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近期及持续结构变化下的自适应预测AI 解读

We consider time series forecasting in the presence of ongoing structural change where both the time series dependence and the nature of the structural change are unknown. Methods that downweight older data, such as rolling regressions, forecast averaging over different windows and exponentially weighted moving averages, known to be robust to historical structural change, are found also to be useful in the presence of ongoing structural change in the forecast period. A…

存在结构突变时的最优预测AI 解读

This paper considers the problem of forecasting under continuous and discrete structural breaks and proposes weighting observations to obtain optimal forecasts in the MSFE sense. We derive optimal weights for one step ahead forecasts. Under continuous breaks, our approach largely recovers exponential smoothing weights. Under discrete breaks, we provide analytical expressions for optimal weights in models with a single regressor, and asymptotically valid weights for models…

期权隐含通胀概率密度的经济含义AI 解读

Recently a market in options based on consumer price index inflation (inflation caps and floors) has emerged in the US. This paper uses quotes on these derivatives to construct probability densities for inflation. We study how these probability density functions respond to news announcements and find that the implied odds of deflation are sensitive to certain macroeconomic news releases. We also estimate empirical pricing kernels using these option prices along with time…

时间序列的渐进分块经验似然方法AI 解读

This article develops a new blockwise empirical likelihood (BEL) method for stationary, weakly dependent time processes, called the progressive block empirical likelihood (PBEL). In contrast to the standard version of BEL, which uses data blocks of constant length for a given sample size and whose performance can depend crucially on the block length selection, this new approach involves a data-blocking scheme where blocks increase in length by an arithmetic progression.…

大型稀疏列联表时间依赖的贝叶斯建模AI 解读

In many applications, it is of interest to study trends over time in relationships among categorical variables, such as age group, ethnicity, religious affiliation, political party and preference for particular policies. At each time point, a sample of individuals provide responses to a set of questions, with different individuals sampled at each time. In such settings, there tends to be abundant missing data and the variables being measured may change over time. At each…

函数型数据中主成分数量的选择AI 解读

Functional principal component analysis (FPCA) has become the most widely used dimension reduction tool for functional data analysis. We consider functional data measured at random, subject-specific time points, contaminated with measurement error, allowing for both sparse and dense functional data, and propose novel information criteria to select the number of principal component in such data. We propose a Bayesian information criterion based on marginal modeling that can…

考虑时间序列依赖的统计学习：小鼠睡眠评分应用AI 解读

We develop methodology which combines statistical learning methods with generalized Markov models, thereby enhancing the former to account for time series dependence. Our methodology can accommodate very general and very long-term time dependence structures in an easily estimable and computationally tractable fashion. We apply our methodology to the scoring of sleep behavior in mice. As currently used methods are expensive, invasive, and labor intensive, there is…

持续性周期对零频率单位根检验的影响AI 解读

In this paper we investigate the impact of persistent (nonstationary or near nonstationary) cycles on the asymptotic and finite-sample properties of standard unit root tests. Results are presented for the augmented Dickey–Fuller (ADF) normalized bias and t -ratio-based tests (Dickey and Fuller, 1979, Journal of the American Statistical Association 745, 427–431; Said and Dickey, 1984; Biometrika 71, 599–607). the variance ratio unit root test of Breitung (2002, Journal of…

非平稳自回归中利用虚拟变量处理无限方差创新AI 解读

We consider estimation and testing in finite-order autoregressive models with a (near) unit root and infinite-variance innovations. We study the asymptotic properties of estimators obtained by dummying out “large” innovations, i.e., those exceeding a given threshold. These estimators reflect the common practice of dealing with large residuals by including impulse dummies in the estimated regression. Iterative versions of the dummy-variable estimator are also discussed. We…

交互固定效应面板模型中的同质性检验AI 解读

This paper proposes a residual-based Lagrange Multiplier (LM) test for slope homogeneity in large-dimensional panel data models with interactive fixed effects. We first run the panel regression under the null to obtain the restricted residuals and then use them to construct our LM test statistic. We show that after being appropriately centered and scaled, our test statistic is asymptotically normally distributed under the null and a sequence of Pitman local alternatives. The…

存在水平变化与确定性趋势时的记忆参数估计AI 解读

We propose estimators of the memory parameter of a time series that are robust to a wide variety of random level shift processes, deterministic level shifts, and deterministic time trends. The estimators are simple trimmed versions of the popular log-periodogram regression estimator that employ certain sample-size-dependent and, in some cases, data-dependent trimmings that discard low-frequency components. We also show that a previously developed trimmed local Whittle…

非线性协整向量误差修正模型中的检验与推断AI 解读

We analyze estimators and tests for a general class of vector error correction models that allows for asymmetric and nonlinear error correction. For a given number of cointegration relationships, general hypothesis testing is considered, where testing for linearity is of particular interest. Under the null of linearity, parameters of nonlinear components vanish, leading to a nonstandard testing problem. We apply so-called sup-tests to resolve this issue, which requires…

时空最大稳定过程的统计推断AI 解读

Summary Max-stable processes have proved to be useful for the statistical modelling of spatial extremes. Several families of max-stable random fields have been proposed in the literature. One such representation is based on a limit of normalized and rescaled pointwise maxima of stationary Gaussian processes that was first introduced by Kabluchko and co-workers. This paper deals with statistical inference for max-stable space–time processes that are defined in an analogous…

二阶平稳性检验与局部平稳序列自协方差的近似置信区间AI 解读

Summary Many time series are not second order stationary and it is not appropriate to analyse them by using methods designed for stationary series. The paper introduces a new test for second-order stationarity that detects kinds of departures from stationarity that are different from those based on Fourier methods. The new test is also computationally fast, designed to work with Gaussian and a wide range of non-Gaussian time series, and can locate non-stationarities in time…

结构 VAR 模型中脉冲响应函数的推断AI 解读

Skepticism toward traditional identifying assumptions based on exclusion restrictions has led to a surge in the use of structural VAR models in which structural shocks are identified by restricting the sign of the responses of selected macroeconomic aggregates to these shocks. Researchers commonly report the vector of pointwise posterior medians of the impulse responses as a measure of central tendency of the estimated response functions, along with pointwise 68% posterior…

长记忆、短记忆与负记忆时间序列中学生化均值的分布理论AI 解读

We consider the problem of estimating the variance of the partial sums of a stationary time series that has either long memory, short memory, negative/intermediate memory, or is the first-difference of such a process. The rate of growth of this variance depends crucially on the type of memory, and we present results on the behavior of tapered sums of sample autocovariances in this context when the bandwidth vanishes asymptotically. We also present asymptotic results for the…

无需正性约束的 GARCH 模型：指数 GARCH 还是对数 GARCH？AI 解读

This paper provides a probabilistic and statistical comparison of the log-GARCH and EGARCH models, which both rely on multiplicative volatility dynamics without positivity constraints. We compare the main probabilistic properties (strict stationarity, existence of moments, tails) of the EGARCH model, which are already known, with those of an asymmetric version of the log-GARCH. The quasi-maximum likelihood estimation of the log-GARCH parameters is shown to be strongly…

跳跃是否影响股票风险溢价的动态变化？AI 解读

This paper investigates whether risks associated with time-varying arrival of jumps and their effect on the dynamics of higher moments of returns are priced in the conditional mean of daily market excess returns. We find that jumps and jump dynamics are significantly related to the market equity premium. The results from our time-series approach reinforce the importance of the skewness premium found in cross-sectional studies using lower-frequency data; and offer a potential…

半参数向量乘法误差模型AI 解读

SUMMARY Financial time series are often non‐negative‐valued (volumes, trades, durations, realized volatility, daily range) and exhibit clustering. When joint dynamics is of interest, the vector multiplicative error model (vMEM; the element‐by‐element product of a vector of conditionally autoregressive scale factors and a multivariate i.i.d. innovation process) is a suitable strategy. Its parameters can be estimated by generalized method of moments, bypassing the problem of…

包含偏度与杠杆效应的条件异方差因子模型AI 解读

SUMMARY Conditional heteroskedasticity, skewness and leverage effects are well‐known features of financial returns. The literature on factor models has often made assumptions that preclude the three effects to occur simultaneously. In this paper I propose a conditionally heteroskedastic factor model that takes into account the presence of both the conditional skewness and leverage effects. This model is specified in terms of conditional moment restrictions and unconditional…

含非加性不可观测异质性的面板模型：估计与推断AI 解读

This paper considers fixed effects estimation and inference in linear and nonlinear panel data models with random coefficients and endogenous regressors. The quantities of interest—means, variances, and other moments of the random coefficients—are estimated by cross sectional sample moments of generalized method of moments (GMM) estimators applied separately to the time series of each individual. To deal with the incidental parameter problem introduced by the noise of the…

微观结构噪声的相关结构：金融经济学视角AI 解读

We introduce the financial economics of market microstructure to the financial econometrics of asset return volatility estimation. In particular, we derive the cross-correlation function between latent returns and market microstructure noise in several leading microstructure environments. We propose and illustrate several corresponding theory-inspired volatility estimators, which we apply to stock and oil prices. Our analysis and results are useful for assessing the validity…

波动率过程秩的检验：随机扰动方法AI 解读

In this paper, we present a test for the maximal rank of the matrix-valued volatility process in the continuous Itô semimartingale framework. Our idea is based upon a random perturbation of the original high frequency observations of an Itô semimartingale, which opens the way for rank testing. We develop the complete limit theory for the test statistic and apply it to various null and alternative hypotheses. Finally, we demonstrate a homoscedasticity test for the rank…

含移动平均误差的随机波动率模型及其通胀预测应用AI 解读

We introduce a new class of models that has both stochastic volatility and moving average errors, where the conditional mean has a state space representation. Having a moving average component, however, means that the errors in the measurement equation are no longer serially independent, and estimation becomes more difficult. We develop a posterior simulator that builds upon recent advances in precision-based algorithms for estimating these new models. In an empirical…