In this paper we derive asymptotic theory for linear panel regression augmented with estimated common factors. We give conditions under which the estimated factors can be used in place of the latent factors in the regression equation. For the principal component estimates of the factor space it is shown that these conditions are satisified when T/N → 0 and N/T 3 → 0 under regularity. Monte-Carl...

The conventional heteroskedasticity-robust (HR) variance matrix estimator for cross-sectional regression (with or without a degrees-of-freedom adjustment), applied to the fixed-effects estimator for panel data with serially uncorrelated errors, is inconsistent if the number of time periods T is fixed (and greater than 2) as the number of entities n increases. We provide a bias-adjusted HR estim...

We propose an estimation procedure for (semiparametric) panel data censored regression models in which the error terms may be subject to general forms of non-stationarity, thus permitting heteroscedasticity over time. The proposed estimator exploits a weak structural form imposed on the individual speci ̄c e®ect. This is in contrast to the estimators introduced in Honor¶e(1992) where the individ...

This paper develops an instrumental variables estimator for quantile regression in panel data with fixed effects. Asymptotic properties of the instrumental variables estimator are studied for large N and T when Na/T → 0, for some a > 0. Wald and Kolmogorov-Smirnov type tests for general linear restrictions are developed. The estimator is applied to the problem of measurement errors in variables...

The notion of a conditional linear predictor is used as a distributionfree method for eliminating the individual-specific effects in a class of nonlinear, unobserved components panel data models. The methodology is applied to a general count model, which allows for individual dispersion in addition to an individual mean effect. As a corollary of the general results, the multinomial quasi-condit...

Decompositions by population subgroups and by income sources represent the traditional techniques for decomposing income inequality. Compared with the classical methodologies, the regression-based method gives the opportunity of quantifying the contribution to the inequality of a set of factors, while taking the correlations among them into account. In this framework, two regression-based decom...

In this paper we provide a new methodology to analyze the (Gaussian) profile quasi likelihood function for panel regression models with interactive fixed effects, also called factor models. The number of factors is assumed to be known. Employing the perturbation theory of linear operators, we derive a power series expansion of the likelihood function in the regression parameters. Using this exp...

the aim of this study is to investigate the relationship between tax avoidance and firm value with an emphasis on agency cost and disclosure quality of firms listed in tehran stock exchange. the sample of this study consists of 68 firms for the period 2004 to 2015.this study is applied in purpose and correlation- descriptive in method. in this study to test the hypotheses, multivariate linear r...

Laboratory experiments on the provision of public goods follow each subject over time as he interacts with a small group of others, deciding in each period how much of an initial allocation to contribute to a group good that yields a return to all group members. These experiments produce data sets that are rich in dynamics, as subjects respond not only to the parameters of the experiment, but a...

This paper provides a method to construct simultaneous confidence bands for quantile functions and quantile effects in nonlinear network and panel models with unobserved two-way effects, strictly exogenous covariates, and possibly discrete outcome variables. The method is based upon projection of simultaneous confidence bands for distribution functions constructed from fixed effects distributio...