Topics on Computational Econometrics

April 10, 12, 14, 15, 17, 19 (TBA)
Kuan-Pin Lin
Professor of Economics
Portland State University and WISE/SOE Xiamen University
(Last updated: 04/15/2017)

Introduction

Economic data observations come in different forms and structures. Data structures such as cross sections, time series, and panel data are familiar in economics. Based on economic theory and statistical methods, econometrics addresses issues of causal inference among economic variables. The goal of econometric analysis is for a reliable prediction and a better decision making. With the advances of information technology and rapid growth of data collection, current state of econometric analysis faces the challenge of using massive datasets and computation intensive methods. This course presents case studies of computational econometrics with open source R/RStudio software.

Suggested Readings

Topics

Subject to time constraint and program revision, the following introductory topics and case studies will be selected and discussed during this short course:
  1. Economic Data Analysis Using R
  2. Econometric Computing in the Cloud
  3. State Space Time Series Analysis and Forecasting
  4. Case Studies:
    1. The Economist's Big Mac Price Index (1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7)
    2. Wine Price in Vancouver BC, Canada (2.1, 2.2, 2.3, 2.4)
    3. U. S. Misery Index (3.1, 3.2, 3.3, 3.4, Notes)
    4. Chinese Yuan and Stock Market (6.1, 6.2, 6.3, 6.4)
      (7.1, 7.2, 7.3, 7.4, 8.1)

Expectation