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Artículo: AMZ-B0DLP6NH9N
Nonparametric and Semiparametric Methods in Econometrics: With Python (Richman Computational Economics)
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Paperback
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0.42 kg
0.42 kg
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USA
Sobre este producto
- Unlock the power of cutting-edge statistical techniques with our new comprehensive guide to Nonparametric and Semiparametric Methods in Econometrics. This all-encompassing resource is perfect for econometrics enthusiasts, data scientists, and statisticians who wish to harness modern approaches to complex economic data sets.Key Features:In-depth exploration of nonparametric and semiparametric methods in econometrics, providing a solid foundation for advanced economic analysis.Practical guidance on implementing and analyzing models using Python, allowing you to put theory into practice immediately.Comprehensive insight into a wide range of nonparametric topics, from kernel density estimation to machine learning algorithms.Book description:Dive into a world of sophisticated econometric tools designed to handle the complexities of economic data without the constraints of traditional parametric methods. This reference guide takes you on a journey through fundamental topics and recent advances in the field of nonparametric and semiparametric econometric methods. With the added benefit of Python coding examples in each chapter, you will be equipped to apply these techniques in real-world scenarios effortlessly. Whether you're an academic, researcher, or practitioner, this book will serve as an indispensable resource in your tool kit for modern statistical analysis.What you will learn:Master the art of nonparametric kernel density estimation techniques.Implement the Nadaraya-Watson kernel regression for analyzing conditional mean functions.Gain proficiency in local polynomial regression and its advantages over simpler models.Delve into flexible modeling with spline regression methods, including B-splines and natural splines.Explore series estimation techniques using diverse basis functions.Apply nearest neighbor methods to regression and classification tasks.Achieve precision in nonparametric estimates through effective bandwidth selection.Employ cross-validation techniques for model refinement in nonparametric contexts.Learn partially linear models for a hybrid approach to parametric and nonparametric analysis.Assess single-index models to simplify complex multivariate relationships.Utilize additive models and backfitting algorithms for robust estimation.Extend your skills with generalized additive models for broader applications.Execute nonparametric quantile regression for in-depth conditional quantile analysis.Address endogeneity issues with nonparametric instrumental variable methods.Explore semiparametric regression to mix parametric and nonparametric elements effectively.Apply bootstrap methods to enhance inference reliability in nonparametric models.Conduct hypothesis testing in nonparametric settings with various statistical tests.Estimate conditional distributions for comprehensive response variable analysis.Discover empirical likelihood methods for improved nonparametric estimation and inference.Navigate nonparametric panel data models for cross-sectional time-series challenges.Engage in nonparametric time series analysis, including spectral density estimation.Analyze functional data with advanced techniques for function-based observations.Implement dimension reduction techniques like PCA for econometric improvements.Embrace Bayesian methods for nonparametric inference, powered by Dirichlet processes.Delve into nonparametric survival analysis for effective failure time data analysis.Measure dependencies nonparametrically with Kendall's tau and other techniques.Investigate Granger causality using sophisticated nonparametric tests.
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