Nonlinear Models with Individual Effects

具有个体效应的非线性模型

• 批准号: 8810049
• 负责人: Whitney Newey
• 金额: $ 3.12万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1988
• 资助国家: 美国
• 起止时间: 1988-07-15 至 1991-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8810049&HistoricalAwards=false
• 关键词: Nonlinear; Models; Individual; Effects

Panel data, consisting of observations across time for individual economic agents, allows the possibility of controlling for unobserved individual heterogeneity. Such heterogeneity can arise from unobserved differences in tastes or technology and can be an important phenomenon. In such instances failure to control for it can result in misleading inferences. This problem is particularly severe when the unobserved heterogeneity affects explanatory variables. Situations of this sort arise naturally when some of the explanatory variables are decision variables, as is the case, for example, in dynamic panel data models. Models and methods of controlling for unobserved heterogeneity in linear models are well established. However, less is known about how to control for unobserved heterogeneity in nonlinear models. The methods that work for linear models do not carry over in a straightforward way to most nonlinear models. Except in special circumstances, it is not possible to eliminate individual effects by some data transformation, such as first differencing of time series observations which works in linear models. Also, fixed effects estimators of nonlinear models, which treat the individual effects as parameters to be estimated, encounter problems because of the short length of the typical time series in economic panel data. This research concerns two topics in controlling for unobserved heterogeneity in nonlinear models of panel data. The first topic deals with modelling the distribution of the individual effects, conditional on the explanatory variables. Such models can be used to specify random effects limited dependent variable models with a nonparametric conditional mean for the individual effect. The second topic investigates the use of the jacknife, applied to the time series dimension of panel data, for reduction of the bias of fixed effects estimators in nonlinear models. The research will examine the extent to which this nonparametric bias reduction method can alleviate the incidental parameters bias, in different models. The basic goals of the project are to assess the potential empirical impact of the models and methods to be considered, as well as derivation of asymptotic distributions for the various estimators.

面板数据由各个经济主体随时间的观察组成，可以控制未观察到的个体异质性。 这种异质性可能源于品味或技术方面未观察到的差异，并且可能是一种重要现象。 在这种情况下，未能对其进行控制可能会导致误导性的推论。 当未观察到的异质性影响解释变量时，这个问题尤其严重。 当某些解释变量是决策变量时，这种情况自然会出现，例如在动态面板数据模型中。 控制线性模型中未观察到的异质性的模型和方法已经很完善。 然而，人们对如何控制非线性模型中未观察到的异质性知之甚少。 适用于线性模型的方法不能直接适用于大多数非线性模型。 除特殊情况外，不可能通过某些数据变换来消除个体影响，例如线性模型中时间序列观测值的一阶差分。 此外，由于经济面板数据中典型时间序列的长度较短，非线性模型的固定效应估计器将个体效应视为要估计的参数，因此会遇到问题。 本研究涉及控制面板数据非线性模型中未观察到的异质性的两个主题。 第一个主题涉及以解释变量为条件的个体效应分布建模。 此类模型可用于指定随机效应受限因变量模型，其中单个效应具有非参数条件平均值。 第二个主题研究了将刀刀应用于面板数据的时间序列维度，以减少非线性模型中固定效应估计量的偏差。 该研究将检验这种非参数偏差减少方法可以在不同模型中减轻附带参数偏差的程度。 该项目的基本目标是评估要考虑的模型和方法的潜在实证影响，以及推导各种估计量的渐近分布。