สมศักดิ์ จันทร์เอม ภาพนิทัศน์มีความสำคัญอย่างมากในการทำความเข้าใจข้อมูล และเพื่อประสิทธิภาพในการตัดสินใจ เครื่องมือที่ช่วยในการใช้สร้างภาพนิทัศน์ของข้อมูลในปัจจุบัน มีหลายตัว ในหนังสือเล่มจะใช้ภาษาอาร์ในการเขียนโปรแกรมเพื่อสร้างภาพนิทัศน์ และใช้โปรแกรม RStudio เพื่อช่วยการใช้เขียนโปรแกรมภาษาอาร์ให้มีความสะดวกสบายมากยิ่งขึ้นด้วยเครื่องมือช่วยที่หลากหลาย ในหนังสือเล่มไม่ได้สนใจในประเด็นตัวแบบสถิติ (statistics model) เศรษฐมิติ (econometrics) หรือการเรียนรู้ของเครื่องจักร (machine learning) ด้วยภาษาอาร์ แต่ถ้าผู้อ่านได้ศึกษาและทำความเข้าใจในหนังสือเล่มนี้แล้ว ผู้อ่านจะได้เรียนรู้พื้นฐานการเขียนโปรแกรมภาษาอาร์ที่จำเป็นอย่างมีหลักการ เช่นชนิดของโครงสร้างข้อมูลที่สำคัญคือวัตถุแบบเวคเตอร์ (vector) และกรอบข้อมูล (data frame) … Read more →

This is a minimal example of using the bookdown package to write a book. The HTML output format for this example is bookdown::gitbook, set in the _output.yml file. [...] These notes have been compiled from the notes I use in my Introduction to Econometrics, Econometrics II, and Data Anaytics class. In many cases, these notes mirror the powerpoint slides avaiable on blackboard. At times, I will add additional details in the text and provide more examples. This text should not be seen as a replacement for the required textbook. The required textbook provides many more examples and ... Read more →

Beginners with little background in statistics and econometrics often have a hard time understanding the benefits of having programming skills for learning and applying Econometrics. ‘Introduction to Econometrics with R’ is an interactive companion to the well-received textbook ‘Introduction to Econometrics’ by James H. Stock and Mark W. Watson (2015). It gives a gentle introduction to the essentials of R programming and guides students in implementing the empirical applications presented throughout the textbook using the newly aquired skills. This is supported by interactive programming exercises generated with DataCamp Light and integration of interactive visualizations of central concepts which are based on the flexible JavaScript library D3.js. Read more →

This book walks through the ten most important statistical theorems as highlighted by Jeffrey Wooldridge, presenting intuiitions, proofs, and applications. […] A list of 10 econometric theorems was circulated on Twitter citing what Jeffrey Wooldridge claims you need to apply repeatedly in order to do econometrics. As a political scientist with applied statistics training, this list caught my attention because it contains many of the theorems I see used in (methods) papers, but which I typically glaze over for lack of understanding. The complete list (slightly paraphrased) is: As an exercise … Read more →

Textbook for ECON 381: Statistics and Probability for Econometrics […] Here the introduction to the class and … Read more →

The subject of this textbook is Bayesian regression analysis, and its main aim is to provide introductory level theory foundation, and facilitate applicability of Bayesian inference. […] Since late 90’s Bayesian inference has gained a lot of popularity among researchers due to the computational revolution and availability of algorithms to solve complex integrals. However, many researchers, students and practitioners still lack understanding and application of this inferential approach. The main reason is the requirement of good programming skills. Introduction to Bayesian econometrics: A … Read more →

Prelude to Econometrics Using R […] In preparation for my first semester teaching Econometrics using R, I prepared a series of R Notebooks for use in class. Not only did I use these notebooks to teach the material in class, I also provided them to students for their use, study, and such. At one point, in referring to this collection of notebooks, a student told me that they liked my book, and my first instinct was to reply that it wasn’t really a book, it was just a collection of teaching notes. But this student’s statement stuck with me, and I realized that, while it wasn’t really … Read more →

Add description […] Outline of preface (preliminary and incomplete). June 21, 2016. I started teaching the course Introduction to Financial Econometrics at UW in 1998. Motivation was to teach more statistics and quantitative methods to economics majors. I found that combining statistics topics with finance applications was very effective and popular. Early classes used Microsoft Excel as the main software tool (R was not around then). Experience with Excel was, and still is, in high demand by employers in the finance industry. However, Excel is not a good tool for doing statistics. In early … Read more →

This is a minimal example of using the bookdown package to write a book. set in the _output.yml file. The HTML output format for this example is bookdown::gitbook, [...] I’ve written this booklet as a companion to Stock and Watson (2019). I will assign sections for you to read before coming to class. This will give us time in class to address common misconceptions and ask new questions. I recommend you read the corresponding material in Stock and Watson (2019) after you have read the chapter in this companion text and either before or after our in-class ... Read more →

This research paper aims at modelling customer portfolio, churn and customer value. […] This paper is being realized as part of our last year in master’s degree in economics. It aims at studying a firm’s most valuable asset namely its customers. To that end, we adopt a quantitative approach based on econometrics and data analysis with a threefold purpose to : After having defined the subject’s key concepts, we apply duration models and machine learning techniques to a kaggle dataset related to customers of a fictional telecommunications service provider (TSP). Keywords: customer portfolio … Read more →

Introduction to R for Econometrics […] This is a short introduction to R to go with the first year econometrics courses at the Tinbergen Institute. It is aimed at people who are relatively new to R, or programming in general.1 The goal is to give you enough of knowledge of the fundamentals of R to write and adapt code to fit econometric models to data, and to simulate your own data, working alone or with others. You will be able to: read data from csv files, plot it, manipulate it into the form you want, use sets of functions others have built (packages), write your own functions to compute … Read more →

Data Science and Econometrics for NBA Analytics […] The 1960s was a period of time where oil became the most valuable and productivity augmenting resource for companies to extract, prompting companies to engage in a race to extract as much oil as possible without any regard for the environmental and social consequences. However, recent times has seen data replace oil as the most valuable resource, even for sports organizations. Analytics have a major place in today’s sports world. At some level, every sports organization relies on data and analytics for team development, salary structure, … Read more →

Class notes for the BGU course - Introduction to Data Science. […] This book accompanies the course I give at Ben-Gurion University, named “Introduction to Data Science”. This is an introductory-level, hands-on focused course, designed for students with basic background in statistics and econometrics, and without programming experience. It introduces students to different tools needed for building a data science pipeline, including data processing, analysis, visualization and modeling. The course is taught in R environment. Many of the contents in this book are taken from BGU’s “R” course, … Read more →

This is a beginner’s guide to applied econometrics using the free statistics software R. […] … Read more →