### 2016　Advanced Topics in Econometrics

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Graduate major in Industrial Engineering and Economics
Instructor(s)
Higuchi Yoichiro
Course component(s)
Lecture
Day/Period(Room No.)
Tue3-4(W9-626)  Fri3-4(W9-626)
Group
-
Course number
IEE.B434
Credits
2
2016
Offered quarter
4Q
Syllabus updated
2016/4/27
Lecture notes updated
-
Language used
Japanese
Access Index

### Course description and aims

Big-data science is recently developed in quantity, of course, but also expanding to the time-series direction and to spatial dimensions. This multi-dimensionalization can be treated efficiently with tools of matrix algebra and matrix differentiation. In this lecture, we first study these matrix tools, and then, choosing spatial econometrics among fields towards which economics and econometrics are challenging, we study frontier techniques and knowledge in the field.

### Student learning outcomes

First of all, by studying matrix algebra and matrix differentiation in statistics and econometrics, we acquire maneuvering technique to handle spatial data. Then, we study various models and methods for count data analysis to handle spatial interaction dta such as international trade and inter-regional migration. Finally we study spatial autocorrelation models and their applications to spatial data and spatial interaction data.

### Keywords

Matrix Algebra, Matrix Differentiation, Spatial Datam Count Data, Gravity Model, International Trade, Inter-regional Migration, Spatial Interaction Data, Spatial Autocorrelation Model

### Competencies that will be developed

Intercultural skills Communication skills Specialist skills Critical thinking skills Practical and/or problem-solving skills
- - - -

### Class flow

Scheduled contents are then lectured with reference documents handed out. Exercises and quizes are given as homeworks Answers to these homeworks are explained in the following class.

### Course schedule/Required learning

Course schedule Required learning
Class 1 Ｉｎｔｒｏｄｕｃｔｉｏｎ None
Class 2 Matrix Algebra and Matrix Differentials Exercises of Matrix Differentials(1)
Class 3 Count data analysis: Regression with Single-variate discrete distribution (Binomial and Poisson distribution) Exercises of Matrix Differentials(2)
Class 4 Count data analysis: Expansion of Regression with Single-variate discrete distribution Related Exercises (1)
Class 5 Count data analysis: Regression with Multivariate discrete distributions
Class 6 Count data analysis: Model Identification Related Exercises (2)
Class 7 Spatial Interaction Data analysis: Trade and Migration
Class 8 Spatial Interaction Data analysis: Structure and Characteristics of Spatial Intraction Models Related Exercises (3)
Class 9 Spatial Interaction Data analysis: Application of Linear Regression
Class 10 Spatial Interaction Data analysis: Application of Single-variate Discrete Distribution Related Exercises (4)
Class 11 Spatial Interaction Data analysis: Application of Multivariate Discrete Distribution
Class 12 Spatial Interaction Data analysis: Model Identification Related Exercises (5)
Class 13 Spatial Econometrics: Spatial Autocorrelation Model
Class 14 Spatial Econometrics: Spatial Autoregression Model Related Exercises (6)
Class 15 Application of Spatial Econometric Models to Spatial Interaction Analysis

### Textbook(s)

No particular textbook

### Reference books, course materials, etc.

Wooldridge, J. Econometrc Ananlysis of Cross Section and Panel Data，２ｎd ed.
Cameron & Trivedi(2005) Microeconometrics, Methods and Applications
Cameron & Trivedi(2013) Regression Analysis of Count Data, 2nd ed.
LeSage & Pace(2009) Introduction to Spatial Econometrics

### Assessment criteria and methods

70% by Short tests and homeworks, and 30% by Final Report

### Related courses

• IEE.B207 ： Econometrics I
• IEE.B301 ： Econometrics II
• IEE.B336 ： Applied Econometrics