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价格离散与函数型价格指数AI 解读

Let S denote a set of consumers who have identical, nondecreasing, ordinal, quasiconcave utility functions u: XS(t) -* u[X (t)], where XX(t) is the vector of n goods consumed by individual s at time t. Consumers shop at different stores and hence may pay different prices for commodities.4 Let PS(t) and yS(t) denote exogenous vectors of commodity prices and income, respectively, faced by individual s at time t. This does not preclude the existence of a subset, 5, of consumers…

非因果性的线性理论AI 解读

Abstract : Different definitions of noncausality (according to Granger, Sims, Haugh and Pierce,...) are analyzed in terms of orthogonality in the Hilbert space of square integrable variables. Conditions, when necessary, are given for their respective equivalence. Some problems of testability are mentioned. Finally noncausality is also analyzed in terms of rational expectations, extending previous results of Sims. (Author)

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带数量配给的季度宏观经济模型估计AI 解读

We specify and estimate a small fix-price model with quantity rationing on both the goods and labor markets. The side of the market which is rationed is random. The model is able to generate a productivity cycle and the degree of capacity utilization is endogenous. The rationing scheme, allocating supply between the various components of demand is estimated. The probabilities of being in the various regimes, the intensity of the disequilibrium on the markets, and the results…

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教育回报：连续选择变量下的选择性偏差方法AI 解读

The essence of selection bias is that we do not observe nonoptimal choices. This applies whether the choice variable is discrete or continuous. This paper extends the selection bias methodology to the case where the choice variable is continuous and the choice set is ordered. The leading practical application of this analysis is the schooling choice problem. Schooling is treated as a continuous choice variable and selectivity corrected rates of return are estimated. The…

先验信息极少时的模型选择AI 解读

A NUMBER OF AUTHORS argue that a Bayesian posterior odds criterion is appropriate for model selection.2 This paper considers how to derive this criterion when there is minimal prior information. We propose minimizing measures of prior information relative to the models in question rather than relative to the parameters of the particular models. In so doing, we obtain an expression for the odds that is invariant to the parameterization of the particular models and overcomes…

多项 Logit 模型的设定检验AI 解读

Discrete choice models are now used in a variety of situations in applied econometrics. By far the model specification which is used most often is the multinomial logit model. Yet it is widely known that a potentially important drawback of the multinomial logit model is the independence from irrelevant alternatives property. While most analysts recognize the implications of the independence of irrelevant alternatives property, it has remained basically a maintained…

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回归模型中 LM、LR 与 Wald 检验的稳健性AI 解读

In this paper we show that, in the small sample case, while the LR test is robust, the LM and W tests are not robust when the errors are Student t. For the large sample, however, all three tests are found to be robust. A variation of the W test is also considered and it turns out to be nonrobust. The implications of assuming the errors to be distributed as multivariate Student t, rather than multivariate normal, are also discussed. It is found that not all of Evans and…

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局部渐近设定误差分析AI 解读

An approximation to the inconsistency introduced by imposing an incorrect restriction on a parametric model is given. The approximation can be applied to estimators generated by optimizing any objective function satisfying certain regularity conditions. Examples given include analysis of misspecification in discrete choice and time-series models estimated by maximum likelihood, and in a nonlinear regression model. SPECIFICATION ERROR ANALYSIS in the linear regression model…

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误差协方差矩阵非标量时线性模型中的假设检验AI 解读

A WELL DEVELOPED EXACT STATISTICAL THEORY exists for hypothesis testing in the normal linear regression model when the errors are independent and homoscedastic. In the more general case where the error covariance matrix is nonscalar and depends on a set of unknown parameters, exact analysis is difficult and reliance is usually placed on asymptotic approximations for large sample size n. In this paper, higher-order asymptotic expansions are developed for comparing the size…

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