962312 :: เศรษฐมิติ 1

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Course Overview

คำอธิบายรายวิชา(ไทย) : การวิเคราะห์สมการถดถอยสำหรับนักเศรษฐศาสตร์ การกำหนดแบบสมการ การประเมินค่าแบบจำลอง การวิเคราะห์สมการถดถอยอย่างง่าย สมการถดถอยเชิงซ้อน ปัญหาของแบบจำลองสมการถดถอยเชิงซ้อน และแบบจำลองระบบสมการเบื้องต้น

คำอธิบายรายวิชา(อังกฤษ) : Regression analysis for economists, emphasis on specification, estimation and interpretation of econometric models, analysis simple linear, multicollinearity, heteroskedasticity, autocorrelation and introduction to simultaneous equation models.

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Last Updated On 6 August 2018

123 Downloadable Resources
1 Assignment

Course Content

Start Date: 20/07/16
Category: หลักสูตรเศรษฐศาสตร์

General

ASK ANY QUESTIONS
Announcements
Course Syllabus !!!
Course Schedule!!!
How to submit your works
QUIZ 1. Close at 8:45 pm 30-8-62
QuiZ 2 open 8.00 pm close by 8.45 pm 26/9
QUIZ PractinceR

Lecture Slide & Reading

Ch.1 INTRODUCTION
Reading GT Intro
Ch.2 Simple Regression Models
Reading Ch.2 SimpleRegression
ReadingCh.3 GT
Reading GT Appendix 3A proof est Var(sigma2)
CLRM assumptions/ BLUE estimator (Week 5)
Correlation Coefficient clip
Slide: Classical Normal Linear Regression Model (Week5)
Video: Classical Normal Linear Regression Model (Week5+)
Slide: Interval Estimation, Hypothesis testing (Week5+)
Video(1): Interval Estimation for parameter estimator
Video(2): Interval Estimation for parameter estimator
Video(3): CI for variance(sigma2)
Video(4): Hypothesis Testing using CI
Video(5): Hypothesis Testing using ttest,chi2, Pvalue
Video(example): Hypothesis Testing using ttest,chi2, Pvalue
Slide: Report Results ANOVA, F-value , p-value
Reading : Interval Estimation (GTch.4-5)
Slide: Extension of the two-variables Linear regression (Week6)
Slide: Multiple Regression Analysis (Week6)
Reading: Ch.7 GT
Reading: Ch.8 GT
Slide: Multiple regression with Qualitative Variables: Dummy variables (Week7)
Slide: ความสัมพันธ์เชิงเส้นต่อกันของตัวแปรอิสระ (Multicollinearity)
Slide:Probability Linear Model / Probit/Logit (Week8)
Slide: Heteroscedasticity
Lecture note: makeup Heteroscedasticity
Time Series (Week10)
Autocorrelation ความสัมพันธ์กันเองของตัวแปรสุ่มความคลาดเคลื่อน
Regression Diagnostics
Lecturenote: Autoregression (14Nov)
Lecture note:First Difference method (19Nov)
Lecture Note-Makeup: Qualitative variable (31Oct)
MLR further _Hetero Examples

Practice with RStudio

Read ANOVA table
Unit 1: Start Using R
Unit 2: Easy Starting in R/Rstudio
Unit 3: Linear Regression with One Regression
Unit 4 : Interval Estimation, Hypothesis Testing
Unit5: Multiple Regression
Binary dependent variable regression in R part1
Binary dependent variable regression part2
Unit 6: Detect heteroscedasticity
Rscript
Unit7 :Time series Regression and autocorrelation
us_macro_quarterly.xls (for Unit7)

ProblemSets

PS4 due 2 oct (by 13.00)
Answer PS3
PS#3 (due 9 Sep. 16.00)
PS2 Updated!! (due Wed 28/8 23.50)
PS2 key
Answer PS1
PS1 (8 points)
Assignment 4 with answer
Assignment 4 by 29 Oct 23.55
data ใช้ประกอบการอธิบาย Assignment 4
Key assignment 4
Assignment 5 Heteroscedasticity
Assignment 6 Time series is due by 18,23.55 ka

Computer Exercises (do CE after PS is recommended)

CE7: TS data due Fri 11/10 by 23.00
WorkingTS data
CE#6 due Th. 25 Sep. 23.00
CE#5 Instruction due TH. 12 Sep. 20.00
CE5 Rsrcipt
CE#4 (due 5 Sep. by16.00)
CE4 Rscript
CE#3 (due Thu 29/8 16.00)
CE3 data
CE3 Answer
CE#2Answer
CE#2 (due TH 22/8 16:00)
CE#1ANSWER
CE#1 (past due)
Computer Exercise 4: Normality test
charity.csv
Computer Exercise 5
Computer Exercise 6: Multicollinearity
new elemapi2.dta
Computer Exercise 7 Heteroscedasticity
Extra Note Hetero. (makeup 8Nov.)
Heteroscedasticity Example using STATA Part 1/3
Hetero. Example ....Part 2/3
Hetero. Example .......Part 3/3
STATA example: Testing Autocorrelation for cross-sectional data
Computer Exercise 8 : Time series and Autocorrelation
Autocorrelation for Time series data
Instruction Computer Exercise 8 Multicollinearity

TermProject

Project part6: full paper report (Max 5 pages) ภายใน 23.50น.
Part5 Regression Diagnostic tests (ตรวจสอบสมการถดถอยพหุสัมพันธ์การแก้ไข) due Monday 14/10 by 22.00
Part4: Estimation (วิธีการทางเศรษฐมิติ) due Fri 4/10 by 22.00
Part3: Data description and sources, due Friday 27 Sep. by 20.00
Part2: Model Specification (Friday 20 Sep. by midnight)
Part1: Proposal_submit (Friday 6 Sep. by midnight)
Project Guideline