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弱 ARMA 模型适配性的自助谱检验AI 解读

This paper proposes a Cramér–von Mises (CM) test statistic to check the adequacy of weak ARMA models. Without posing a martingale difference assumption on the error terms, the asymptotic null distribution of the CM test is obtained. Moreover, this CM test is consistent, and has nontrivial power against the local alternative of order n−1/2. Due to the unknown dependence of error terms and the estimation effects, a new block-wise random weighting method is constructed to…

扩散过程近似转移概率密度的显式形式AI 解读

A continuous-time diffusion process is very popular in modeling and provides useful tools to analyze particularly, but not restricted to, a variety of economic and financial variables. The transition probability density function (TPDF) of a diffusion process plays an important role in understanding and explaining the dynamics of the process. A new way to find closed-form approximate TPDFs for multivariate diffusions is proposed in this paper. This method can be applied to…

二次卡尔曼滤波器AI 解读

We propose a new filtering and smoothing technique for non-linear state-space models. Observed variables are quadratic functions of latent factors following a Gaussian VAR. Stacking the vector of factors with its vectorized outer-product, we form an augmented state vector whose first two conditional moments are known in closed-form. We also provide analytical formulae for the unconditional moments of this augmented vector. Our new Quadratic Kalman Filter (Qkf) exploits these…

过度识别、非递归与时变系数结构向量自回归的估计AI 解读

This paper provides a general procedure to estimate structural vector autoregressions. The algorithm can be used in constant or time-varying coefficient models, and in the latter case, the law of motion of the coefficients can be linear or nonlinear. It can deal in a unified way with just-identified (recursive or nonrecursive) or overidentified systems where identification restrictions are of linear or of nonlinear form. We study the transmission of monetary policy shocks in…

基于似然比的结构突变时点置信集AI 解读

We propose the use of likelihood-ratio-based confidence sets for the timing of structural breaks in parameters from time series regression models. The confidence sets are valid for the broad setting of a system of multivariate linear regression equations under fairly general assumptions about the error and regressors, and allowing for multiple breaks in mean and variance parameters. In our asymptotic analysis, we determine the critical values for a likelihood ratio test of a…

随机图模型中的局部依赖：刻画、性质与统计推断AI 解读

Summary Dependent phenomena, such as relational, spatial and temporal phenomena, tend to be characterized by local dependence in the sense that units which are close in a well-defined sense are dependent. In contrast with spatial and temporal phenomena, though, relational phenomena tend to lack a natural neighbourhood structure in the sense that it is unknown which units are close and thus dependent. Owing to the challenge of characterizing local dependence and constructing…

维度可能增长的多元时间序列协方差估计与线性过程自助法AI 解读

Multivariate time series present many challenges, especially when they are high dimensional. The paper’s focus is twofold. First, we address the subject of consistently estimating the autocovariance sequence; this is a sequence of matrices that we conveniently stack into one huge matrix. We are then able to show consistency of an estimator based on the so-called flat-top tapers; most importantly, the consistency holds true even when the time series dimension is allowed to…

极值估计中的分部极大化AI 解读

In this paper, we present various iterative algorithms for extremum estimation in cases where direct computation of the extremum estimator or via the Newton–Raphson algorithm is difficult, if not impossible. While the Newton–Raphson algorithm makes use of the full Hessian matrix, which may be difficult to evaluate, our algorithms use parts of the Hessian matrix only, the parts that are easier to compute. We establish consistency and asymptotic efficiency of our iterative…

非平稳方差下的结构变化检验AI 解读

Non‐stationarity of the volatility process reflects low‐frequency volatility changes of an economic time series, and its theoretical and empirical relevance has been widely recognized. We investigate how it affects cumulative sum (CUSUM) related tests for structural change in regression coefficients. Non‐stationary variances generally invalidate standard structural change tests by introducing an infinite‐dimensional nuisance parameter in the limit distribution, and we…

经济理论与预测：来自文献的经验AI 解读

Does economic theory help in forecasting key macroeconomic variables? This article aims to provide some insight into the question by drawing lessons from the literature. The definition of ‘economic theory’ includes a broad range of examples, such as accounting identities, disaggregation and spatial restrictions when forecasting aggregate variables, cointegration and forecasting with dynamic stochastic general equilibrium (DSGE) models. We group the lessons into three themes.…

用于测度与预测的似然动态因子分析AI 解读

We present new results for the likelihood‐based analysis of the dynamic factor model. The latent factors are modelled by linear dynamic stochastic processes. The idiosyncratic disturbance series are specified as autoregressive processes with mutually correlated innovations. The new results lead to computationally efficient procedures for the estimation of the factors and for the parameter estimation by maximum likelihood methods. We also present the implications of our…

个体固定效应线性动态面板模型的自助法推断AI 解读

This paper’s main contribution is to propose and theoretically justify the application of bootstrap methods for inference in autoregressive panel data models with fixed effects. Whereas the focus of the existing literature has been on bias correcting the standard fixed effects OLS estimator (due to the well known incidental parameter bias), our focus here is on improving the quality of inference by relying on the bootstrap instead of the standard normal distribution when…

三遍回归滤波：利用大量预测变量进行预测的新方法AI 解读

We forecast a single time series using many predictor variables with a new estimator called the three-pass regression filter (3PRF). It is calculated in closed form and conveniently represented as a set of ordinary least squares regressions. 3PRF forecasts are consistent for the infeasible best forecast when both the time dimension and cross section dimension become large. This requires specifying only the number of relevant factors driving the forecast target, regardless of…

大型投资组合的风险AI 解读

The risk of a large portfolio is often estimated by substituting a good estimator of the volatility matrix. However, the accuracy of such a risk estimator is largely unknown. We study factor-based risk estimators under a large amount of assets, and introduce a high-confidence level upper bound (H-CLUB) to assess the estimation. The H-CLUB is constructed using the confidence interval of risk estimators with either known or unknown factors. We derive the limiting distribution…

因子增广回归预测：频率学派模型平均方法AI 解读

This paper considers forecast combination with factor-augmented regression. In this framework, a large number of forecasting models are available, varying by the choice of factors and the number of lags. We investigate forecast combination across models using weights that minimize the Mallows and the leave-h-out cross validation criteria. The unobserved factor regressors are estimated by principle components of a large panel with N predictors over T periods. With these…

高维向量自回归的 oracle 不等式AI 解读

This paper establishes non-asymptotic oracle inequalities for the prediction error and estimation accuracy of the LASSO in stationary vector autoregressive models. These inequalities are used to establish consistency of the LASSO even when the number of parameters is of a much larger order of magnitude than the sample size. We also state conditions under which no relevant variables are excluded.

广义风险溢价AI 解读

This paper develops an optimal trading strategy explicitly linked to an agent׳s preferences and assessment of the distribution of asset returns. The price of this strategy is a portfolio of implied moments, and its expected excess returns naturally accommodate compensation for higher-order moment risk. Variance risk and the equity premium approximate it to first order and it nests cross-sectional asset pricing models such as the linear Capital Asset Pricing Model (CAPM). An…

连续响应观测驱动时间序列模型的信息论最优性AI 解读

We investigate information-theoretic optimality properties of the score function of the predictive likelihood as a device for updating a real-valued time-varying parameter in a univariate observation-driven model with continuous responses. We restrict our attention to models with updates of one lag order. The results provide theoretical justification for a class of score-driven models which includes the generalized autoregressive conditional heteroskedasticity model as a…

使用无偏似然估计时的 MCMC 高效实现AI 解读

When an unbiased estimator of the likelihood is used within a Metropolis–Hastings chain, it is necessary to trade off the number of Monte Carlo samples used to construct this estimator against the asymptotic variances of the averages computed under this chain. Using many Monte Carlo samples will typically result in Metropolis–Hastings averages with lower asymptotic variances than the corresponding averages that use fewer samples; however, the computing time required to…

向量误差修正模型的自动化估计AI 解读

Model selection and associated issues of post-model selection inference present well known challenges in empirical econometric research. These modeling issues are manifest in all applied work but they are particularly acute in multivariate time series settings such as cointegrated systems where multiple interconnected decisions can materially affect the form of the model and its interpretation. In cointegrated system modeling, empirical estimation typically proceeds in a…

渐近一致最强面板单位根检验：备择异质性的影响AI 解读

In a Gaussian, heterogeneous, cross-sectionally independent panel with incidental intercepts, Moon, Perron, and Phillips (2007, Journal of Econometrics 141, 416–459) present an asymptotic power envelope yielding an upper bound to the local asymptotic power of unit root tests. In case of homogeneous alternatives this envelope is known to be sharp, but this paper shows that it is not attainable for heterogeneous alternatives. Using limit experiment theory we derive a sharp…

AR(1) 模型中 t 统计量的二阶展开AI 解读

The purpose of this paper is to differentiate between several asymptotically valid methods for confidence set construction for the autoregressive coefficient in AR(1) models. We show that the nonparametric grid bootstrap procedure suggested by Hansen (1999, Review of Economics and Statistics 81, 594–607) achieves a second order refinement in the local-to-unity asymptotic approach when compared with a modified version of Stock’s (1991, Journal of Monetary Economics 28,…

横截面与时间维度均大时动态面板系统 GMM 的渐近性质AI 解读

In this paper, we derive the asymptotic properties of the system generalized method of moments (GMM) estimator in dynamic panel data models with individual and time effects when both N and T , the dimensions of cross-section and time series, are large. Specifically, we show that the two-step system GMM estimator is consistent when a suboptimal weighting matrix where off-diagonal blocks are set to zero is used. Such consistency results theoretically support the use of the…

We bring together some recent advances in the literature on vector autoregressive moving-average models, creating a simple specification and estimation strategy for the cointegrated case. We show that in this case with fixed initial values there exists a so-called final moving-average representation. We prove that the specification strategy is consistent. The performance of the proposed method is investigated via a Monte Carlo study and a forecasting exercise for US interest…