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LOCAL LINEAR FITTING UNDER NEAR EPOCH DEPENDENCE

近历元依赖条件下的局部线性拟合

Local linear fitting of nonlinear processes under strong (i.e., α-) mixing conditions has been investigated extensively. However, it is often a difficult step to establish the strong mixing of a nonlinear process composed of several parts such as the popular combination of autoregressive moving average (ARMA) and generalized autoregressive conditionally heteroskedastic (GARCH) models. In this paper we develop an asymptotic theory of local linear fitting for near epoch…

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PERMANENT-TRANSITORY DECOMPOSITIONS UNDER WEAK EXOGENEITY

弱外生性下的永久—暂时分解

This note shows that the orthogonal shocks obtained by imposing a recursive structure on the contemporaneous impacts of the errors in a vector-error correction model (VECM) are related to the orthogonal permanent-transitory shocks in a Gonzalo and Ng (2001, Journal of Economic Dynamics and Control 25, 1527–1546) decomposition provided some of the variables in the system are weakly exogenous. Specifically, when there are as many weakly exogenous variables as common trends and…

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COMMON STOCHASTIC TRENDS AND AGGREGATION IN HETEROGENEOUS PANELS

异质面板中的共同随机趋势与聚合

In nonstationary heterogeneous panels where the number of units is finite and where each unit cointegrates, a large number of conditions needs to be satisfied for cointegration to be preserved in the aggregate relationship. In reality, the conditions most likely will not hold. This paper takes a closer look at what happens when the conditions are violated. In this case, the question of whether an aggregate relationship is observationally equivalent to a cointegrating…

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Common Periodic Correlation Features and the Interaction of Stocks and Flows in Daily Airport Data

共同周期相关特征与机场日度数据中的存量和流量互动

We propose the multivariate representation of univariate and bivariate (possibly nonstationary) periodic models as a benchmark for the imposition of common periodic correlation (CPC) feature restrictions to obtain parameter parsimony. CPCs are short-run common dynamic features that co-vary across the different days of the week and possibly also across weeks and that can be common across different time series. We also show how periodic models can be used to describe…

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A Note on Common Cycles, Common Trends, and Convergence

关于共同周期、共同趋势与收敛的札记

This article compares and contrasts structural time series models and the common features methodology. The way in which trends are handled is highlighted by describing a recent structural time series model that allows convergence to a common growth path. Postsample data are used to test its forecasting performance for income per head in U.S. regions. A test for common cycles is proposed, its asymptotic distribution is given, and small-sample properties are studied by Monte…

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Co-Breaking

协同结构突变(Co-breaking)

This article has two aims. First, we provide a synopsis of the literature on co-breaking that has developed in several, seemingly disconnected, strands. We establish a consistent terminology, collect theoretical results, delimit co-breaking to cointegration and common features, and review recent contributions to co-breaking regressions and the budding analysis of co-breaking rank. Second, we present new results in the field, particularly, on the importance of co-breaking for…

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Determining the Number of Primitive Shocks in Factor Models

因子模型中原始冲击数量的确定

A widely held but untested assumption underlying macroeconomic analysis is that the number of shocks driving economic fluctuations, q, is small. In this article we associate q with the number of dynamic factors in a large panel of data. We propose a methodology to determineq without having to estimate the dynamic factors. We first estimate a VAR in r static factors, where the factors are obtained by applying the method of principal components to a large panel of data, then…

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A Multivariate Generalized Orthogonal Factor GARCH Model

多元广义正交因子 GARCH 模型

We propose a factor generalized autoregressive conditional heteroscedasticity (GARCH) model and develop test procedures for checking the correctness of the number of factors. Maximum likelihood estimation of the model is straightforward once computationally simple preliminary estimates are obtained. Motivated by the empirical application of the article, a mixture of Gaussian distributions is considered in addition to the conventional Gaussian likelihood. Interestingly, some…

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A More Timely and Useful Index of Leading Indicators

更及时、更有用的先行指标指数

Effectively predicting cyclical movements in the economy is a major challenge. The U.S. leading index (LI) has long been used to analyze and predict economic fluctuations. We describe and test a new procedure for making the LI more timely. The new LI significantly outperforms its older counterpart. It offers substantial gains in real-time, out-of-sample forecasts of changes in aggregate economic activity (real GDP, the index of coincident indicators, and industrial…

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Forecasting the Volatility of Australian Stock Returns

澳大利亚股票收益的波动率预测

This article develops multivariate factor models for forecasting volatility in Australian stocks. We suggest estimation procedures for approximate factor models that are robust to jumps when the cross-sectional dimension is not very large, and also work with volatility measures that have been constructed so that they contain no jump components. Out-of-sample forecast analysis shows that multivariate factor models of volatility outperform univariate models, but there is…

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Exploring the international linkages of the euro area: a global VAR analysis

欧元区的国际联系:全球 VAR 分析

Abstract This paper presents a quarterly global model combining individual country vector error‐correcting models in which the domestic variables are related to the country‐specific foreign variables. The global VAR (GVAR) model is estimated for 26 countries, the euro area being treated as a single economy, over the period 1979–2003. It advances research in this area in a number of directions. In particular, it provides a theoretical framework where the GVAR is derived as an…

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Nonlinear autoregressive leading indicator models of output in G‐7 countries

七国集团产出的非线性自回归领先指标模型

Abstract This paper studies linear and nonlinear autoregressive leading indicator models of business cycles in G‐7 countries. Our models use the spread between short‐term and long‐term interest rates as leading indicators for GDP. We examine data admissibility by determining whether these models have the ability to produce time series with classical cycles that resemble the observed classical cycles in the data, and then we ask whether this data admissibility lends itself to…

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Codependence in cointegrated autoregressive models

协整自回归模型中的共同依赖周期

Abstract This paper investigates codependent cycles, i.e., transitory components that react to common stimuli in a similar, although not necessarily synchronous fashion. Unlike previous studies, the methodology of this paper allows FIML estimation of the restricted VAR/VECM and therefore the extraction of the unobserved codependent cyclical components via a Beveridge‐Nelson decomposition. It is further shown that the number and order of cofeature combinations that yield the…

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An evaluation of the forecasts of the federal reserve: a pooled approach

美联储预测的评价:跨预测期限的合并方法

Abstract The Federal Reserve Greenbook forecasts of real GDP, inflation and unemployment are analysed for the period 1974–1997. We consider whether these forecasts exhibit systematic bias, and whether efficient use is made of information, that is, whether revisions to these forecasts over time are predictable. Rather than analyse the forecasts separately for each horizon of interest, we discuss and implement methods that pool information over horizons. We conclude that there…

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Empirical Bayesian Analysis for Computer Experiments Involving Finite-Difference Codes

涉及有限差分程序的计算机实验经验贝叶斯分析

Computer experiments are increasingly used in scientific investigations as substitutes for physical experiments in cases where the latter are difficult or impossible to perform. A computer experiment consists of several runs of a computer model or “code” for the purpose of better understanding the input → output relationship. One practical difficulty in the use of these models is that a single run may require a prohibitive amount of computational resources in some…

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A Nonstationary Negative Binomial Time Series With Time-Dependent Covariates

含时变协变量的非平稳负二项时间序列模型

Boston Harbor has a history of poor water quality, including contamination by enteric pathogens. We conduct a statistical analysis of data collected by the Massachusetts Water Resources Authority (MWRA) between 1996 and 2002 to evaluate the effects of court-mandated improvements in sewage treatment. Motivated by the ineffectiveness of standard Poisson mixture models and their zero-inflated counterparts, we propose a new negative binomial model for time series of Enterococcus…

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Small-Area Estimation With State–Space Models Subject to Benchmark Constraints

满足基准约束的状态空间小区域估计

This article shows how to benchmark small-area estimators, produced by fitting separate state–space models within the areas, to aggregates of the survey direct estimators within a group of areas. State–space models are used by the U.S. Bureau of Labor Statistics (BLS) for the production of all of the monthly employment and unemployment estimates in census divisions and the states. Computation of the benchmarked estimators and their variances is accomplished by incorporating…

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Generalized Exponential Predictors for Time Series Forecasting

用于时间序列预测的广义指数预测器

We consider the problem of prediction for stationary and nonstationary univariate time series using a modification suggested by the usual exponentially weighted moving average method. The modification leads to a class of general exponential predictors that can improve on the usual finite approximations to an infinite autoregressive process. We provide the theoretical justifications and suggest a class of predictors that covers modified and finite autoregressive fits as…

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A Monte Carlo Approach to Filtering for a Class of Marked Doubly Stochastic Poisson Processes

一类带标记双随机 Poisson 过程的蒙特卡洛滤波

Marked doubly stochastic Poisson processes are a particular type of marked point processes that are characterized by the number of events in any time interval as being conditionally Poisson distributed, given another positive stochastic process called intensity. Here we consider a subclass of these processes in which the intensity is assumed to be a deterministic function of another nonexplosive marked point process. In particular, we will investigate an intensity jump…

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