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多重协整系统中的完全修正最小二乘参数估计AI 解读

Multicointegration is traditionally defined as a particular long run relationship among variables in a parametric vector autoregressive model that introduces additional cointegrating links between these variables and partial sums of the equilibrium errors. This paper departs from the parametric model, using a semiparametric formulation that reveals the explicit role that singularity of the long run conditional covariance matrix plays in determining multicointegration. The…

多项式趋势下的泡沫检验AI 解读

Summary This paper develops the asymptotic theory of the least squares estimator of the autoregressive (AR) coefficient in an AR(1) regression with intercept when data is generated from a polynomial trend model in different forms. It is shown that the commonly used right-tailed unit root tests tend to favour the explosive alternative. A new procedure, which implements the right-tailed unit root tests in an AR(2) regression, is proposed. It is shown that, when the data…

持续性预测回归中的参数不稳定与结构变化检验AI 解读

This paper develops parameter instability and structural change tests within predictive regressions for economic systems governed by persistent vector autoregressive dynamics. Specifically, in a setting where all – or a subset – of the variables may be fractionally integrated and the predictive relation may feature cointegration, we provide sup-Wald break tests that are constructed using the Local speCtruM (LCM) approach. The new tests cover both parameter variation and…

局部错误定价与市场微观结构噪声：参数化视角AI 解读

We extend the classic ”martingale-plus-noise” model for high-frequency returns to accommodate an error correction mechanism and endogenous pricing errors. It is motivated by (i) novel empirical evidence documenting that microstructure noise exhibits frequently changing patterns of serial dependence which are interwoven with innovations to the efficient price; (ii) building a bridge between high-frequency econometrics and market microstructure models. We identify temporal…

含平稳协变量的分位数协整非参数推断AI 解读

This paper considers the inference problems in nonlinear quantile regressions with both stationary and nonstationary covariates. The nonparametric local constant quantile estimator is proposed to estimate the unknown quantile regression function, whose asymptotic properties are established under quite general conditions. Specification testing of the quantile regression function is further considered through a statistic constructed based on the integrated squared distance…

次几何遍历的自回归过程AI 解读

In this paper, we discuss how the notion of subgeometric ergodicity in Markov chain theory can be exploited to study stationarity and ergodicity of nonlinear time series models. Subgeometric ergodicity means that the transition probability measures converge to the stationary measure at a rate slower than geometric. Specifically, we consider suitably defined higher-order nonlinear autoregressions that behave similarly to a unit root process for large values of the observed…

伪函数系数回归及基于边际积分的稳健推断AI 解读

Functional-coefficient cointegrating models have become popular to model nonlinear nonstationarity in econometrics (Cai et al., 2009; Xiao, 2009). However, there is rare study on testing the existence of functional-coefficient cointegration. Consequently, functional-coefficient regressions involving nonstationary regressors may be spurious. This paper investigates the effect that spurious functional-coefficient regression has on the model diagnostics. We find that common…

预测回归中的 LASSO 方法AI 解读

Explanatory variables in a predictive regression typically exhibit low signal strength and various degrees of persistence. Variable selection in such a context is of great importance. In this paper, we explore the pitfalls and possibilities of the LASSO methods in this predictive regression framework. In the presence of stationary, local unit root, and cointegrated predictors, we show that the adaptive LASSO cannot asymptotically eliminate all cointegrating variables with…

大型非平稳数据集的共同趋势检验AI 解读

We propose a testing-based procedure to determine the number of common trends in a large nonstationary dataset. Our procedure is based on a factor representation, where we determine whether there are (and how many) common factors (i) with linear trends, and (ii) with stochastic trends. Cointegration among the factors is also permitted. Our analysis is based on the fact that those largest eigenvalues of a suitably scaled covariance matrix of the data corresponding to the…

大型 VAR 中的协整AI 解读

The paper analyzes cointegration in vector autoregressive processes (VARs) for the cases when both the number of coordinates, N, and the number of time periods, T, are large and of the same order. We propose a way to examine a VAR of order 1 for the presence of cointegration based on a modification of the Johansen likelihood ratio test. The advantage of our procedure over the original Johansen test and its finite sample corrections is that our test does not suffer from…

基于样本自协方差的单位根检验AI 解读

Summary We propose a new unit-root test for a stationary null hypothesis $H_0$ against a unit-root alternative $H_1$. Our approach is nonparametric as $H_0$ assumes only that the process concerned is $I(0)$, without specifying any parametric forms. The new test is based on the fact that the sample autocovariance function converges to the finite population autocovariance function for an $I(0)$ process, but diverges to infinity for a process with unit roots. Therefore, the new…

基于动态因子模型的协整时间序列永久—暂时分解：商品价格应用AI 解读

Summary We propose a cointegration-based Permanent-Transitory decomposition for nonstationary dynamic factor models (DFMs). Our methodology exploits the cointegration relations among the observable variables and assumes they are driven by a common and an idiosyncratic component. The common component is further split into a long-term nonstationary and a short-term stationary part. A Monte Carlo experiment shows that incorporating the cointegration structure into the DFM leads…

非平稳波动率下的自适应协整检验AI 解读

This article develops a class of adaptive cointegration tests for multivariate time series with nonstationary volatility. Persistent changes in the innovation variance matrix of a vector autoregressive model lead to size distortions in conventional cointegration tests, which may be resolved using the wild bootstrap, as shown in recent work by Cavaliere, Rahbek, and Taylor. We show that it also leads to the possibility of constructing tests with higher power, by taking the…

通过多元谱的总Frobenius范数进行模型识别AI 解读

Abstract We study the integral of the Frobenius norm as a measure of the discrepancy between two multivariate spectra. Such a measure can be used to fit time series models, and ensures proximity between model and process at all frequencies of the spectral density—this is more demanding than Kullback–Leibler discrepancy, which is instead related to one-step ahead forecasting performance. We develop new asymptotic results for linear and quadratic functionals of the…

金融指数峰度的时间聚合效应AI 解读

Indices of financial returns typically display sample kurtosis that declines towards the Gaussian value 3 as the sampling interval increases. This paper uses stochastic unit root (STUR) and continuous time analysis to explain the phenomenon. Limit theory for the sample kurtosis reveals that STUR specifications provide two sources of excess kurtosis, both of which decline with the sampling interval. Limiting kurtosis is shown to be random and is a functional of the limiting…

基于高频数据的单位根检验AI 解读

Deviations of asset prices from the random walk dynamic imply the predictability of asset returns and thus have important implications for portfolio construction and risk management. This paper proposes a real-time monitoring device for such deviations using intraday high-frequency data. The proposed procedures are based on unit root tests with in-fill asymptotics but extended to take the empirical features of high-frequency financial data (particularly jumps) into…

非平稳数据下的反事实分析与推断AI 解读

Recently, there has been growing interest in developing econometric tools to conduct counterfactual analysis with aggregate data when a single “treated” unit suffers an intervention, such as a policy change, and there is no obvious control group. Usually, the proposed methods are based on the construction of an artificial/synthetic counterfactual from a pool of “untreated” peers, organized in a panel data structure. In this article, we investigate the consequences of…

动态面板数据模型的稳健似然估计AI 解读

We develop likelihood-based estimators for autoregressive panel data models that are consistent in the presence of time series heteroskedasticity. Bias-corrected conditional score estimators, random effects maximum likelihood in levels and first differences, and estimators that impose mean stationarity are considered for general autoregressive models with individual effects. We investigate identification under unit roots, and show that random effects estimation in levels may…

存在内生性的非线性协整幂函数回归AI 解读

This paper develops an asymptotic theory for nonlinear cointegrating power function regression. The framework extends earlier work on the deterministic trend case and allows for both endogeneity and heteroskedasticity, which makes the models and inferential methods relevant to many empirical economic and financial applications, including predictive regression. A new test for linear cointegration against nonlinear departures is developed based on a simple linearized…

具有无限方差 GARCH 误差的非平稳线性过程AI 解读

Recently, Cavaliere, Georgiev, and Taylor (2018, Econometric Theory 34, 302–348) (CGT) considered the augmented Dickey–Fuller (ADF) test for a unit-root model with linear noise driven by i.i.d. infinite variance innovations and showed that ordinary least squares (OLS)-based ADF statistics have the same distribution as in Chan and Tran (1989, Econometric Theory 5, 354–362) for i.i.d. infinite variance noise. They also proposed an interesting question to extend their results…

非平稳随机波动率下的自助法AI 解读

In this paper we investigate to what extent the bootstrap can be applied to conditional mean models, such as regression or time series models, when the volatility of the innovations is random and possibly non-stationary. In fact, the volatility of many economic and financial time series displays persistent changes and possible non-stationarity. However, the theory of the bootstrap for such models has focused on deterministic changes of the unconditional variance and little…

具有良好尺寸与功效表现的简便股票收益可预测性检验AI 解读

We develop easy-to-implement tests for return predictability which, relative to extant tests in the literature, display attractive finite sample size control and power across a wide range of persistence and endogeneity levels for the predictor. Our approach is based on the standard regression t-ratio and a variant where the predictor is quasi-GLS (rather than OLS) demeaned. In the strongly persistent near-unit root environment, the limiting null distributions of these…

协趋势与协整综合模型中简单能量平衡方程的统计检验AI 解读

We develop new tests for the linear relationship between temperature and forcing, which is one of the most studied implications from a simple energy balance model. We consider a bivariate system of temperature and forcing where the time path of well-mixed-greenhouse-gases forcing is included as a potential common trend function in addition to a stochastic trend and a broken linear trend. Our test statistics are first devised as the likelihood ratio and then are modified to…

持久性经济系统中预测回归的有效推断AI 解读

This paper studies standard predictive regressions in economic systems governed by persistent vector autoregressive dynamics for the state variables. In particular, all – or a subset – of the variables may be fractionally integrated, which induces a spurious regression problem. We propose a new inference and testing procedure – the Local speCtruM (LCM) approach – for joint significance of the regressors, that is robust against the variables having different integration…