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协变量依赖转移概率的马尔可夫状态转换模型最大似然估计AI 解读

This paper considers maximum likelihood (ML) estimation in a large class of models with hidden Markov regimes. We investigate consistency of the ML estimator and local asymptotic normality for the models under general conditions, which allow for autoregressive dynamics in the observable process, Markov regime sequences with covariate‐dependent transition matrices, and possible model misspecification. A Monte Carlo study examines the finite‐sample properties of the ML…

按性别、种族、族裔与出生地划分的美国长期劳动收入结果AI 解读

This paper is part of the Global Repository of Income Dynamics (GRID) project cross‐country comparison of earnings inequality, volatility, and mobility. Using data from the U.S. Census Bureau's Longitudinal Employer‐Household Dynamics (LEHD) infrastructure files, we produce a uniform set of earnings statistics for the U.S. From 1998 to 2019, we find U.S. earnings inequality has increased and volatility has decreased. The combination of increased inequality and reduced…

部分取整概率预测调查中的不确定性度量AI 解读

Although survey‐based point predictions have been found to outperform successful forecasting models, corresponding variance forecasts are frequently diagnosed as heavily distorted. Professional forecasters who report inconspicuously low ex ante variances often produce squared forecast errors that are much larger on average. In this paper, we document the novel stylized fact that this variance misalignment is related to the rounding behavior of survey participants. Rounding…

大型贝叶斯 VAR 的非对称共轭先验AI 解读

Large Bayesian VARs are now widely used in empirical macroeconomics. One popular shrinkage prior in this setting is the natural conjugate prior as it facilitates posterior simulation and leads to a range of useful analytical results. This is, however, at the expense of modeling flexibility, as it rules out cross‐variable shrinkage, that is, shrinking coefficients on lags of other variables more aggressively than those on own lags. We develop a prior that has the best of both…

美国货币政策与财政政策的战略互动AI 解读

We estimate a model in which fiscal and monetary policy obey the targeting rules of distinct policy authorities, with potentially different objective functions. We find: (1) Time‐consistent policy fits U.S. time series at least as well as instrument‐rules‐based behavior; (2) American policies often do not conform to the conventional mix of conservative monetary policy and debt‐stabilizing fiscal policy, although economic agents expect fiscal policy to stabilize debt…

经济不确定性与结构改革：来自股票市场波动的证据AI 解读

Does economic uncertainty promote the implementation of structural reforms? We answer this question using one of the most exhaustive cross‐country panel data sets on reforms in six major areas and measuring economic uncertainty with stock market volatility. To identify causality, we exploit exogenous differential variation in countries' exposure to foreign volatility shocks due to predetermined and time‐invariant bilateral characteristics. Across all specifications, we find…

识别假设的不确定性AI 解读

Uncertainty about the choice of identifying assumptions is common in causal studies, but is often ignored in empirical practice. This paper considers uncertainty over models that impose different identifying assumptions, which can lead to a mix of point‐ and set‐identified models. We propose performing inference in the presence of such uncertainty by generalizing Bayesian model averaging. The method considers multiple posteriors for the set‐identified models and combines…

自然资源资产时变风险建模与气候变化影响AI 解读

A multivariate GARCH model of natural resources is specified to capture the effects of time varying portfolio risk. A special feature of the model is the inclusion of realized volatility for natural resource assets that are available at multiple frequencies as well as being sensitive to sudden changes in climatic conditions. Natural resource portfolios under climate change are simulated from bootstrapping schemes as well as being derived from global climate model…

动态分位数模型的一致设定检验AI 解读

Correct specification of a conditional quantile model implies that a particular conditional moment is equal to zero. We nonparametrically estimate the conditional moment function via series regression and test whether it is identically zero using uniform functional inference. Our approach is theoretically justified via a strong Gaussian approximation for statistics of growing dimensions in a general time series setting. We propose a novel bootstrap method in this nonstandard…

GARCH模型中的R估计量：渐近理论与应用AI 解读

Summary The quasi-maximum likelihood estimation is a commonly-used method for estimating the generalized autoregressive conditional heteroscedastic parameters. However, such estimators are sensitive to outliers and their asymptotic normality is proved under the finite fourth moment assumption on the underlying error distribution. In this paper, we propose a novel class of estimators of the generalized autoregressive conditional heteroscedastic parameters based on ranks of…

具有乘法结构的非平稳非参数回归模型估计AI 解读

Summary This paper presents a multiplicative nonstationary nonparametric regression model which allows for a broad class of nonstationary processes. We propose a three-step estimation procedure to uncover the conditional mean function and establish uniform convergence rates and asymptotic normality of our estimators. The new model can also be seen as a dimension-reduction technique for a general two-dimensional time-varying nonparametric regression model, which is especially…

波动率预测比较中的排序不变条件AI 解读

Summary The paper derives four conditions that guarantee rank-invariance, i.e., that the empirical rankings (based on measurement error-affected variance proxies) of competing volatility forecasts be consistent with the true rankings (based on the unobservable conditional variance). The first three establish bounds beyond which the separation between the forecasts is enough for their rankings not to be affected by the measurement error. The conditions’ ability to establish…

具有门限效应的因子增强预测回归AI 解读

Summary This paper introduces a factor-augmented forecasting regression model in the presence of threshold effects. We consider least squares estimation of the regression parameters and establish asymptotic theories for estimators of both slope coefficients and the threshold parameter. Prediction intervals are also constructed for factor-augmented forecasts. Moreover, we develop a likelihood ratio statistic for tests on the threshold parameter and a sup-Wald test statistic…

渐变均值的偏离是否具有实质意义？基于上确界范数估计量的检验AI 解读

Classical change point analysis aims at (1) detecting abrupt changes in the mean of a possibly nonstationary time series and at (2) identifying regions where the mean exhibits a piecewise constant behavior. In many applications however, it is more reasonable to assume that the mean changes gradually in a smooth way. Those gradual changes may either be nonrelevant (i.e., small), or relevant for a specific problem at hand, and the present paper presents statistical methodology…

短程依赖下方差恒定性的渐近检验AI 解读

We present a novel approach to test for heteroscedasticity of a nonstationary time series that is based on Gini’s mean difference of logarithmic local sample variances. In order to analyse the large sample behaviour of our test statistic, we establish new limit theorems for U-statistics of dependent triangular arrays. We derive the asymptotic distribution of the test statistic under the null hypothesis of a constant variance and show that the test is consistent against a…

凹扩展线性模型中惩罚样条估计量的渐近性质：收敛速率AI 解读

This paper develops a general theory on rates of convergence of penalized spline estimators for function estimation when the likelihood functional is concave in candidate functions, where the likelihood is interpreted in a broad sense that includes conditional likelihood, quasi-likelihood and pseudo-likelihood. The theory allows all feasible combinations of the spline degree, the penalty order and the smoothness of the unknown functions. According to this theory, the…

弱依赖数据半参数回归的 Wilks 定理AI 解读

The empirical likelihood inference is extended to a class of semiparametric models for stationary, weakly dependent series. A partially linear single-index regression is used for the conditional mean of the series given its past, and the present and past values of a vector of covariates. A parametric model for the conditional variance of the series is added to capture further nonlinear effects. We propose suitable moment equations which characterize the mean and variance…

Expectiles define a least squares analogue of quantiles. They have been the focus of a substantial quantity of research in the context of actuarial and financial risk assessment over the last decade. The behaviour and estimation of unconditional extreme expectiles using independent and identically distributed heavy-tailed observations have been investigated in a recent series of papers. We build here a general theory for the estimation of extreme conditional expectiles in…

公司债市场存在风险收益权衡吗？时间序列与横截面证据AI 解读

We provide time-series and cross-sectional evidence on the significance of a risk-return tradeoff in the bond and equity markets. We find a significantly positive intertemporal relation between expected return and risk in the bond market. We also propose novel measures of systematic and idiosyncratic risk for individual corporate bonds and find a significantly positive cross-sectional relation between systematic risk and expected bond returns, whereas there is no significant…

核心盈余：新数据与证据AI 解读

Using a novel dataset, we show that components of firms’ GAAP earnings stemming from ancillary business activities or transitory shocks are significant in frequency and magnitude. These components have grown over time and are dispersed across various sections of the 10-K. Excluding them from GAAP earnings yields a core earnings measure that distinguishes between the recurring and non-recurring components of net income and forecasts future performance. Analysts and market…

大规模数据的时变协方差矩阵估计AI 解读

Time variation is a fundamental problem in statistical and econometric analysis of macroeconomic and financial data. Recently, there has been considerable focus on developing econometric modelling that enables stochastic structural change in model parameters and on model estimation by Bayesian or nonparametric kernel methods. In the context of the estimation of covariance matrices of large dimensional panels, such data requires taking into account time variation, possible…

依赖随机映射的迭代与非线性动态中的外生性AI 解读

We discuss the existence and uniqueness of stationary and ergodic nonlinear autoregressive processes when exogenous regressors are incorporated into the dynamic. To this end, we consider the convergence of the backward iterations of dependent random maps. In particular, we give a new result when the classical condition of contraction on average is replaced with a contraction in conditional expectation. Under some conditions, we also discuss the dependence properties of these…

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…

利用时变波动率识别结构冲击AI 解读

Abstract I propose to identify an SVAR, up to shock ordering, using the autocovariance structure of the squared innovations implied by an arbitrary stochastic process for the shock variances. These higher moments are available without parametric assumptions on the variance process. In contrast, previous approaches exploiting heteroskedasticity rely on the path of innovation covariances, which can only be recovered from the data under specific parametric assumptions on the…