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利用线性模型与人工神经网络评估期限结构信息的模型选择方法AI 解读

We take a model selection approach to the question of whether forward interest rates are useful in predicting future spot rates, using a variety of out-of-sample forecast-based model selection criteria: forecast mean squared error, forecast direction accuracy, and forecast-based trading system profitability. We also examine the usefulness of a class of novel prediction models called 'artificial neural networks,' and investigate the issue of appropriate window sizes for…

依赖过程驱动的非线性动态中的适应性学习AI 解读

The authors provide a convergence theory for adaptive learning algorithms useful for the study of learning by economic agents. Their results extend the framework of L. Ljung previously utilized by A. Marcet-T. J. Sargent and M. Woodford by permitting nonlinear laws of motion driven by stochastic processes that may exhibit moderate dependence, such as mixing and mixingale processes. The authors draw on previous work by H. J. Kushner and D. S. Clark to provide readily…

最小渐近协方差矩阵估计量的确定AI 解读

We give a straightforward condition sufficient for determining the minimum asymptotic variance estimator in certain classes of estimators relevant to econometrics. These classes are relatively broad, as they include extremum estimation with smooth or nonsmooth objective functions; also, the rate of convergence to the asymptotic distribution is not required to be n −½ . We present examples illustrating the content of our result. In particular, we apply our result to a class…

时间序列模型中遗漏非线性的检验AI 解读

In this paper a new test, the neural network test for neglected nonlinearity, is compared with the Keenan test, the Tsay test, the White dynamic information matrix test, the McLeod-Li test, the Ramsey RESET test, the Brock-Dechert-Scheinkman test, and the Bispectrum test. The neural network test is based on the approximating ability of neural network modeling techniques recently developed by cognitive scientists. This test is a Lagrange multiplier test that statistically…

趋势变化的直接检验AI 解读

The authors consider tests for changing trend that do not require prior knowledge about the location of the changepoint. The limiting distribution is derived from the functional central limit theorem and the critical value from the hitting probability of a Brownian bridge. Applying a test sensitive to the alternative of trend stationarity with structural breaks, they find that for real gross national product, real per capita gross national product, and real wages before…

单隐层前馈网络学习的若干渐近结果AI 解读

We investigate the properties of a recursive estimation procedure (the method of “back-propagation”) for a class of nonlinear regression models (single hidden-layer feedforward network models) recently developed by cognitive scientists. The results follow from more general results for a class of recursive m estimators, obtained using theorems of Ljung (1977) and Walk (1977) for the method of stochastic approximation. Conditions are given ensuring that the back-propagation…

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相依异质过程的不变性原理与中心极限定理AI 解读

Building on work of McLeish, we present a number of invariance principles for doubly indexed arrays of stochastic processes which may exhibit considerable dependence, heterogeneity, and/or trending moments. In particular, we consider possibly time-varying functions of infinite histories of heterogeneous mixing processes and obtain general invariance results, with central limit theorems following as corollaries. These results are formulated so as to apply to economic time…

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具有改进有限样本性质的若干异方差稳健协方差估计量AI 解读

We examine several modified versions of the heteroskedasticity-consistent covariance matrix estimator of Hinkley (1977) and White (1980). On the basis of sampling experiments which compare the performance of quasi t statistics, we find that one estimator, based on the jackknife, performs better in small samples than the rest. We also examine finite-sample properties using modified critical values based on Edgeworth approximations, as proposed by Rothenberg (1988). In…

参数模型一致估计的统一理论AI 解读

We present a general theory of consistent estimation for possibly misspecified parametric models based on recent results of Domowitz and White. This theory extends the unification of Burguete, Gallant, and Souza by allowing for heterogeneous, time-dependent data and dynamic models. The theory is applied to yield consistency results for quasi-maximum-likelihood and method of moments estimators. Of particular interest is a new generalized rank condition for identifiability.

相依观测下的非线性回归AI 解读

This paper provides general conditions which ensure consistency and asymptotic normality for the nonlinear least squares estimator. These conditions apply to time-series, cross-section, panel, or experimental data for single equations as well as systems of equations. The regression errors may be serially correlated and/or heteroscedastic. For an important special case, we propose a new covariance matrix estimator which is consistent regardless of the presence of…

错设模型的极大似然估计AI 解读

This paper examines the consequences and detection of model misspecification when using maximum likelihood techniques for estimation and inference. The quasi-maximum likelihood estimator (QMLE) converges to a well defined limit, and may or may not be consistent for particular parameters of interest. Standard tests (Wald, Lagrange Multiplier, or Likelihood Ratio) are invalid in the presence of misspecification, but more general statistics are given which allow inferences to…

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独立观测下的工具变量回归AI 解读

INTRODUCTION THE METHOD OF INSTRUMENTAL VARIABLES (IV), proposed independently by Reiersol [15] and Geary [4], is one of the most useful classes of estimation techniques available to econometricians. The method is particularly useful in the context of errors-in-variables and simultaneous equation problems, and it ineludes ordinary least squares (OLS), two-stage least squares (2SLS) (Klein [9]), three-stage least squares (3SLS) (Madansky [12]) and certain full-information…

This paper examines the consequences and detection of model misspecification when using maximum likelihood techniques for estimation and inference. The quasi-maximum likelihood estimator (QMLE) converges to a well defined limit, and may or may not be consistent for particular parameters of interest. Standard tests (Wald, Lagrange Multiplier, or Likelihood Ratio) are invalid in the presence of misspecification, but more general statistics are given which allow inferences to…

非线性回归模型误设的后果与检测AI 解读

Under general conditions given here, the least squares estimator for the parameters of a misspecified nonlinear model converge strongly to the parameters of a (weighted) least squares approximation to the true model. With additional conditions, the least squares estimator is asymptotically normal. A new, specification-robust estimator of the covariance matrix is obtained, which simplifies to the usual estimator when the model is correct up to an independent additive error.…

异方差一致的协方差矩阵估计量与异方差直接检验AI 解读

This paper presents a parameter covariance matrix estimator which is consistent even when the disturbances of a linear regression model are heteroskedastic. This estimator does not depend on a formal model of the structure of the heteroskedasticity. By comparing the elements of the new estimator to those of the usual covariance estimator, one obtains a direct test for heteroskedasticity, since in the absence of heteroskedasticity, the two estimators will be approximately…

横截面数据的非线性回归AI 解读

This paper is a revised version of a paper originally en.titled "Asymptotic Properties of Nonlinear Weighted Least Squares Estimators with Independem not Identically Distributed Regressors." In Section 2, the strong consistency of a class of weighted least squares (WLS) estimators is proven under general conditions, as well as the strong consistency of weighted least squares with estimated weights (EWLS). Conditions which ensure asymptotic normality of the estimators are…