Bộ sách luyện thi ETS 2023 LC&RC
199,000₫
299,000₫
Sách gia công, Bìa mềm
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
thể loại:Computers
Năm:2021
In lần thứ:1
Nhà xuát bản:Apress
Ngôn ngữ:english
Trang:241
