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ASYMPTOTICS OF SPECTRAL DENSITY ESTIMATES

谱密度估计的渐近理论

We consider nonparametric estimation of spectral densities of stationary processes, a fundamental problem in spectral analysis of time series. Under natural and easily verifiable conditions, we obtain consistency and asymptotic normality of spectral density estimates. Asymptotic distribution of maximum deviations of the spectral density estimates is also derived. The latter result sheds new light on the classical problem of tests of white noises.

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NONSTATIONARITY-EXTENDED WHITTLE ESTIMATION

扩展至非平稳情形的 Whittle 估计

For long memory time series models with uncorrelated but dependent errors, we establish the asymptotic normality of the Whittle estimator under mild conditions. Our framework includes the widely used fractional autoregressive integrated moving average models with generalized autoregressive conditional heteroskedastic-type innovations. To cover nonstationary fractionally integrated processes, we extend the idea of Abadir, Distaso, and Giraitis (2007, Journal of Econometrics…

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ASYMPTOTIC THEORY FOR EMPIRICAL SIMILARITY MODELS

经验相似性模型的渐近理论

We consider the stochastic process $Y_t = \sum\nolimits_{i < t} {s_w } (x_t ,x_i)Y_i /\sum\nolimits_{i < t} {s_w } (x_t ,x_i) + \varepsilon _t$ , t = 2, …, n , where s w ( x t , x i ) is a similarity function between the t th and the i th observations and { ε t } is a random disturbance term. This process was originally axiomatized by Gilboa, Lieberman, and Schmeidler (2006, Review of Economics and Statistics 88, 433–444) as a way by which agents, or…

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IMPROVED AND EXTENDED END-OF-SAMPLE INSTABILITY TESTS USING A FEASIBLE QUASI-GENERALIZED LEAST SQUARES PROCEDURE

利用可行准广义最小二乘改进和扩展样本末端不稳定性检验

This paper extends the Andrews (2002, Econometrica 71, 1661–1694) and Andrews and Kim (2006, Journal of Business & Economic Statistics 24, 379–394) ordinary least squares–based end-of-sample instability tests for linear regression models. The author proposes to quasi-difference the data first using a consistent estimate of the sum of the autoregressive coefficients of the error process and then test for the end-of-sample instability. For the cointegration model, the feasible…

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SUP-TESTS FOR LINEARITY IN A GENERAL NONLINEAR AR(1) MODEL

一般非线性 AR(1) 模型的线性性上确界检验

We consider linearity testing in a general class of nonlinear time series models of order one, involving a nonnegative nuisance parameter that (a) is not identified under the null hypothesis and (b) gives the linear model when equal to zero. This paper studies the asymptotic distribution of the likelihood ratio test and asymptotically equivalent supremum tests. The asymptotic distribution is described as a functional of chi-square processes and is obtained without imposing a…

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A REPRESENTATION THEORY FOR POLYNOMIAL COFRACTIONALITY IN VECTOR AUTOREGRESSIVE MODELS

向量自回归模型中多项式协分数关系的表示理论

We extend the representation theory of the autoregressive model in the fractional lag operator of Johansen (2008, Econometric Theory 24, 651–676). A recursive algorithm for the characterization of cofractional relations and the corresponding adjustment coefficients is given, and it is shown under which condition the solution of the model is fractional of order d and displays cofractional relations of order d − b and polynomial cofractional relations of order d − 2 b ,…, d −…

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PANEL UNIT ROOT TESTS WITH CROSS-SECTION DEPENDENCE: A FURTHER INVESTIGATION

存在截面依赖的面板单位根检验:进一步研究

An effective way to control for cross-section correlation when conducting a panel unit root test is to remove the common factors from the data. However, there remain many ways to use the defactored residuals to construct a test. In this paper, we use the panel analysis of nonstationarity in idiosyncratic and common components (PANIC) residuals to form two new tests. One estimates the pooled autoregressive coefficient, and one simply uses a sample moment. We establish their…

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Estimating time variation in measurement error from data revisions: an application to backcasting and forecasting in dynamic models

利用数据修订估计时变测量误差:动态模型中的回溯估计与预测

Abstract Over time, economic statistics are refined. This implies that data measuring recent economic events are typically less reliable than older data. Such time variation in measurement error affects optimal forecasts. Measurement error, and its time variation, are of course unobserved. Our contribution is to show how estimates of these can be recovered from the variance of revisions to data using a behavioural model of the statistics agency. We illustrate the gains in…

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Long‐run relations in European electricity prices

欧洲电力价格的长期关系

Abstract This paper analyses the interdependencies existing in wholesale electricity prices in six major European countries. The results of a robust multivariate long‐run dynamic analysis reveal the presence of four highly integrated central European markets (France, Germany, the Netherlands and Austria). The trend shared by these four electricity markets appears to be common also to gas prices, but not to oil prices. The existence of a common long‐term dynamics among…

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Responses to monetary policy shocks in the east and the west of Europe: a comparison

欧洲东部与西部对货币政策冲击的响应比较

Abstract This paper compares impulse responses to monetary policy shocks in the euro area countries before the EMU and in the New Member States (NMS) from central–eastern Europe. We mitigate the small‐sample problem, which is especially acute for the NMS, by using a Bayesian estimation that combines information across countries. The impulse responses in the NMS are broadly similar to those in the euro area countries. There is some evidence that in the NMS, which have had…

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Profile quasi-maximum likelihood estimation of partially linear spatial autoregressive models

部分线性空间自回归模型的剖面准最大似然估计

We propose profile quasi-maximum likelihood estimation of spatial autoregressive models that are partially linear. The rate of convergence of the spatial parameter estimator depends on some general features of the spatial weight matrix of the model. The estimators of other finite-dimensional parameters in the model have the regular -rate of convergence and the estimator of the nonparametric component is consistent but with different restrictions on the choice of bandwidth…

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Efficient estimation of the semiparametric spatial autoregressive model

半参数空间自回归模型的有效估计

Efficient semiparametric and parametric estimates are developed for a spatial autoregressive model, containing non-stochastic explanatory variables and innovations suspected to be non-normal. The main stress is on the case of distribution of unknown, nonparametric, form, where series nonparametric estimates of the score function are employed in adaptive estimates of parameters of interest. These estimates are as efficient as the ones based on a correct form, in particular…

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GMM estimation of spatial autoregressive models with unknown heteroskedasticity

未知异方差下空间自回归模型的 GMM 估计

In the presence of heteroskedastic disturbances, the MLE for the SAR models without taking into account the heteroskedasticity is generally inconsistent. The 2SLS estimates can have large variances and biases for cases where regressors do not have strong effects. In contrast, GMM estimators obtained from certain moment conditions can be robust. Asymptotically valid inferences can be drawn with consistently estimated covariance matrices. Efficiency can be improved by…

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A generalized nonlinear IV unit root test for panel data with cross-sectional dependence

允许截面依赖的面板数据广义非线性工具变量单位根检验

This paper proposes a unit root test for panel data with cross-sectional dependence. The test generalizes the nonlinear IV unit root test of Chang (2002) to the case where there exist some common factors in panels. The main idea is to eliminate the cross-sectional dependence through the method of principal components as in Bai and Ng (2004) and then apply Chang's test to the treated data. Under certain conditions, the proposed test is consistent and has a standard normal…

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A semiparametric cointegrating regression: Investigating the effects of age distributions on consumption and saving

半参数协整回归:年龄分布对消费与储蓄的影响

We consider a semiparametric cointegrating regression model, for which the disequilibrium error is further explained nonparametrically by a functional of distributions changing over time. The paper develops the statistical theories of the model. We propose an efficient econometric estimator and obtain its asymptotic distribution. A specification test for the model is also investigated. The model and methodology are applied to analyze how an aging population in the US…

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Nonparametric transfer function models

非参数传递函数模型

In this paper a class of nonparametric transfer function models is proposed to model nonlinear relationships between 'input' and 'output' time series. The transfer function is smooth with unknown functional forms, and the noise is assumed to be a stationary autoregressive-moving average (ARMA) process. The nonparametric transfer function is estimated jointly with the ARMA parameters. By modeling the correlation in the noise, the transfer function can be estimated more…

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Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances

含自回归与异方差扰动的空间自回归模型:设定与估计

This study develops a methodology of inference for a widely used Cliff-Ord type spatial model containing spatial lags in the dependent variable, exogenous variables, and the disturbance terms, while allowing for unknown heteroskedasticity in the innovations. We first generalize the GMM estimator suggested in (Kelejian and Prucha, 1998) and (Kelejian and Prucha, 1999) for the spatial autoregressive parameter in the disturbance process. We also define IV estimators for the…

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Indirect inference for dynamic panel models

动态面板模型的间接推断

Maximum likelihood (ML) estimation of the autoregressive parameter of a dynamic panel data model with fixed effects is inconsistent under fixed time series sample size and large cross section sample size asymptotics. This paper proposes a general, computationally inexpensive method of bias reduction that is based on indirect inference, shows unbiasedness and analyzes efficiency. Monte Carlo studies show that our procedure achieves substantial bias reductions with only mild…

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Common breaks in means and variances for panel data

面板数据均值与方差的共同断点

This paper establishes the consistency of the estimated common break point in panel data. Consistency is obtainable even when a regime contains a single observation, making it possible to quickly identify the onset of a new regime. We also propose a new framework for developing the limiting distribution for the estimated break point, and show how to construct confidence intervals. The least squares method is used for estimating breaks in means and the quasi-maximum…

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Derivative Pricing With Wishart Multivariate Stochastic Volatility

Wishart多变量随机波动率下的衍生品定价

This paper deals with the pricing of derivatives written on several underlying assets or factors satisfying a multivariate model with Wishart stochastic volatility matrix. This multivariate stochastic volatility model leads to a closed-form solution for the conditional Laplace transform, and quasi-explicit solutions for derivative prices written on more than one asset or underlying factor. Two examples are presented: (i) a multiasset extension of the stochastic volatility…

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Analyzing the Term Structure of Interest Rates Using the Dynamic Nelson–Siegel Model With Time-Varying Parameters

使用时变参数动态Nelson–Siegel模型分析利率期限结构

In this article we introduce time-varying parameters in the dynamic Nelson–Siegel yield curve model for the simultaneous analysis and forecasting of interest rates of different maturities. The Nelson–Siegel model has been recently reformulated as a dynamic factor model with vector autoregressive factors. We extend this framework in two directions. First, the factor loadings in the Nelson–Siegel yield model depend on a single loading parameter that we treat as the fourth…

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ECF estimation of Markov models where the transition density is unknown

转移密度未知的马尔可夫模型的经验特征函数估计

In this paper, we consider the estimation of Markov models where the transition density is unknown. The approach we propose is based on the empirical characteristic function estimation procedure with an approximate optimal weight function. The approximate optimal weight function is obtained through an Edgeworth/Gram--Charlier expansion of the logarithmic transition density of the Markov process. We derive the estimating equations and demonstrate that they are similar to the…

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Improving robust model selection tests for dynamic models

改进动态模型的稳健模型选择检验

We propose an improved model selection test for dynamic models using a new asymptotic approximation to the sampling distribution of a new test statistic. The model selection test is applicable to dynamic models with very general selection criteria and estimation methods. Since our test statistic does not assume the exact form of a true model, the test is essentially non-parametric once competing models are estimated. For the unknown serial correlation in data, we use a…

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Theory and inference for a Markov switching GARCH model

马尔可夫切换 GARCH 模型的理论与推断

We develop a Markov-switching GARCH model (MS-GARCH) wherein the conditional mean and variance switch in time from one GARCH process to another. The switching is governed by a hidden Markov chain. We provide sufficient conditions for geometric ergodicity and existence of moments of the process. Because of path dependence, maximum likelihood estimation is not feasible. By enlarging the parameter space to include the state variables, Bayesian estimation using a Gibbs sampling…

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