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因子分析中更多数据总是更好吗？AI 解读

Factors estimated from large macroeconomic panels are being used in an increasing number of applications. However, little is known about how the size and the composition of the data affect the factor estimates. In this paper, we question whether it is possible to use more series to extract the factors, and yet the resulting factors are less useful for forecasting, and the answer is yes. Such a problem tends to arise when the idiosyncratic errors are cross-correlated. It can…

VAR、共同因子与均衡经济周期模型的实证验证AI 解读

Equilibrium business cycle models have typically less shocks than variables. As pointed out by Altug, 1989 and Sargent, 1989, if variables are measured with error, this characteristic implies that the model solution for measured variables has a factor structure. This paper compares estimation performance for the impulse response coefficients based on a VAR approximation to this class of models and an estimation method that explicitly takes into account the restrictions…

摘要暂缺

利率期限结构与宏观经济的联合模型AI 解读

Abstract We present and estimate a continuous time term structure model that incorporates observable macroeconomic variables and latent variables with a clear macroeconomic interpretation. Our model is able to accurately describe the joint dynamics for US macroeconomic variables and the yield curve. However, the observable variables do not explain the long end of the term structure. Central tendencies of these macroeconomic variables do a much better job in this respect.…

利用可逆跳跃算法输出高效估计贝叶斯因子AI 解读

We propose a class of estimators of the Bayes factor which is based on an extension of the bridge sampling identity of Meng & Wong (1996) and makes use of the output of the reversible jump algorithm of Green (1995). Within this class we give the optimal estimator and also a suboptimal one which may be simply computed on the basis of the acceptance probabilities used within the reversible jump algorithm for jumping between models. The proposed estimators are very easily…

金融因子、宏观信息与利率期限结构的预期理论AI 解读

In this paper we develop on the VAR framework, originally proposed by Campbell and Shiller (J. Political Econom 95 (1987) 1062) to evaluate the Expectations Theory, along three dimensions: the use of a testing method based on a real-time procedure, the measurement of the risk premium, the specification of the implicit monetary policy maker’s reaction function. We use financial factors and macroeconomic information to construct a test of the theory based on simulating…

监督主成分预测AI 解读

In regression problems where the number of predictors greatly exceeds the number of observations, conventional regression techniques may produce unsatisfactory results. We describe a technique called supervised principal components that can be applied to this type of problem. Supervised principal components is similar to conventional principal components analysis except that it uses a subset of the predictors selected based on their association with the outcome. Supervised…

函数型主成分分析的性质AI 解读

Summary Functional data analysis is intrinsically infinite dimensional; functional principal component analysis reduces dimension to a finite level, and points to the most significant components of the data. However, although this technique is often discussed, its properties are not as well understood as they might be. We show how the properties of functional principal component analysis can be elucidated through stochastic expansions and related results. Our approach…

函数主成分分析的惩罚样条模型AI 解读

Summary We propose an iterative estimation procedure for performing functional principal component analysis. The procedure aims at functional or longitudinal data where the repeated measurements from the same subject are correlated. An increasingly popular smoothing approach, penalized spline regression, is used to represent the mean function. This allows straightforward incorporation of covariates and simple implementation of approximate inference procedures for…

政府债券收益率期限结构预测AI 解读

Despite powerful advances in yield curve modeling in the last twenty years, comparatively little attention has been paid to the key practical problem of forecasting the yield curve. In this paper we do so. We use neither the no-arbitrage approach, which focuses on accurately fitting the cross section of interest rates at any given time but neglects time-series dynamics, nor the equilibrium approach, which focuses on time-series dynamics (primarily those of the instantaneous…

利用间距检验面板数据的横截面相关性AI 解读

This article provides tools for characterizing the extent of cross-section correlation in panel data when we do not know a priori how many and which series are correlated. Our tests are based on the probability integral transformation of the ordered correlations. We first split the transformed correlations by their size into two groups, then evaluate the variance ratio of the two subsamples. The problem of testing cross-section correlation thus becomes one of identifying…

纵向数据的函数型线性回归分析AI 解读

We propose nonparametric methods for functional linear regression which are designed for sparse longitudinal data, where both the predictor and response are functions of a covariate such as time. Predictor and response processes have smooth random trajectories, and the data consist of a small number of noisy repeated measurements made at irregular times for a sample of subjects. In longitudinal studies, the number of repeated measurements per subject is often small and may…

允许横截面依赖的购买力平价面板平稳性检验AI 解读

We investigate the purchasing power parity (PPP) hypothesis for a group of 17 countries using a new panel-based test of stationarity that allows for arbitrary cross-sectional dependence. We treat the short-run time series dynamics nonparametrically and thus avoid the need to fit separate, and potentially misspecified, models for the individual series. The statistic is simple to compute and uses standard normal critical values, even in the presence of a wide range of…

多元纵向数据的动态相关AI 解读

Nonparametric methodology for longitudinal data analysis is becoming increasingly popular. The analysis of multivariate longitudinal data, where data on several time courses are recorded for each subject, has received considerably less attention, despite its importance for practical data analysis. In particular, there is a need for measures and estimates to capture dependency between the components of vector-valued longitudinal data. We propose and analyze a simple and…

摘要暂缺

存在共同冲击的横截面回归AI 解读

This paper considers regression models for cross-section data that exhibit cross-section dependence due to common shocks, such as macroeconomic shocks. The paper analyzes the properties of least squares (LS) estimators in this context. The results of the paper allow for any form of cross-section dependence and heterogeneity across population units. The probability limits of the LS estimators are determined, and necessary and sufficient conditions are given for consistency.…

不假定参数已识别的 GMM 参数检验AI 解读

We propose a generalized method of moments (GMM) Lagrange multiplier statistic, i.e., the K statistic, that uses a Jacobian estimator based on the continuous updating estimator that is asymptotically uncorrelated with the sample average of the moments. Its asymptotic χ-super-2 distribution therefore holds under a wider set of circumstances, like weak instruments, than the standard full rank case for the expected Jacobian under which the asymptotic χ-super-2 distributions of…

主成分的应用：利用大型数据集分析货币政策AI 解读

Abstract The empirical analysis of monetary policy requires the construction of instruments for future expected inflation. Dynamic factor models have been applied rather successfully to inflation forecasting. In fact, two competing methods have recently been developed to estimate large‐scale dynamic factor models based, respectively, on static and dynamic principal components. This paper combines the econometric literature on dynamic principal components and the empirical…

稀疏纵向数据的函数型数据分析AI 解读

We propose a nonparametric method to perform functional principal components analysis for the case of sparse longitudinal data. The method aims at irregularly spaced longitudinal data, where the number of repeated measurements available per subject is small. In contrast, classical functional data analysis requires a large number of regularly spaced measurements per subject. We assume that the repeated measurements are located randomly with a random number of repetitions for…

函数型自适应模型估计AI 解读

In this article we are interested in modeling the relationship between a scalar, Y, and a functional predictor, X(t). We introduce a highly flexible approach called functional adaptive model estimation (FAME), which extends generalized linear models (GLMs), generalized additive models (GAMs), and projection pursuit regression (PPR) to handle functional predictors. The FAME approach can model any of the standard exponential family of response distributions that are assumed…

多元非平稳时间序列的 SLEX 分析AI 解读

We develop a procedure for analyzing multivariate nonstationary time series using the SLEX library (smooth localized complex exponentials), which is a collection of bases, each basis consisting of waveforms that are orthogonal and time-localized versions of the Fourier complex exponentials. Under the SLEX framework, we build a family of multivariate models that can explicitly characterize the time-varying spectral and coherence properties. Every model has a spectral…

含不可观测产品特征的特征价格指数：个人电脑应用AI 解读

We show that hedonic price indexes may be biased when not all product characteristics are observed. We derive two primary sources of bias. The first source is a classical selection problem that arises due to changes over time in the values of unobserved characteristics. The second comes from changes in the implicit prices of unobserved characteristics. Next we show that the bias can be corrected for under fairly general assumptions using extensions of factor analysis…

动态因子模型中的贝叶斯模型平均预测AI 解读

This paper considers the problem of forecasting in dynamic factor models using Bayesian model averaging. Theoretical justifications for averaging across models, as opposed to selecting a single model, are given. Practical methods for implementing Bayesian model averaging with factor models are described. These methods involve algorithms which simulate from the space defined by all possible models. We discuss how these simulation algorithms can also be used to select the…

平稳时间序列因果因子模型的识别AI 解读

We consider identification of a class of dynamic factor model. We show that identification holds under reasonably general conditions. The results apply to many of the dynamic factor models that have appeared in the literature and to many worthwhile generalizations of those models. Copyright Royal Economic Socciety 2004