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时间序列分析中的门限模型：若干回顾与思考AI 解读

In this paper, I reflect on the developments of the threshold model in time series analysis since its birth in 1978, with particular reference to econometrics.

时间序列与金融计量经济学前沿：概览AI 解读

Two of the fastest growing frontiers in econometrics and quantitative finance are time series and financial econometrics. Significant theoretical contributions to financial econometrics have been made by experts in statistics, econometrics, mathematics, and time series analysis. The purpose of this special issue of the journal on “Frontiers in Time Series and Financial Econometrics” is to highlight several areas of research by leading academics in which novel methods have…

变点估计：重新审视多重检验问题AI 解读

We consider large scale multiple testing for data that have locally clustered signals. With this structure, we apply techniques from changepoint analysis and propose a boundary detection algorithm so that the clustering information can be utilized. Consequently the precision of the multiple testing procedure is substantially improved. We study tests with independent as well as dependent |$p$|-values. Monte Carlo simulations suggest that the methods perform well with…

共同断点下因子载荷结构变化的检验AI 解读

This paper proposes a new test for factor loading structural change in dynamic factor models. We first show that the leading test proposed by Breitung and Eickmeier (2011) exhibits nonmonotonic power, essentially because the breaks are considered as spurious factors with stable factor loadings. The proposed test eliminates the effects of the spurious factors by maximizing the test statistic over possible numbers of the original factors. Monte Carlo simulations and an…

动态资产定价模型的回归估计AI 解读

We propose regression-based estimators for beta representations of dynamic asset pricing models with an affine pricing kernel specification. We allow for state variables that are cross-sectional pricing factors, forecasting variables for the price of risk, and factors that are both. The estimators explicitly allow for time-varying prices of risk, time-varying betas, and serially dependent pricing factors. Our approach nests the Fama-MacBeth two-pass estimator as a special…

时间序列自归一化：近期进展综述AI 解读

This article reviews some recent developments on the inference of time series data using the self-normalized approach. We aim to provide a detailed discussion about the use of self-normalization in different contexts and highlight distinctive feature associated with each problem and connections among these recent developments. The topics covered include: confidence interval construction for a parameter in a weakly dependent stationary time series setting, change point…

含吸收态的状态切换ARMA贝叶斯推断：事前实际利率动态AI 解读

One goal of this article is to develop an efficient Metropolis–Hastings (MH) algorithm for estimating an ARMA model with a regime-switching mean, by designing a new efficient proposal distribution for the regime-indicator variable. Unlike the existing algorithm, our algorithm can achieve reasonably fast convergence to the posterior distribution even when the latent regime-indicator variable is highly persistent or when there exist absorbing states. Another goal is to…

动态 Copula 的隐马尔可夫结构AI 解读

Understanding the time series dynamics of a multi-dimensional dependency structure is a challenging task. Multivariate covariance driven Gaussian or mixed normal time varying models have only a limited ability to capture important features of the data such as heavy tails, asymmetry, and nonlinear dependencies. The present paper tackles this problem by proposing and analyzing a hidden Markov model (HMM) for hierarchical Archimedean copulae (HAC). The HAC constitute a wide…

非平稳解释变量预测回归中的不稳定性检验AI 解读

It is well known that allowing the coefficients to be time-varying in a predictive model with possibly nonstationary regressors can help to deal with instability in predictability associated with linear predictive models. In this paper, an L 2 -type test statistic is proposed to test the stability of the coefficient vector, and the asymptotic distributions of the proposed test statistic are developed under both null and alternative hypotheses. A Monte Carlo experiment is…

动态因子模型中的参数不稳定性检验AI 解读

In this paper, we develop tests for structural breaks of factor loadings in dynamic factor models. We focus on the joint null hypothesis that all factor loadings are constant over time. Because the number of factor loading parameters goes to infinity as the sample size grows, conventional tests cannot be used. Based on the fact that the presence of a structural change in factor loadings yields a structural change in second moments of factors obtained from the full sample…

基于最优检验的结构断点日期置信集AI 解读

In this paper, we propose constructing a confidence set for the date of a one‐time structural change using a point optimal test. Following Elliott and Müller (2007, Journal of Econometrics 141, 1196–1218), we first construct a test for the break date that maximizes the weighted average of the power function. The confidence set is then obtained by inverting the test statistic. We carefully choose the weights and show by Monte Carlo simulations that the confidence set based on…

加性模型的设定与结构突变检验及其已实现方差应用AI 解读

We study two types of testing problems in a nonparametric additive model setting: We develop methods to test (i) whether an additive component function has a given parametric form and (ii) whether an additive component has a structural break. We apply the theory to a nonparametric extension of the linear heterogeneous autoregressive model which is widely employed to describe realized variance data. We find that the linearity assumption is often rejected, but actual…

模型误设下的结构突变模型及其预测含义AI 解读

This paper revisits the least squares estimator of the linear regression with a structural break. We view the model as an approximation to the true data generating process whose exact nature is unknown but perhaps changing over time either continuously or with some jumps. This view is widely held in the forecasting literature and under this view, the time series dependence property of all the observed variables is unstable as well. We establish that the rate of convergence…

滞回自回归时间序列模型AI 解读

This paper extends the classical two-regime threshold autoregressive model by introducing hysteresis to its regime-switching structure, which leads to a new model: the hysteretic autoregressive model. The proposed model enjoys the piecewise linear structure of a threshold model but has a more flexible regime switching mechanism. A sufficient condition is given for geometric ergodicity. Conditional least squares estimation is discussed, and the asymptotic distributions of its…

美国货币政策的均衡不确定性与识别失效：稳健检验结果AI 解读

Summary We propose a novel identification‐robust test for the null hypothesis that an estimated New Keynesian model has a reduced form consistent with the unique stable solution against the alternative of sunspot‐driven multiple equilibria. Our strategy is designed to handle identification failures as well as the misspecification of the relevant propagation mechanisms. We invert a likelihood ratio test for the cross‐equation restrictions (CER) that the New Keynesian system…

Summary We estimate versions of the Nelson–Siegel model of the yield curve of US government bonds using a Markov switching latent variable model that allows for discrete changes in the stochastic process followed by the interest rates. Our modeling approach is motivated by evidence suggesting the existence of breaks in the behavior of the US yield curve that depend, for example, on whether the economy is in a recession or a boom, or on the stance of monetary policy. Our…

异质大规模数据中的最大最小效应AI 解读

Large-scale data are often characterized by some degree of inhomogeneity as data are either recorded in different time regimes or taken from multiple sources. We look at regression models and the effect of randomly changing coefficients, where the change is either smoothly in time or some other dimension or even without any such structure. Fitting varying-coefficient models or mixture models can be appropriate solutions but are computationally very demanding and often return…

股票收益与现金流可预测性：波动率风险的作用AI 解读

We examine the joint predictability of return and cash flow within a present value framework, by imposing the implications from a long-run risk model that allow for both time-varying volatility and volatility uncertainty. We provide new evidence that the expected return variation and the variance risk premium positively forecast both short-horizon returns and dividend growth rates. We also confirm that dividend yield positively forecasts long-horizon returns, but that it…

异方差ARFIMA模型的分数单整自助得分检验及商品价格应用AI 解读

Empirical evidence from time series methods which assume the usual I(0)/I(1) paradigm suggests that the efficient market hypothesis, stating that spot and futures prices of a commodity should co-integrate with a unit slope on futures prices, does not hold. However, these statistical methods are known to be unreliable if the data are fractionally integrated. Moreover, spot and futures price data tend to display clear patterns of time-varying volatility which also has the…

半参数乘法波动率模型的稳健最小绝对偏差估计AI 解读

We investigate a model in which we connect slowly time varying unconditional long-run volatility with short-run conditional volatility whose representation is given as a semi-strong GARCH(1,1) process with heavy tailed errors. We focus on robust estimation of both long-run and short-run volatilities. Our estimation is semiparametric since the long-run volatility is totally unspecified whereas the short-run conditional volatility is a parametric semi-strong GARCH(1,1)…

时变非参数回归模型的结构变化检验AI 解读

In this paper, we consider a nonparametric model with a time-varying regression function and locally stationary regressors. We are interested in the question whether the regression function has the same shape over a given time span. To tackle this testing problem, we propose a kernel-based L 2 -test statistic. We derive the asymptotic distribution of the statistic both under the null and under fixed and local alternatives. To improve the small sample behavior of the test, we…

含单整解释变量门限模型的稳健估计与推断AI 解读

This paper studies the robust estimation and inference of threshold models with integrated regressors. We derive the asymptotic distribution of the profiled least squares (LS) estimator under the diminishing threshold effect assumption that the size of the threshold effect converges to zero. Depending on how rapidly this sequence converges, the model may be identified or only weakly identified and asymptotic theorems are developed for both cases. As the convergence rate is…

利用惩罚样条估计含非平稳解释变量的变系数模型AI 解读

This paper considers estimation and inference for varying-coefficient models with nonstationary regressors. We propose a nonparametric estimation method using penalized splines, which achieves the same optimal convergence rate as kernel-based methods, but enjoys computation advantages. Utilizing the mixed model representation of penalized splines, we develop a likelihood ratio test statistic for checking the stability of the regression coefficients. We derive both the exact…

非平稳与尖峰厚尾金融时间序列的建模AI 解读

Financial time series is often assumed to be stationary and has a normal distribution in the literature. Both assumptions are however unrealistic. This paper proposes a new methodology with a focus on volatility estimation that is able to account for nonstationarity and heavy tails simultaneously. In particular, a local exponential smoothing (LES) approach is developed, in which weak estimates with different memory parameters are aggregated in a locally adaptive way. The…