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on Econometrics |
| By: | George Kapetanios; M. Hashem Pesaran |
| Abstract: | This paper considers alternative approaches to the analysis of large panel data models in the presence of error cross section dependence. A popular method for modelling such dependence uses a factor error structure. Such models raise new problems for estimation and inference. This paper compares two alternative methods for carrying out estimation and inference in panels with a multifactor error structure. One uses the correlated common effects estimator that proxies the unobserved factors by cross section averages of the observed variables as suggested by Pesaran (2004), and the other uses principal components following the work of Stock and Watson (2002). The paper develops the principal component method and provides small sample evidence on the comparative properties of these estimators by means of extensive Monte Carlo experiments. An empirical application to company returns provides an illustration of the alternative estimation procedures. |
| Keywords: | cross section dependence, large panels, principal components, common correlated effects, return equations |
| JEL: | C12 C13 C33 |
| Date: | 2005 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_1416&r=ecm |
| By: | Roger Gay |
| Abstract: | New results for ratios of extremes from distributions with a regularly varying tail are presented. Deriving from independence results for certain functions of order statistics, 'consecutive' ratios of extremes are shown to be independent as well as non-distribution specific. They have tractable distributions related to beta distributions. The minimum variance unbiased maximum likelihood estimator has the form of Hill's estimator. It achieves the Cramer-Rao minimum variance bound and is a function of a sufficient statistic. For small sample sizes the form of the moment generating function of the estimator shows it has a gamma distribution. |
| Keywords: | Tail-index, Minimum variance unbiased, Maximum likelihood, Asymptotically normal |
| JEL: | C13 |
| Date: | 2005–04 |
| URL: | http://d.repec.org/n?u=RePEc:msh:ebswps:2005-8&r=ecm |
| By: | Takao Kobayashi (Faculty of Economics, University of Tokyo); Seisho Sato (Department of Prediction and Control, Institute of Statistical Mathematics); Akihiko Takahashi (Faculty of Economics, University of Tokyo) |
| Abstract: | This paper proposes a new approach to style analysis by utilizing a general state space model and Monte Carlo filter. In particular,We regard coefficients of style indices as state variables in the state space model and apply Monte Carlo filter as estimation method. Moreover, an empirical analysis using actual funds' data confirms the validity of our approach. |
| Date: | 2005–04 |
| URL: | http://d.repec.org/n?u=RePEc:tky:fseres:2005cf337&r=ecm |
| By: | Thomas A. Severini (Northwestern University); Gautam Tripathi (University of Connecticut) |
| Abstract: | In applied work economists often seek to relate a given response variable y to some causal parameter mu* associated with it. This parameter usually represents a summarization based on some explanatory variables of the distribution of y, such as a regression function, and treating it as a conditional expectation is central to its identification and estimation. However, the interpretation of mu* as a conditional expectation breaks down if some or all of the explanatory variables are endogenous. This is not a problem when mu* is modelled as a parametric function of explanatory variables because it is well known how instrumental variables techniques can be used to identify and estimate mu*. In contrast, handling endogenous regressors in nonparametric models, where mu* is regarded as fully unknown, presents di±cult theoretical and practical challenges. In this paper we consider an endogenous nonparametric model based on a conditional moment restriction. We investigate identification related properties of this model when the unknown function mu* belongs to a linear space. We also investigate underidentification of mu* along with the identification of its linear functionals. Several examples are provided in order to develop intuition about identification and estimation for endogenous nonparametric regression and related models. |
| Keywords: | Endogeneity, Identification, Instrumental variables, Nonparametric models. |
| JEL: | C14 |
| Date: | 2005–04 |
| URL: | http://d.repec.org/n?u=RePEc:uct:uconnp:2005-12&r=ecm |
| By: | Pablo M Garcia (Centro de Estudios para la Producción - CEP) |
| Abstract: | It is usual to estimate willingness-to-pay in discrete choice models through Logit models –or their expanded versions-. Nevertheless, these models have very restrictive distributional assumptions. This paper is intended to examine the above- mentioned issue and to propose an alternative estimation using non-parametric techniques (through Simple Index Models). Furthermore, this paper introduces an empirical application of willingness-to-pay for improved subway travel times in the City of Buenos Aires. |
| JEL: | C2 L9 |
| Date: | 2005–04–23 |
| URL: | http://d.repec.org/n?u=RePEc:wpa:wuwpem:0504007&r=ecm |