Introductory Econometrics

Using Monte Carlo Simulation with Microsoft Excel

by Humberto Barreto, Frank Howland

Subject categories
ISBNs
  • 9780521843195
  • 9780511133572
  • 9781107713796
This highly accessible and innovative text with supporting web site uses Excel (R) to teach the core concepts of econometrics without advanced mathematics. It enables students to use Monte Carlo simulations in order to understand the data generating process and sampling distribution. Intelligent repetition of concrete examples effectively conveys the properties of the ordinary least squares (OLS) estimator and the nature of heteroskedasticity and autocorrelation. Coverage includes omitted variables, binary response models, basic time series, and simultaneous equations. The authors teach students how to construct their own real-world data sets drawn from the internet, which they can analyze with Excel (R) or with other econometric software. The accompanying web site with text support can be found at www.wabash.edu/econometrics.

  • Cambridge University Press; December 2005
  • ISBN: 9780511133572
  • Read online, or download in secure PDF or secure ePub format
  • Title: Introductory Econometrics
  • Author: Humberto Barreto; Frank Howland
  • Imprint: Cambridge University Press
Subject categories
ISBNs
  • 9780521843195
  • 9780511133572
  • 9781107713796

In The Press

'Hats off to Barreto and Howland for a clearly-written text that introduces the undergraduate to data analysis and econometric techniques using Excel. The book's strength is in using Monte Carlo simulation to illustrate sampling theory and the Gauss Markov theorem. I am in total agreement with the authors that computer-based exercises help to make abstract concepts operations and meaningful. Most juniors and seniors are familiar with the basic features of Excel spreadsheets. Showing them how to use SOLVER, the DATA ANALYSIS TOOLS, and to run Monte Carlo simulations, allows an instructor to take a familiar tool (Excel) and use it to introduce undergraduates to econometrics in an intuitive and non-threatening way.' Jon M. Conrad, Cornell University