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Local Likelihood Estimation and Bias Reduction in Varying-coefficient Models

Local Likelihood Estimation and Bias Reduction in Varying-coefficient Models
Author: Göran Kauermann
Publisher:
Total Pages: 84
Release: 1995
Genre: Econometrics
ISBN:

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Abstract: "Varying coefficient models result from generalized linear models by allowing the parameter of the linear predictor to vary across some additional explanatory quantity called effect modifier. While Hastie & Tibshirani (1993) used spline smoothing techniques in univariate varying-coefficient models here the local likelihood approach is considered within the framework of multivariate generalized models. This approach allows the investigation of asymptotic properties of the estimate. Based on the Taylor expansion of the local likelihood function, consistency and asymptotic normality of the estimates are shown under rather general assuptions. Moreover, a numerically simple additive bias reduction method is proposed. The results are given for discrete as well as for continuous effect modifiers and asymptotically optimal rates of smoothing are derived. In the paper a different normalization of weights used. [sic] Instead of summing up to one the weights are one at the target point and less than one in the neighbourhood. This setting results by theoretical considerations and is supported by simulations showing an improvement of the variance estimation of the estimates for finite sample size. It is easy to see that both normalizations are asymptotically equivalent. An example taken from the German socio-economic panel demonstrates the applicability of the presented results."


Econometrics in Theory and Practice

Econometrics in Theory and Practice
Author: Robert Galata
Publisher: Springer Science & Business Media
Total Pages: 329
Release: 2012-12-06
Genre: Business & Economics
ISBN: 3642470270

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Three Essays on Estimation and Testing of Nonparametric Models

Three Essays on Estimation and Testing of Nonparametric Models
Author: Guangyi Ma
Publisher:
Total Pages:
Release: 2012
Genre:
ISBN:

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In this dissertation, I focus on the development and application of nonparametric methods in econometrics. First, a constrained nonparametric regression method is developed to estimate a function and its derivatives subject to shape restrictions implied by economic theory. The constrained estimators can be viewed as a set of empirical likelihood-based reweighted local polynomial estimators. They are shown to be weakly consistent and have the same first order asymptotic distribution as the unconstrained estimators. When the shape restrictions are correctly specified, the constrained estimators can achieve a large degree of finite sample bias reduction and thus outperform the unconstrained estimators. The constrained nonparametric regression method is applied on the estimation of daily option pricing function and state-price density function. Second, a modified Cumulative Sum of Squares (CUSQ) test is proposed to test structural changes in the unconditional volatility in a time-varying coefficient model. The proposed test is based on nonparametric residuals from local linear estimation of the time-varying coefficients. Asymptotic theory is provided to show that the new CUSQ test has standard null distribution and diverges at standard rate under the alternatives. Compared with a test based on least squares residuals, the new test enjoys correct size and good power properties. This is because, by estimating the model nonparametrically, one can circumvent the size distortion from potential structural changes in the mean. Empirical results from both simulation experiments and real data applications are presented to demonstrate the test's size and power properties. Third, an empirical study of testing the Purchasing Power Parity (PPP) hypothesis is conducted in a functional-coefficient cointegration model, which is consistent with equilibrium models of exchange rate determination with the presence of trans- actions costs in international trade. Supporting evidence of PPP is found in the recent float exchange rate era. The cointegration relation of nominal exchange rate and price levels varies conditioning on the real exchange rate volatility. The cointegration coefficients are more stable and numerically near the value implied by PPP theory when the real exchange rate volatility is relatively lower.


Parameter Estimation in Stochastic Volatility Models

Parameter Estimation in Stochastic Volatility Models
Author: Jaya P. N. Bishwal
Publisher: Springer Nature
Total Pages: 634
Release: 2022-08-06
Genre: Mathematics
ISBN: 3031038614

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This book develops alternative methods to estimate the unknown parameters in stochastic volatility models, offering a new approach to test model accuracy. While there is ample research to document stochastic differential equation models driven by Brownian motion based on discrete observations of the underlying diffusion process, these traditional methods often fail to estimate the unknown parameters in the unobserved volatility processes. This text studies the second order rate of weak convergence to normality to obtain refined inference results like confidence interval, as well as nontraditional continuous time stochastic volatility models driven by fractional Levy processes. By incorporating jumps and long memory into the volatility process, these new methods will help better predict option pricing and stock market crash risk. Some simulation algorithms for numerical experiments are provided.


The New Palgrave Dictionary of Economics

The New Palgrave Dictionary of Economics
Author:
Publisher: Springer
Total Pages: 7493
Release: 2016-05-18
Genre: Law
ISBN: 1349588024

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The award-winning The New Palgrave Dictionary of Economics, 2nd edition is now available as a dynamic online resource. Consisting of over 1,900 articles written by leading figures in the field including Nobel prize winners, this is the definitive scholarly reference work for a new generation of economists. Regularly updated! This product is a subscription based product.


Mathematical Reviews

Mathematical Reviews
Author:
Publisher:
Total Pages: 1852
Release: 2005
Genre: Mathematics
ISBN:

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The Work of Raymond J. Carroll

The Work of Raymond J. Carroll
Author: Marie Davidian
Publisher: Springer
Total Pages: 599
Release: 2014-06-06
Genre: Mathematics
ISBN: 3319058010

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This volume contains Raymond J. Carroll's research and commentary on its impact by leading statisticians. Each of the seven main parts focuses on a key research area: Measurement Error, Transformation and Weighting, Epidemiology, Nonparametric and Semiparametric Regression for Independent Data, Nonparametric and Semiparametric Regression for Dependent Data, Robustness, and other work. The seven subject areas reviewed in this book were chosen by Ray himself, as were the articles representing each area. The commentaries not only review Ray’s work, but are also filled with history and anecdotes. Raymond J. Carroll’s impact on statistics and numerous other fields of science is far-reaching. His vast catalog of work spans from fundamental contributions to statistical theory to innovative methodological development and new insights in disciplinary science. From the outset of his career, rather than taking the “safe” route of pursuing incremental advances, Ray has focused on tackling the most important challenges. In doing so, it is fair to say that he has defined a host of statistics areas, including weighting and transformation in regression, measurement error modeling, quantitative methods for nutritional epidemiology and non- and semiparametric regression.


Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA

Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA
Author: Elias T. Krainski
Publisher: CRC Press
Total Pages: 284
Release: 2018-12-07
Genre: Mathematics
ISBN: 0429629850

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Modeling spatial and spatio-temporal continuous processes is an important and challenging problem in spatial statistics. Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA describes in detail the stochastic partial differential equations (SPDE) approach for modeling continuous spatial processes with a Matérn covariance, which has been implemented using the integrated nested Laplace approximation (INLA) in the R-INLA package. Key concepts about modeling spatial processes and the SPDE approach are explained with examples using simulated data and real applications. This book has been authored by leading experts in spatial statistics, including the main developers of the INLA and SPDE methodologies and the R-INLA package. It also includes a wide range of applications: * Spatial and spatio-temporal models for continuous outcomes * Analysis of spatial and spatio-temporal point patterns * Coregionalization spatial and spatio-temporal models * Measurement error spatial models * Modeling preferential sampling * Spatial and spatio-temporal models with physical barriers * Survival analysis with spatial effects * Dynamic space-time regression * Spatial and spatio-temporal models for extremes * Hurdle models with spatial effects * Penalized Complexity priors for spatial models All the examples in the book are fully reproducible. Further information about this book, as well as the R code and datasets used, is available from the book website at http://www.r-inla.org/spde-book. The tools described in this book will be useful to researchers in many fields such as biostatistics, spatial statistics, environmental sciences, epidemiology, ecology and others. Graduate and Ph.D. students will also find this book and associated files a valuable resource to learn INLA and the SPDE approach for spatial modeling.