Hands-on Intermediate Econometrics Using R: Templates For Learning Quantitative Methods And R Software
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A01=Hrishikesh D Vinod
Artificial Neural Network (ANN)
Author_Hrishikesh D Vinod
Automatic ARIMA Models
Bootstrap
Category=KCH
Cointegration
Confidence Intervals
Confusion Matrix
Convergence Concepts
CP Criterion
Double Bootstrap
Duration (Survival) Models
Elastic Net Estimator
Endogeneity Problem
eq_bestseller
eq_business-finance-law
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Generalized Correlations
Generalized Partial Correlation Coefficients
Heckman Selection Bias
Hodrick-Prescott Filter
Identification of Simultaneous Equations
Iteratively Reweighted Least Squares (IRLS)
K-class Estimators
Kernel Regressions
Logit Model
Maximum Entropy Bootstrap
Model Selection
New Keynesian Phillips Curve (NKPC)
Nonlinear Granger-Causality
Pairs Trading
Panel Data
Pillar Charts
Probit Model
Production Functions
Quantile Regression
Receiver Operating Characteristic (ROC)
Regression
Stochastic Frontier Analysis (SFA)
Vector AR and VARMA
Product details
- ISBN 9789811256738
- Publication Date: 05 May 2022
- Publisher: World Scientific Publishing Co Pte Ltd
- Publication City/Country: SG
- Product Form: Paperback
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How to learn both applied statistics (econometrics) and free, open-source software R? This book allows students to have a sense of accomplishment by copying and pasting many hands-on templates provided here.The textbook is essential for anyone wishing to have a practical understanding of an extensive range of topics in Econometrics. No other text provides software snippets to learn so many new statistical tools with hands-on examples. The explicit knowledge of inputs and outputs of each new method allows the student to know which algorithm is worth studying. The book offers sufficient theoretical and algorithmic details about a vast range of statistical techniques.The second edition's preface lists the following topics generally absent in other textbooks. (i) Iteratively reweighted least squares, (ii) Pillar charts to represent 3D data. (iii) Stochastic frontier analysis (SFA) (iv) model selection with Mallows' Cp criterion. (v) Hodrick-Prescott (HP) filter. (vi) Automatic ARIMA models. (vi) Nonlinear Granger-causality using kernel regressions and bootstrap confidence intervals. (vii) new Keynesian Phillips curve (NKPC). (viii) Market-neutral pairs trading using two cointegrated stocks. (ix) Artificial neural network (ANN) for product-specific forecasting. (x) Vector AR and VARMA models. (xi) New tools for diagnosing the endogeneity problem. (xii) The elegant set-up of k-class estimators and identification. (xiii) Probit-logit models and Heckman selection bias correction. (xiv) Receiver operating characteristic (ROC) curves and areas under them. (xv) Confusion matrix. (xvi) Quantile regression (xvii) Elastic net estimator. (xviii) generalized Correlations (xix) maximum entropy bootstrap for time series. (xx) Convergence concepts quantified. (xxi) Generalized partial correlation coefficients (xxii) Panel data and duration (survival) models.
