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Author: F Marqués Publisher: ISBN: Category : Languages : en Pages : 392
Book Description
In this book, supervised learning techniques (econometrics techniques) related to regression will be developed. More specifically, we will go deeper into the linear models of uniecuational and multiecuational multiple regression with all their problems of identification, estimation and diagnosis. Dynamic models and univariate and multivariate time series models are also contemplated. An important part of the content is devoted to limited dependent variable, count and sample selection models, with special mention to Logit, Probit and Tobit models. Finally, non-linear predictive models and panel data models are also discussed.
Author: F Marqués Publisher: ISBN: Category : Languages : en Pages : 392
Book Description
In this book, supervised learning techniques (econometrics techniques) related to regression will be developed. More specifically, we will go deeper into the linear models of uniecuational and multiecuational multiple regression with all their problems of identification, estimation and diagnosis. Dynamic models and univariate and multivariate time series models are also contemplated. An important part of the content is devoted to limited dependent variable, count and sample selection models, with special mention to Logit, Probit and Tobit models. Finally, non-linear predictive models and panel data models are also discussed.
Author: C Perez Publisher: Independently Published ISBN: Category : Languages : en Pages : 234
Book Description
Machine learning uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outcomes, and unsupervised learning, which finds hidden patterns or intrinsic structures in the input data. In this book, supervised learning techniques (predictive techniques) related to regression will be developed. More specifically, we will go deeper into the linear models multiple regression with all their problems of identification, estimation and diagnosis. Dynamic models and univariate time series models are also contemplated. Almon, Koyck, Klein, and other dynamic models are developed. An important part of the content is the structural changes and stability in dynamic predictive models, unit roots and co-integration. A great variety of examples and practical exercises solved with the Eviews software are presented.
Author: C Perez Publisher: ISBN: Category : Languages : en Pages : 174
Book Description
Machine learning uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outcomes, and unsupervised learning, which finds hidden patterns or intrinsic structures in the input data. In this book, supervised learning techniques (predictive techniques) related to regression will be developed. More specifically, we will go deeper into the multidimensional linear regression models, models in simultaneous equations, Box Jenkins ARIMA models, multivariate time series models, models of limited dependent variable and counting. Logistic models, Probit models, Tobit models, Poisson models, Negative Binomial models, panel data models and non linear models. A wide variety of examples and exercises are developed with the Eviews software.
Author: Felix Chan Publisher: Springer Nature ISBN: 3031151496 Category : Business & Economics Languages : en Pages : 385
Book Description
This book helps and promotes the use of machine learning tools and techniques in econometrics and explains how machine learning can enhance and expand the econometrics toolbox in theory and in practice. Throughout the volume, the authors raise and answer six questions: 1) What are the similarities between existing econometric and machine learning techniques? 2) To what extent can machine learning techniques assist econometric investigation? Specifically, how robust or stable is the prediction from machine learning algorithms given the ever-changing nature of human behavior? 3) Can machine learning techniques assist in testing statistical hypotheses and identifying causal relationships in ‘big data? 4) How can existing econometric techniques be extended by incorporating machine learning concepts? 5) How can new econometric tools and approaches be elaborated on based on machine learning techniques? 6) Is it possible to develop machine learning techniques further and make them even more readily applicable in econometrics? As the data structures in economic and financial data become more complex and models become more sophisticated, the book takes a multidisciplinary approach in developing both disciplines of machine learning and econometrics in conjunction, rather than in isolation. This volume is a must-read for scholars, researchers, students, policy-makers, and practitioners, who are using econometrics in theory or in practice.
Author: Publisher: CESAR PEREZ ISBN: 1716260116 Category : Computers Languages : en Pages : 234
Book Description
Microsoft SQL Server is a relational database management system, developed by the company Microsoft. The development language used (by command line or through the Management Studio graphic interface) is Transact-SQL (TSQL), an implementation of the ANSI standard of the SQL language, used to manipulate and retrieve data (DML), create tables and define relationships between them (DDL). This book develops the design, management and administration of databases through the relational language TRANSACT SQL
Author: Tshepo Chris Nokeri Publisher: ISBN: 9781484283707 Category : Languages : en Pages : 0
Book Description
Get up to speed on the application of machine learning approaches in macroeconomic research. This book brings together economics and data science. Author Tshepo Chris Nokeri begins by introducing you to covariance analysis, correlation analysis, cross-validation, hyperparameter optimization, regression analysis, and residual analysis. In addition, he presents an approach to contend with multi-collinearity. He then debunks a time series model recognized as the additive model. He reveals a technique for binarizing an economic feature to perform classification analysis using logistic regression. He brings in the Hidden Markov Model, used to discover hidden patterns and growth in the world economy. The author demonstrates unsupervised machine learning techniques such as principal component analysis and cluster analysis. Key deep learning concepts and ways of structuring artificial neural networks are explored along with training them and assessing their performance. The Monte Carlo simulation technique is applied to stimulate the purchasing power of money in an economy. Lastly, the Structural Equation Model (SEM) is considered to integrate correlation analysis, factor analysis, multivariate analysis, causal analysis, and path analysis. After reading this book, you should be able to recognize the connection between econometrics and data science. You will know how to apply a machine learning approach to modeling complex economic problems and others beyond this book. You will know how to circumvent and enhance model performance, together with the practical implications of a machine learning approach in econometrics, and you will be able to deal with pressing economic problems. What You Will Learn Examine complex, multivariate, linear-causal structures through the path and structural analysis technique, including non-linearity and hidden states Be familiar with practical applications of machine learning and deep learning in econometrics Understand theoretical framework and hypothesis development, and techniques for selecting appropriate models Develop, test, validate, and improve key supervised (i.e., regression and classification) and unsupervised (i.e., dimension reduction and cluster analysis) machine learning models, alongside neural networks, Markov, and SEM models Represent and interpret data and models .
Author: Marijn A. Bolhuis Publisher: International Monetary Fund ISBN: 1513529978 Category : Computers Languages : en Pages : 21
Book Description
We leverage insights from machine learning to optimize the tradeoff between bias and variance when estimating economic models using pooled datasets. Specifically, we develop a simple algorithm that estimates the similarity of economic structures across countries and selects the optimal pool of countries to maximize out-of-sample prediction accuracy of a model. We apply the new alogrithm by nowcasting output growth with a panel of 102 countries and are able to significantly improve forecast accuracy relative to alternative pools. The algortihm improves nowcast performance for advanced economies, as well as emerging market and developing economies, suggesting that machine learning techniques using pooled data could be an important macro tool for many countries.
Author: Chukwuemeka Tiptop Okoro Publisher: ISBN: Category : Languages : en Pages : 250
Book Description
There is a large group of people in a variety of fields, including finance, economics, accounting, science, mathematics, engineering, statistics, and public policy who need to understand some basic concepts of time series analysis and forecasting. Analyzing time-series data and forecasting future values of a time series are among the most important problems that analysts face in many fields. But to Successfully analyze this time series data requires that the analyst interact with computer software because the techniques and algorithms are just not suitable to manual calculations.This book has been written with the aim of solving this problems by providing a step-by-step guide to economic and financial econometrics using EViews. It contains a brief overviews of the concepts of econometric models, and data analysis techniques followed by procedures of how they can be implemented in EViews. This book is written as a compendium for undergraduate and graduate students in economics, finance, statistics and accounting. It can also serve as a guide for researchers and practitioners who desire to use EViews for analyzing financial data. This book may be used as a textbook companion for post graduate level courses in time series analysis, empirical finance, statistics and financial econometrics. Since, many organizations can improve their effectiveness and business results by making better short-to-medium term forecasts, this book should be useful to a wide variety of professionals. Topics Covered with examples Include: Chapter 1: Introduction to EViews. Chapter 2: Descriptive Statistics and Preliminary Tests. Chapter 3: Running Regression Analysis in EViews. Chapter 4: Forecasting Using Regression Models. Chapter 5: Economic Forecasting using ARIMA Modelling. Chapter 6: Volatility Modeling: ARCH, GARCH and EGARCH Models. An Introduction to Financial Econometrics. Chapter 7: Vector Autoregressive (VAR) Model. An Introduction to Macroeconometrics. Chapter 8: Vector Error Correction Model (VECM). Chapter 9: Autoregressive Distributed Lag Model (ARDL). Chapter 10: Panel Data Analysis
Author: Ebru Çağlayan Akay Publisher: Peter Lang Gmbh, Internationaler Verlag Der Wissenschaften ISBN: 9783631795682 Category : Econometrics Languages : en Pages : 0
Book Description
The book aims to bring together studies using different data types (panel data, cross-sectional data and time series data) and different methods (e.g., panel regression, nonlinear time series, chaos approach, among others) and to create a source for those interested in these topics and methods by addressing some selected applied econometrics topics.
Author: F Marqués
Author: F Marqués
Author: C Perez
Author: C Perez
Author: Felix Chan
Author: PEREZ. C. PEREZ
Author: 
Author: Tshepo Chris Nokeri
Author: Marijn A. Bolhuis
Author: Chukwuemeka Tiptop Okoro
Author: Ebru Çağlayan Akay