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Nonlinear Factor Models For Network And Panel Data
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Book Synopsis Nonlinear Factor Models for Network and Panel Data by : Mingli Chen
Download or read book Nonlinear Factor Models for Network and Panel Data written by Mingli Chen and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Factor structures or interactive effects are convenient devices to incorporate latent variables in panel data models. We consider fixed effect estimation of nonlinear panel single-index models with factor structures in the unobservables, which include logit, probit, ordered probit and Poisson specifications. We establish that fixed effect estimators of model parameters and average partial effects have normal distributions when the two dimensions of the panel grow large, but might suffer from incidental parameter bias. We show how models with factor structures can also be applied to capture important features of network data such as reciprocity, degree heterogeneity, homophily in latent variables and clustering. We illustrate this applicability with an empirical example to the estimation of a gravity equation of international trade between countries using a Poisson model with multiple factors.
Book Synopsis Nonlinear Factor Analysis by : Roderick P. McDonald
Download or read book Nonlinear Factor Analysis written by Roderick P. McDonald and published by . This book was released on 1967 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Non-linear Latent Factor Models for Revealing Structure in High-dimensional Data by : Roland Memisevic
Download or read book Non-linear Latent Factor Models for Revealing Structure in High-dimensional Data written by Roland Memisevic and published by . This book was released on 2008 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: Real world data is not random: The variability in the data-sets that arise in computer vision, signal processing and other areas is often highly constrained and governed by a number of degrees of freedom that is much smaller than the superficial dimensionality of the data. Unsupervised learning methods can be used to automatically discover the "true", underlying structure in such data-sets and are therefore a central component in many systems that deal with high-dimensional data.We also show how we can introduce supervision signals into latent variable models using conditioning. Supervision signals make it possible to attach "meaning" to the axes of a latent representation and to untangle the factors that contribute to the variability in the data. We develop a model that uses conditional latent variables to extract rich distributed representations of image transformations, and we describe a new model for learning transformation features in structured supervised learning problems.In this thesis we develop several new approaches to modeling the low-dimensional structure in data. We introduce a new non-parametric framework for latent variable modelling, that in contrast to previous methods generalizes learned embeddings beyond the training data and its latent representatives. We show that the computational complexity for learning and applying the model is much smaller than that of existing methods, and we illustrate its applicability on several problems.
Book Synopsis Nonlinear Modeling by : Johan A.K. Suykens
Download or read book Nonlinear Modeling written by Johan A.K. Suykens and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 265 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nonlinear Modeling: Advanced Black-Box Techniques discusses methods on Neural nets and related model structures for nonlinear system identification; Enhanced multi-stream Kalman filter training for recurrent networks; The support vector method of function estimation; Parametric density estimation for the classification of acoustic feature vectors in speech recognition; Wavelet-based modeling of nonlinear systems; Nonlinear identification based on fuzzy models; Statistical learning in control and matrix theory; Nonlinear time-series analysis. It also contains the results of the K.U. Leuven time series prediction competition, held within the framework of an international workshop at the K.U. Leuven, Belgium in July 1998.
Book Synopsis Nonlinear Dynamic Factor Models by : Gianluca Giudice
Download or read book Nonlinear Dynamic Factor Models written by Gianluca Giudice and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Dynamic Factor Models by : Jörg Breitung
Download or read book Dynamic Factor Models written by Jörg Breitung and published by . This book was released on 2016 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: Factor models can cope with many variables without running into scarce degrees of freedom.
Book Synopsis Nonlinear Panel Data Models with High-Dimensional Fixed Effects by : Amrei Stammann
Download or read book Nonlinear Panel Data Models with High-Dimensional Fixed Effects written by Amrei Stammann and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis The Econometric Analysis of Network Data by : Bryan Graham
Download or read book The Econometric Analysis of Network Data written by Bryan Graham and published by Academic Press. This book was released on 2020-05-15 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Econometric Analysis of Network Data serves as an entry point for advanced students, researchers, and data scientists seeking to perform effective analyses of networks, especially inference problems. It introduces the key results and ideas in an accessible, yet rigorous way. While a multi-contributor reference, the work is tightly focused and disciplined, providing latitude for varied specialties in one authorial voice. Answers both ‘why’ and ‘how’ questions in network analysis, bridging the gap between practice and theory allowing for the easier entry of novices into complex technical literature and computation Fully describes multiple worked examples from the literature and beyond, allowing empirical researchers and data scientists to quickly access the ‘state of the art’ versioned for their domain environment, saving them time and money Disciplined structure provides latitude for multiple sources of expertise while retaining an integrated and pedagogically focused authorial voice, ensuring smooth transition and easy progression for readers Fully supported by companion site code repository 40+ diagrams of ‘networks in the wild’ help visually summarize key points
Book Synopsis A Generalized Dynamic Factor Model for Panel Data by : Nikolaos Zirogiannis
Download or read book A Generalized Dynamic Factor Model for Panel Data written by Nikolaos Zirogiannis and published by . This book was released on 2013 with total page 43 pages. Available in PDF, EPUB and Kindle. Book excerpt: We develop a generalized dynamic factor model for panel data with the goal of estimating an unobserved index. While similar models have been developed in the literature of dynamic factor analysis, our contribution is threefold. First, contrary to simple dynamic factor analysis where multiple attributes of the same subject are measured at each time period, our model also accounts for multiple subjects. It is therefore suitable to a panel data framework. Second, our model estimates a unique unobserved index for every subject for every time period, as opposed to previous work where a temporal index common to all subjects was used. Third, we develop a novel iterative estimation process which we call the Two-Cycle Conditional Expectation-Maximization (2CCEM) algorithm and is flexible enough to handle a variety of different types of datasets. The model is applied on a panel measuring attributes related to the operation of water and sanitation utilities.
Book Synopsis Equivalence of Alternative Specifications of Nonlinear Panel Data Models by : Peter J. Simmons
Download or read book Equivalence of Alternative Specifications of Nonlinear Panel Data Models written by Peter J. Simmons and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Essays on Nonlinear Panel Data Models by : Center for Economic Research (Tilburg)
Download or read book Essays on Nonlinear Panel Data Models written by Center for Economic Research (Tilburg) and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Non-stationary Dynamic Factor Models for Large Datasets by : Matteo Barigozzi
Download or read book Non-stationary Dynamic Factor Models for Large Datasets written by Matteo Barigozzi and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Essays on Nonlinear Panel Models with Unobserved Heterogeneity by : Robert Martin
Download or read book Essays on Nonlinear Panel Models with Unobserved Heterogeneity written by Robert Martin and published by . This book was released on 2017 with total page 113 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Estimating Nonlinear Network Data Models with Fixed Effects by : David W. Hughes
Download or read book Estimating Nonlinear Network Data Models with Fixed Effects written by David W. Hughes and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Nonseparable Panel Data Models Identification, Estimation and Testing by : Dalia A. Ghanem
Download or read book Nonseparable Panel Data Models Identification, Estimation and Testing written by Dalia A. Ghanem and published by . This book was released on 2013 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: Microeconomic panel data, also known as longitudinal data or repeated measures, allow the researcher to observe the same individual across time. One of the advantages of panel data is that they allow the researcher to control for unobservable individual heterogeneity. The linear fixed effects model is the most commonly used method in empirical work to control for unobservable heterogeneity. Chapter 1 reviews the special features of the linear fixed effects model in detail, giving special attention to the definition of fixed effects and correlated random effects. It discusses the issues that arise when we move from a linear model to fully nonseparable models and reviews the two strands of the literature that are relevant for this dissertation: (1) the literature on nonlinear parametric panel data models with fixed effects, (2) the literature on nonparametric identification in nonseparable panel data models. Chapter 2 falls under the parametric nonlinear panel data models with fixed effects. Nonlinear panel data models with fixed effects are an important example in econometrics where the incidental parameter problem arises and the maximum likelihood estimator (MLE) is asymptotically biased. Bias correction of the MLE achieves consistency without increasing the asymptotic variance. Chapter 2 proposes a shrinkage estimator that combines that is shown to lead to a higher-order mean-square error improvement over the analytical bias-corrected estimator. Chapter 3 falls under the literature on nonparametric identification in nonseparable panel data models. Starting from a general DGP that exhibits nonseparability of the structural function, arbitrary individual and time heterogeneity, I give a necessary and sufficient condition for the point-identification of the APE for a subpopulation. This condition is then used to characterize the trade-off between assumptions on unobservable heterogeneity and the structural function that achieve identification. The identifying assumptions here have clear testable implications on the distribution of observables. I hence propose bootstrap-adjusted Kolmogorv-Smirnov and Cramer-von-Mises statistics to test these implications. Chapter 4 is an empirical paper that studies the issue of manipulation of air pollution data by Chinese cities. It applies tests similar in spirit to the tests proposed in Chapter 3 to test the presence of manipulation.
Book Synopsis Three Essays on Nonlinear Panel Data Models and Quantile Regression Analysis by : Iván Fernández-Val
Download or read book Three Essays on Nonlinear Panel Data Models and Quantile Regression Analysis written by Iván Fernández-Val and published by . This book was released on 2005 with total page 406 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation is a collection of three independent essays in theoretical and applied econometrics, organized in the form of three chapters. In the first two chapters, I investigate the properties of parametric and semiparametric fixed effects estimators for nonlinear panel data models. The first chapter focuses on fixed effects maximum likelihood estimators for binary choice models, such as probit, logit, and linear probability model. These models are widely used in economics to analyze decisions such as labor force participation, union membership, migration, purchase of durable goods, marital status, or fertility. The second chapter looks at generalized method of moments estimation in panel data models with individual-specific parameters. An important example of these models is a random coefficients linear model with endogenous regressors. The third chapter (co-authored with Joshua Angrist and Victor Chernozhukov) studies the interpretation of quantile regression estimators when the linear model for the underlying conditional quantile function is possibly misspecified.
Book Synopsis Nonlinear Panel Data Models with Dynamic Heterogeneity by : Christian Gourieroux
Download or read book Nonlinear Panel Data Models with Dynamic Heterogeneity written by Christian Gourieroux and published by . This book was released on 1998 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
