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对异方差和自相关稳健的结构变化检测AI 解读

The assumption of (weak) stationarity is crucial for the validity of most of the conventional tests of structure change in time series. Under complicated nonstationary temporal dynamics, we argue that traditional testing procedures result in mixed structural change signals of the first and second order and hence could lead to biased testing results. The article proposes a simple and unified bootstrap testing procedure that provides consistent testing results under general…

含单整协变量的半参数函数系数模型AI 解读

Abstract Cai, Li, and Park ( Journal of Econometrics , 2009) and Xiao ( Journal of Econometrics , 2009) developed asymptotic theories for estimators of semiparametric varying coefficient models when regressors are integrated processes but the smooth coefficients are functionals of stationary processes. Using a recent result from Phillips ( Econometric Theory , 2009), we extend this line of research by allowing for both the regressors and the covariates entering the smooth…

类别型半参数变系数模型AI 解读

SUMMARY Semiparametric varying‐coefficient models have become a common fixture in applied data analysis. Existing approaches, however, presume that those variables affecting the coefficients are continuous in nature (or that there exists at least one such continuous variable) which is often not the case. Furthermore, when all variables affecting the coefficients are categorical/discrete, theoretical underpinnings cannot be obtained as a special case of existing approaches…

具有多个时变个体效应的面板数据模型AI 解读

This paper considers a panel data model with time-varying individual effects. The data are assumed to contain a large number of cross-sectional units repeatedly observed over a fixed number of time periods. The model has a feature of the fixed-effects model in that the effects are assumed to be correlated with the regressors. The unobservable individual effects are assumed to have a factor structure. For consistent estimation of the model, it is important to estimate the…

序贯多传感器变点检测AI 解读

We develop a mixture procedure to monitor parallel streams of data for a change-point that affects only a subset of them, without assuming a spatial structure relating the data streams to one another. Observations are assumed initially to be independent standard normal random variables. After a change-point the observations in a subset of the streams of data have nonzero mean values. The subset and the post-change means are unknown. The procedure we study uses stream…

函数系数工具变量模型的局部线性 GMM 估计及教育回报应用AI 解读

We consider the local linear generalized method of moment (GMM) estimation of functional coefficient models with a mix of discrete and continuous data and in the presence of endogenous regressors. We establish the asymptotic normality of the estimator and derive the optimal instrumental variable that minimizes the asymptotic variance-covariance matrix among the class of all local linear GMM estimators. Data-dependent bandwidth sequences are also allowed for. We propose a…

潜在阈值动态模型的贝叶斯分析AI 解读

We discuss a general approach to dynamic sparsity modeling in multivariate time series analysis. Time-varying parameters are linked to latent processes that are thresholded to induce zero values adaptively, providing natural mechanisms for dynamic variable inclusion/selection. We discuss Bayesian model specification, analysis and prediction in dynamic regressions, time-varying vector autoregressions, and multivariate volatility models using latent thresholding. Application…

检测持久性多重结构变化的 Wald 检验AI 解读

This paper considers the problem of testing for multiple structural changes in the persistence of a univariate time series. We propose sup-Wald tests of the null hypothesis that the process has an autoregressive unit root throughout the sample against the alternative hypothesis that the process alternates between stationary and unit root regimes. We derive the limit distributions of the tests under the null and establish their consistency under the relevant alternatives. We…

分数阶单整下固定 b 趋势突变检验的行为AI 解读

Testing for the presence of a broken linear trend when the nature of the persistence in the data is unknown is not a trivial problem, because the test needs to be both asymptotically correctly sized and consistent, regardless of the order of integration of the data. In a recent paper, Sayginsoy and Vogelsang (2011, Econometric Theory 27, 992–1025) (SV) show that tests based on fixed- b asymptotics provide a useful solution to this problem in the case where the shocks may be…

利率期限结构中的长期风险：估计AI 解读

SUMMARY This paper estimates a model in which persistent fluctuations in expected consumption growth, expected inflation, and their time‐varying volatility determine asset price variation. The model features Epstein–Zin recursive preferences, which determine the market price of macro risk factors. Analysis of the US nominal term structure data from 1953 to 2006 shows that agents dislike high uncertainty and demand compensation for volatility risks. Also, the time variation…

协变量部分可观测时泊松强度的最优估计AI 解读

Rathbun et al. (2007) and Waagepetersen (2008) propose estimating functions for parameters of Poisson point process intensity that may be applied when space- and/or time-varying covariates are sampled from a probability-based sampling design. This paper demonstrates that Waageptersen’s estimating function is optimal in a class of weighted estimating functions.

波动率结构变化的高功效检验AI 解读

Detecting structural changes in volatility is important for understanding volatility dynamics and stylized facts observed for financial returns such as volatility persistence. We propose modified CUSUM and LM tests that are built on a robust estimator of the long-run variance of squared series. We establish conditions under which the new tests have standard null distributions and diverge faster than standard tests under the alternative. The theory allows smooth and abrupt…

结构变化实时监测的多变量方法AI 解读

SUMMARY Detection of structural change is a critical empirical activity, but continuous ‘monitoring’ for changes in real time raises well‐known econometric issues that have been explored in a single series context. If multiple series co‐break then it is possible that simultaneous examination of a set of series helps identify changes with higher probability or more rapidly than when series are examined on a case‐by‐case basis. Some asymptotic theory is developed for maximum…

实现波动率微笑：利用已实现波动率进行期权定价AI 解读

We develop a discrete-time stochastic volatility option pricing model exploiting the information contained in the Realized Volatility (RV), which is used as a proxy of the unobservable log-return volatility. We model the RV dynamics by a simple and effective long-memory process, whose parameters can be easily estimated using historical data. Assuming an exponentially affine stochastic discount factor, we obtain a fully analytic change of measure. An empirical analysis of…

矩条件稳定性的非参数检验AI 解读

This paper considers testing for moment condition instability for a wide variety of models that arise in econometric applications. We propose a nonparametric test based on smoothing the moment conditions over time. The resulting test takes the form of a U-statistic and has a limiting normal distribution. The proposed test statistic is not affected by changes in the distribution of the data, so long as certain simple regularity conditions hold. We examine the performance of…

不稳定非线性最小二乘模型的估计与推断AI 解读

There is compelling evidence that many macroeconomic and financial variables are not generated by linear models. This evidence is based on testing linearity against either smooth nonlinearity or piece-wise linearity, but there is no framework that encompasses both. This paper provides an econometric framework that allows for both breaks and smooth nonlinearity in between breaks. We estimate the unknown break-dates simultaneously with other parameters via nonlinear…

兼含平稳区制与单位根区制的门限自回归模型估计AI 解读

This paper treats estimation in a class of new nonlinear threshold autoregressive models with both a stationary and a unit root regime. Existing literature on nonstationary threshold models has basically focused on models where the nonstationarity can be removed by differencing and/or where the threshold variable is stationary. This is not the case for the process we consider, and nonstandard estimation problems are the result.

超越随机波动率及收益与波动率跳跃的建模AI 解读

While a great deal of attention has been focused on stochastic volatility in stock returns, there is strong evidence suggesting that return distributions have time-varying skewness and kurtosis as well. Under the risk-neutral measure, for example, this can be observed from variation across time in the shape of Black–Scholes implied volatility smiles. This article investigates model characteristics that are consistent with variation in the shape of return distributions using…

大横截面线性因子定价模型检验：分布无关方法AI 解读

In this article, we develop a finite-sample distribution-free procedure to test the beta-pricing representation of linear factor pricing models. In sharp contrast to extant finite-sample tests, our framework allows for unknown forms of nonnormalities, heteroscedasticity, and time-varying covariances. The power of the proposed test procedure increases as the time series lengthens and/or the cross section becomes larger. So the criticism sometimes heard that nonparametric…

油价能否帮助预测美国实际 GDP？非线性与非对称性的作用AI 解读

There is a long tradition of using oil prices to forecast U.S. real GDP. It has been suggested that the predictive relationship between the price of oil and one-quarter-ahead U.S. real GDP is nonlinear in that (a) oil price increases matter only to the extent that they exceed the maximum oil price in recent years, and that (b) oil price decreases do not matter at all. We examine, first, whether the evidence of in-sample predictability in support of this view extends to…

马尔可夫状态切换MIDAS模型AI 解读

This article introduces a new regression model—Markov-switching mixed data sampling (MS-MIDAS)—that incorporates regime changes in the parameters of the mixed data sampling (MIDAS) models and allows for the use of mixed-frequency data in Markov-switching models. After a discussion of estimation and inference for MS-MIDAS and a small sample simulation-based evaluation, the MS-MIDAS model is applied to the prediction of the U.S. economic activity, in terms of both quantitative…

变化环境下的实时通胀预测AI 解读

This article revisits the accuracy of inflation forecasting using activity and expectations variables. We apply Bayesian model averaging across different regression specifications selected from a set of potential predictors that includes lagged values of inflation, a host of real activity data, term structure data, (relative) price data, and surveys. In this model average, we can entertain different channels of structural instability, by either incorporating stochastic…

宏观经济预测与结构变化AI 解读

SUMMARY The aim of this paper is to assess whether modeling structural change can help improving the accuracy of macroeconomic forecasts. We conduct a simulated real‐time out‐of‐sample exercise using a time‐varying coefficients vector autoregression (VAR) with stochastic volatility to predict the inflation rate, unemployment rate and interest rate in the USA. The model generates accurate predictions for the three variables. In particular, the forecasts of inflation are much…

应对宏观经济模型的误设AI 解读

We estimate a Markov-switching mixture of two familiar macroeconomic models: A richly parameterized DSGE model and a corresponding BVAR model. We show that the Markov-switching mixture model dominates both individual models and improves the fit considerably. Our estimation indicates that the DSGE model plays an important role only in the late 1970s and the early 1980s. We show how to use the mixture model as a data filter for estimation of the DSGE model when the BVAR model…