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似不相关回归中扰动同期相关性的精确检验AI 解读

This paper proposes finite-sample procedures for testing the SURE specification in multi-equation regression models, i.e. whether the disturbances in different equations are contemporaneously uncorrelated or not. We apply the technique of Monte Carlo (MC) tests [Dwass (1957), Barnard (1963)] to obtain exact tests based on standard LR and LM zero correlation tests. We also suggest a MC quasi-LR (QLR) test based on feasible generalized least squares (FGLS). We show that the…

马尔可夫过程、双向自回归与平稳及非平稳自回归的有限样本推断AI 解读

In this paper, we develop finite-sample inference procedures for stationary and nonstationary autoregressive (AR) models. The method is based on special properties of Markov processes and a split-sample technique. The results on Markovian processes (intercalary independence and truncation) only require the existence of conditional densities. They are proved for possibly nonstationary and/or non-Gaussian multivariate Markov processes. In the context of a linear regression…

可计算一般均衡模型的统计推断：摩洛哥经济模型应用AI 解读

We study the problem of measuring the uncertainty of computable general equilibrium (CGE) (or RBC)-type model simulations associated with parameter uncertainty. We describe two approaches for building confidence sets on model endogenous variables. The first uses a standard Wald-type statistic. The second approach assumes that a confidence set (sampling or Baycsian) is available for the free parameters, from which confidence sets are derived by a projection technique. The…

时间序列中的短期与长期因果性：理论AI 解读

Causality in Granger's sense is defined in terms of predictibility one period ahead. The notion of causality is generalized by considering causality at any given horizon 1 <= h <= inifinity, providing a rigorous formalization of indirect causal effects and causality chains in (possibly) nonstationary time series. The authors derive necessary and sufficient conditions for noncausality between vectors up to any given horizon. They observe that coefficients of lagged variables…

一阶自回归分布滞后模型的精确推断方法AI 解读

Exact tests and confidence sets are obtained for general transformations of the coefficients in linear first-order autoregressive models with exogenous variables and i.i.d. disturbances. The tests proposed have known level and are either similar (constant rejection probability under all processes consistent with the null hypothesis) or use bounds which are free of nuisance parameters. Correspondingly, the confidence sets are either similar with known size or conservative.…

计量经济学中的若干不可能性定理及其在结构与动态模型中的应用AI 解读

General characterizations of valid confidence sets and tests in problems involving (locally almost) unidentified (LAU) parameters are presented. In particular, any valid confidence set for an unbounded LAU parameter must be unbounded with positive probability. Consequently, almost surely bounded confidence sets, like Wald-type confidence sets, have zero coverage probability and Wald-type test statistics cannot be pivotal functions. The results are applied to simultaneous…

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多元ARMA模型中两个变量向量之间的因果性检验AI 解读

In the analysis of economic time series, a question often raised is whether a vector of variables causes another one in the sense of Granger. Most of the literature on this topic is concerned with bivariate relationships or uses finite-order autoregressive specifications. The purpose of this article is to develop a causality analysis in the sense of Granger for general vector autoregressive moving average (ARMA) models. We give a definition of Granger noncausality between…

改进的 Berry–Esseen–Chebyshev 界及统计应用AI 解读

A Sharpening Of Nonuniform bounds of the Berry-Esseen type initially obtained by Esseen and later generalized by Kolodjažnyĭ–who also proved that they are, in some sense, optimal–is proposed. Further, the corresponding inequalities are shown to provide uniformly improved Chebyshev bounds for the tail probabilities of the distribution functions to be approximated. In contrast with most results on Berry–Esseen bounds, which emphasize rates of convergence to normality, the…

不变性、非线性模型与渐近检验AI 解读

The invariance properties of several asymptotic tests are studied: invariance to hypothesis representation, reparameterization, and rescaling. Noninvariant tests include Wald tests, variants of LM tests, and Neyman's C(" alpha") tests. For all these tests, simply changing measurement units can lead to vastly different results, e.g. in models with Box-Cox transformations. Various consistent estimators of the information matrix produce tests with different invariance…

非参数 t 检验的非一致界AI 解读

This paper gives simple nonuniform bounds on the tail areas of the permutation distribution of the usual Student's t -statistic when the observations are independent with symmetric distributions. As opposed to uniform bounds, nonuniform bounds depend on the observed sample. It is shown that the nonuniform bounds proposed are always tighter than uniform exponential bounds previously suggested. The use of the bounds to perform nonparametric t -tests is discussed and numerical…

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误差自相关线性回归中的精确检验与置信集AI 解读

This article proposes a general method to build exact tests and confidence sets in linear regressions with first-order autoregressive Gaussian disturbances. Because of a nuisance parameter problem, we argue that generalized bounds tests and conservative confidence sets provide natural inference procedures in such a context. Given an exact confidence set for the autocorrelation coefficient, we describe how to obtain a similar simultaneous confidence set for the…

线性回归中非线性假设、不等式约束与非嵌套假设的精确同时检验AI 解读

In the classical linear model, comparison of two arbitrary hypotheses on the regression coefficients is considered. Problems involving nonlinear hypotheses, inequality restrictions, or non-nested hypotheses are included. Exact bounds on the null distribution of likelihood ratio statistics are derived (based on the central Fisher distribution). As a special case, a bounds test similar to the Durbin-Watson test is proposed. Multiple testing problems are studied: the bounds…

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估计向量自回归模型预测的无偏性AI 解读

Forecasts from a univariate autoregressive model estimated by OLS are unbiased, irrespective of whether the model fitted has the correct order; this property only requires symmetry of the distribution of the innovations. In this paper, this result is generalized to vector autoregressions and a wide class of multivariate stochastic processes (which include Gaussian stationary multivariate stochastic processes) is described for which unbiasedness of predictions holds:…

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