis is proposed, which is shown to be not only asymptotically normal, but under certain conditions also free of the otherwise so common asymptotic incidental parameters bias. Interestingly, the conditions required to achieve unbiasedness become weaker the stronger the trends in the factors, and if the trending is strong enough,…