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非参数冲击贝叶斯 VAR 的快速且排序不变推断AI 解读

Summary The shocks that hit macroeconomic models such as Vector Autoregressions (VARs) have the potential to be non‐Gaussian, exhibiting asymmetries and fat tails. This consideration motivates the VAR developed in this paper that uses a Dirichlet process mixture (DPM) to model the reduced‐form shocks. However, we do not follow the obvious strategy of simply modeling the VAR errors with a DPM as this would lead to computationally infeasible Bayesian inference in larger VARs…

结构突变与汇率波动率 GARCH 模型：重新检验与扩展AI 解读

Summary We examine the empirical significance of structural changes concerning generalized autoregressive conditional heteroskedasticity (GARCH) models of exchange rate volatility using out‐of‐sample tests by replicating and carrying out robustness checks on the volatility forecasting study by Rapach and Strauss (Journal of Applied Econometrics, 2008; 23, 65–90). We employ the same econometric models but incorporate recent US dollar daily exchange rates data while also using…

提升式 Hodrick–Prescott 滤波器的适用范围拓展AI 解读

Summary The global financial crisis and Covid‐19 recession have renewed discussion concerning trend‐cycle discovery in macroeconomic data, and boosting has recently upgraded the popular Hodrick‐Prescott filter to a modern machine learning device suited to data‐rich and rapid computational environments. This paper extends boosting's trend determination capability to higher order integrated processes and time series with roots that are local to unity. The theory is established…

利用借记卡交易数据即时预测挪威家庭消费AI 解读

Summary We use a novel data set covering all domestic debit card transactions in physical terminals by Norwegian households, to nowcast quarterly Norwegian household consumption. These card payments data are not subject to revisions and are available weekly without delays, providing a valuable early indicator of household spending. To account for mixed‐frequency data, we estimate various quantile mixed‐data sampling (QMIDAS) regressions using predictors sampled at monthly…

随机系数模型中的最大化似然比检验AI 解读

Summary In a recent contribution, we developed a family of cumulative sum-based change-point tests in the context of a random coefficient autoregressive model of order 1. In the current paper, we complement the results in that contribution by studying the (maximally selected) likelihood ratio statistic. We show that this has power versus breaks occurring even as close as periods from the beginning/end of sample; moreover, the use of quasi-maximum likelihood-based estimates…

平滑转换向量自回归模型的易实施贝叶斯估计AI 解读

Summary We develop a tractable way of estimating the parameters ruling the nonlinearity in the popular smooth transition VAR model, and identify structural shocks using external instruments. This jointly offers an alternative to the option of identifying shocks recursively and calibrating key parameters. In an illustration, we show that monetary policy shocks generate larger effects on economic activity during economic expansions compared to economic recessions. We then…

Summary In this paper, we estimate impulse responses by local projections in high-dimensional settings. We use the desparsified (de-biased) lasso to estimate the high-dimensional local projections, while leaving the impulse response parameter of interest unpenalized. We establish the uniform asymptotic normality of the proposed estimator under general conditions. Finally, we demonstrate small sample performance through a simulation study and consider two canonical…

门槛非线性与民主—增长关系AI 解读

Summary This paper investigates the relationship between democracy and economic growth in the context of a linear index threshold regression model. We first introduce the baseline model with endogeneity and propose a two-step smoothed generalised method of moments estimation method. We establish the consistency and derive the asymptotic distributions of the proposed estimators. We then extend the approach to a dynamic panel context and employ the model to explore the impact…

弱条件下时间序列二阶参数的同时统计推断AI 解读

Strict stationarity is an assumption commonly used in time-series analysis in order to derive asymptotic distributional results for second-order statistics, like sample autocovariances and sample autocorrelations. Focusing on weak stationarity, this paper derives the asymptotic distribution of the maximum of sample autocovariances and sample autocorrelations under weak conditions by using Gaussian approximation techniques. The asymptotic theory for parameter estimators…

非平稳时间序列的最优速率高斯逼近与显式构造AI 解读

Statistical inference for time series such as curve estimation for time-varying models or testing for existence of change-point have garnered significant attention. However, these works are generally restricted to the assumption of independence and/or stationarity at its best. The main obstacle is that the existing Gaussian approximation results for non-stationary processes only provide an existential proof and thus they are difficult to apply. In this paper, we provide two…

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Proxy-VAR 中强外生性的检验AI 解读

Proxy variables have gained widespread prominence as indispensable tools for identifying structural VAR models. Analogous to instrumental variables, proxies need to be exogenous, i.e. uncorrelated with all non-target shocks. Assessing the exogeneity of proxies has traditionally relied on economic arguments rather than statistical tests. We argue that the economic rationale underlying the construction of commonly used proxy variables aligns with a stronger form of exogeneity.…

预测分位数回归中的统计推断AI 解读

This paper studies inference in predictive quantile regressions when the predictive regressor has a near-unit root. We derive asymptotic distributions for the quantile regression estimator and its heteroskedasticity and autocorrelation consistent (HAC) t -statistic in terms of functionals of Ornstein–Uhlenbeck processes. We then propose a switching-fully modified (FM) predictive test for quantile predictability. The proposed test employs an FM style correction with a…

湿能量平衡模型中的极地放大：结构计量估计与检验AI 解读

Poleward transport of atmospheric moisture and heat play major roles in the magnification of warming in poleward latitudes per degree of global warming, a phenomenon known as polar amplification (PA). We derive a time series econometric framework using a system of equations that have error-correction mechanisms restricted across equations to estimate and an identification strategy to recover the parameters of a moist energy balance model (MEBM) similar to those in the recent…

分数 Ornstein–Uhlenbeck 过程的谱密度AI 解读

This paper introduces a novel and easy-to-implement method for accurately approximating the spectral density of discretely sampled fractional Ornstein–Uhlenbeck (fOU) processes. The method offers a substantial reduction in approximation error, particularly within the rough region of the fractional parameter H∈(0, 0.5). This approximate spectral density has the potential to enhance the performance of estimation methods and hypothesis testing that make use of spectral…

基于复合似然的时空过程变点检测AI 解读

This article develops a unified and computationally efficient method for change-point estimation along the time dimension in a nonstationary spatio-temporal process. By modeling a nonstationary spatio-temporal process as a piecewise stationary spatio-temporal process, we consider simultaneous estimation of the number and locations of change-points, and model parameters in each segment. A composite likelihood-based criterion is developed for change-point and parameter…

利用变分贝叶斯与拉普拉斯近似拟合潜在非高斯模型AI 解读

Latent Gaussian models (LGMs) are perhaps the most commonly used class of models in statistical applications. Nevertheless, in areas ranging from longitudinal studies in biostatistics to geostatistics, it is easy to find datasets that contain inherently non-Gaussian features, such as sudden jumps or spikes, that adversely affect the inferences and predictions made using an LGM. These datasets require more general latent non-Gaussian models (LnGMs) that can handle…

多元函数型时间序列的图主成分分析AI 解读

In this paper, we consider multivariate functional time series with a two-way dependence structure: a serial dependence across time points and a graphical interaction among the multiple functions within each time point. We develop the notion of dynamic weak separability, a more general condition than those assumed in literature, and use it to characterize the two-way structure in multivariate functional time series. Based on the proposed weak separability, we develop a…

贝叶斯样条隐马尔可夫模型及其活动与睡眠分析应用AI 解读

B-spline-based hidden Markov models employ B-splines to specify the emission distributions, offering a more flexible modelling approach to data than conventional parametric HMMs. We introduce a Bayesian framework for inference, enabling the simultaneous estimation of all unknown model parameters including the number of states. A parsimonious knot configuration of the B-splines is identified by the use of a trans-dimensional Markov chain sampling algorithm, while model…

用于高维回归的因子增强稀疏直通深度 ReLU 神经网络AI 解读

This paper introduces a Factor Augmented Sparse Throughput (FAST) model that utilizes both latent factors and sparse idiosyncratic components for nonparametric regression. The FAST model bridges factor models on one end and sparse nonparametric models on the other end. It encompasses structured nonparametric models such as factor augmented additive models and sparse low-dimensional nonparametric interaction models and covers the cases where the covariates do not admit factor…

具有隐含结构的序列相关多变量数据 Copula 建模AI 解读

We propose a copula-based extension of the hidden Markov model (HMM) which applies when the observations recorded at each time in the sample are multivariate. The joint model produced by the copula extension allows decoding of the hidden states based on information from multiple observations. However, unlike the case of independent marginals, the copula dependence structure embedded into the likelihood poses additional computational challenges. We tackle the latter using a…

反事实插补：对可预测误差下反事实结果插补方法的评论AI 解读

The measurement of treatment (intervention) effects on a single (or just a few) treated unit(s) based on counterfactuals constructed from artificial controls has become a popular practice in applied statistics and economics since the proposal of the synthetic control method. However, most of the literature has ignored the time-series properties of the data. The work of Gonçalves and Ng fills this gap by proposing a simple correction for existing estimators to take into…

状态空间模型的分块相关伪边际采样器AI 解读

Pseudo Marginal Metropolis-Hastings (PMMH) is a general approach to Bayesian inference when the likelihood is intractable, but can be estimated unbiasedly.Our article develops an efficient PMMH method that scales up better to higher dimensional state vectors than previous approaches.The improvement is achieved by the following innovations.First, a novel block version of PMMH that works with multiple particle filters is proposed.Second, the trimmed mean of the unbiased…

稳健最窄显著区间搜索：中位数多重变点推断AI 解读

We propose Robust Narrowest Significance Pursuit (RNSP), a methodology for detecting localized regions in data sequences which each must contain a change-point in the median, at a prescribed global significance level.RNSP works by fitting the postulated constant model over many regions of the data using a new signmultiresolution sup-norm-type loss, and greedily identifying the shortest intervals on which the constancy is significantly violated.By working with the signs of…

分位数预测最优性检验AI 解读

Quantile forecasts made across multiple horizons have become an important output of many financial institutions, central banks and international organisations. This paper proposes misspecification tests for such quantile forecasts that assess optimality over a set of multiple forecast horizons and/or quantiles. The tests build on multiple Mincer-Zarnowitz quantile regressions cast in a moment equality framework. Our main test is for the null hypothesis of autocalibration, a…