Modern Linear And Nonlinear Econometrics
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Author | : Joseph Plasmans |
Publisher | : Springer Science & Business Media |
Total Pages | : 412 |
Release | : 2006-08-30 |
Genre | : Business & Economics |
ISBN | : 9780387257600 |
The basic characteristic of Modern Linear and Nonlinear Econometrics is that it presents a unified approach of modern linear and nonlinear econometrics in a concise and intuitive way. It covers four major parts of modern econometrics: linear and nonlinear estimation and testing, time series analysis, models with categorical and limited dependent variables, and, finally, a thorough analysis of linear and nonlinear panel data modeling. Distinctive features of this handbook are: -A unified approach of both linear and nonlinear econometrics, with an integration of the theory and the practice in modern econometrics. Emphasis on sound theoretical and empirical relevance and intuition. Focus on econometric and statistical methods for the analysis of linear and nonlinear processes in economics and finance, including computational methods and numerical tools. -Completely worked out empirical illustrations are provided throughout, the macroeconomic and microeconomic (household and firm level) data sets of which are available from the internet; these empirical illustrations are taken from finance (e.g. CAPM and derivatives), international economics (e.g. exchange rates), innovation economics (e.g. patenting), business cycle analysis, monetary economics, housing economics, labor and educational economics (e.g. demand for teachers according to gender) and many others. -Exercises are added to the chapters, with a focus on the interpretation of results; several of these exercises involve the use of actual data that are typical for current empirical work and that are made available on the internet. What is also distinguishable in Modern Linear and Nonlinear Econometrics is that every major topic has a number of examples, exercises or case studies. By this `learning by doing' method the intention is to prepare the reader to be able to design, develop and successfully finish his or her own research and/or solve real world problems.
Author | : John Groves Heywood |
Publisher | : Springer Verlag |
Total Pages | : 322 |
Release | : 1992 |
Genre | : Mathematics |
ISBN | : 9780387562612 |
Author | : Phoebus J. Dhrymes |
Publisher | : Springer Science & Business Media |
Total Pages | : 411 |
Release | : 2012-12-06 |
Genre | : Business & Economics |
ISBN | : 1461243025 |
This book is intended for second year graduate students and professionals who have an interest in linear and nonlinear simultaneous equations mod els. It basically traces the evolution of econometrics beyond the general linear model (GLM), beginning with the general linear structural econo metric model (GLSEM) and ending with the generalized method of mo ments (GMM). Thus, it covers the identification problem (Chapter 3), maximum likelihood (ML) methods (Chapters 3 and 4), two and three stage least squares (2SLS, 3SLS) (Chapters 1 and 2), the general nonlinear model (GNLM) (Chapter 5), the general nonlinear simultaneous equations model (GNLSEM), the special ca'3e of GNLSEM with additive errors, non linear two and three stage least squares (NL2SLS, NL3SLS), the GMM for GNLSEIVl, and finally ends with a brief overview of causality and re lated issues, (Chapter 6). There is no discussion either of limited dependent variables, or of unit root related topics. It also contains a number of significant innovations. In a departure from the custom of the literature, identification and consistency for nonlinear models is handled through the Kullback information apparatus, as well as the theory of minimum contrast (MC) estimators. In fact, nearly all estimation problems handled in this volume can be approached through the theory of MC estimators. The power of this approach is demonstrated in Chapter 5, where the entire set of identification requirements for the GLSEM, in an ML context, is obtained almost effortlessly, through the apparatus of Kullback information.
Author | : Frauke Schleer-van Gellecom |
Publisher | : Springer Science & Business Media |
Total Pages | : 268 |
Release | : 2013-12-11 |
Genre | : Business & Economics |
ISBN | : 3642420397 |
In recent years nonlinearities have gained increasing importance in economic and econometric research, particularly after the financial crisis and the economic downturn after 2007. This book contains theoretical, computational and empirical papers that incorporate nonlinearities in econometric models and apply them to real economic problems. It intends to serve as an inspiration for researchers to take potential nonlinearities in account. Researchers should be aware of applying linear model-types spuriously to problems which include non-linear features. It is indispensable to use the correct model type in order to avoid biased recommendations for economic policy.
Author | : Mehmet Terzioğlu |
Publisher | : BoD – Books on Demand |
Total Pages | : 339 |
Release | : 2021-03-17 |
Genre | : Business & Economics |
ISBN | : 1839624868 |
The importance of experimental economics and econometric methods increases with each passing day as data quality and software performance develops. New econometric models are developed by diverging from earlier cliché econometric models with the emergence of specialized fields of study. This book, which is expected to be an extensive and useful reference by bringing together some of the latest developments in the field of econometrics, also contains quantitative examples and problem sets. We thank all the authors who contributed to this book with their studies that provide extensive and accessible explanations of the existing econometric methods.
Author | : William A. Barnett |
Publisher | : Cambridge University Press |
Total Pages | : 248 |
Release | : 2000-05-22 |
Genre | : Business & Economics |
ISBN | : 9780521594240 |
This book presents some of the more recent developments in nonlinear time series, including Bayesian analysis and cointegration tests.
Author | : A. Smith |
Publisher | : Createspace Independent Publishing Platform |
Total Pages | : 176 |
Release | : 2017-11-08 |
Genre | : |
ISBN | : 9781979547482 |
Statistics and Machine Learning Toolbox allows you to fit Nonlinear Regression Models. Once you fit a model, you can use it to predict or simulate responses, assess the model fit using hypothesis tests, or use plots to visualize diagnostics, residuals, and interaction effects. Parametric nonlinear models represent the relationship between a continuous response variable and one or more continuous predictor variables in the form y = f(X, b) + e, with f is a nonlinear function. fitnlm attempts to find values of the parameters b that minimize the mean squared differences between the observed responses y and the predictions of the model f(X, b). To do so, it needs a starting value beta0 before iteratively modifying the vector b to a vector with minimal mean squared error. This book develops nonlinear regression models taking into account the stages of identification, estimation, diagnosis and prediction. The most important content is the following: - Nonlinear Regression - Represent the Nonlinear Model - Choose Initial Vector beta0 - Fit Nonlinear Model to Data - Examine Quality and Adjust the Fitted Nonlinear Model - Predict or Simulate Responses Using a Nonlinear Model - Mixed-Effects Models - Introduction to Mixed-Effects Models - Mixed-Effects Model Hierarchy - Specifying Mixed-Effects Models - Specifying Covariate Models - Choosing nlmefit or nlmefitsa - Using Output Functions with Mixed-Effects Models - Examining Residuals for Model Verification - Mixed-Effects Models Using nlmefit and nlmefitsa - Multinomial Models for Nominal Responses - Multinomial Models for Ordinal Responses - Hierarchical Multinomial Models - Generalized Linear Models - Lasso Regularization of Generalized Linear Models - Regularize Poisson Regression - Regularize Logistic Regression - Regularize Wide Data in Parallel - Generalized Linear Mixed-Effects Models - Fit a Generalized Linear Mixed-Effects Model - Multivariate Generalized Linear Models - Multivariate Fixed Effects Panel Model with AutocorrelationMultivariate Longitudinal Analysis - Multivariate Longitudinal Analysis
Author | : Giuseppe Orlando |
Publisher | : Springer Nature |
Total Pages | : 361 |
Release | : 2021-08-31 |
Genre | : Business & Economics |
ISBN | : 3030709825 |
This interdisciplinary book argues that the economy has an underlying non-linear structure and that business cycles are endogenous, which allows a greater explanatory power with respect to the traditional assumption that dynamics are stochastic and shocks are exogenous. The first part of this work is formal-methodological and provides the mathematical background needed for the remainder, while the second part presents the view that signal processing involves construction and deconstruction of information and that the efficacy of this process can be measured. The third part focuses on economics and provides the related background and literature on economic dynamics and the fourth part is devoted to new perspectives in understanding nonlinearities in economic dynamics: growth and cycles. By pursuing this approach, the book seeks to (1) determine whether, and if so where, common features exist, (2) discover some hidden features of economic dynamics, and (3) highlight specific indicators of structural changes in time series. Accordingly, it is a must read for everyone interested in a better understanding of economic dynamics, business cycles, econometrics and complex systems, as well as non-linear dynamics and chaos theory.
Author | : Christopher F. Baum |
Publisher | : Stata Press |
Total Pages | : 362 |
Release | : 2006-08-17 |
Genre | : Business & Economics |
ISBN | : 1597180130 |
Integrating a contemporary approach to econometrics with the powerful computational tools offered by Stata, this introduction illustrates how to apply econometric theories used in modern empirical research using Stata. The author emphasizes the role of method-of-moments estimators, hypothesis testing, and specification analysis and provides practical examples that show how to apply the theories to real data sets. The book first builds familiarity with the basic skills needed to work with econometric data in Stata before delving into the core topics, which range from the multiple linear regression model to instrumental-variables estimation.
Author | : H. J. Bierens |
Publisher | : Springer Science & Business Media |
Total Pages | : 211 |
Release | : 2012-12-06 |
Genre | : Mathematics |
ISBN | : 3642455298 |
This Lecture Note deals with asymptotic properties, i.e. weak and strong consistency and asymptotic normality, of parameter estimators of nonlinear regression models and nonlinear structural equations under various assumptions on the distribution of the data. The estimation methods involved are nonlinear least squares estimation (NLLSE), nonlinear robust M-estimation (NLRME) and non linear weighted robust M-estimation (NLWRME) for the regression case and nonlinear two-stage least squares estimation (NL2SLSE) and a new method called minimum information estimation (MIE) for the case of structural equations. The asymptotic properties of the NLLSE and the two robust M-estimation methods are derived from further elaborations of results of Jennrich. Special attention is payed to the comparison of the asymptotic efficiency of NLLSE and NLRME. It is shown that if the tails of the error distribution are fatter than those of the normal distribution NLRME is more efficient than NLLSE. The NLWRME method is appropriate if the distributions of both the errors and the regressors have fat tails. This study also improves and extends the NL2SLSE theory of Amemiya. The method involved is a variant of the instrumental variables method, requiring at least as many instrumental variables as parameters to be estimated. The new MIE method requires less instrumental variables. Asymptotic normality can be derived by employing only one instrumental variable and consistency can even be proved with out using any instrumental variables at all.