Introductory Econometrics—Using Monte Carlo Simulation with Microsoft Excel®
该书介绍了一种用Excel和蒙特卡洛模拟教授计量经济学的新方法，通过交互式示例帮助学生直观理解回归分析、数据生成过程及经典线性模型，适合初学者掌握实证研究基础。
Becoming intelligent readers of economic journal articles, ability to conduct basic empirical research and preparation for further econometric studies are prime objectives at the introductory level in most econometric curricula. The question is: What is the best way of ‘hitting the road’? The book by Barreto and Howland introduces a new approach in teaching introductory econometrics. The text and the accompanying CD-ROM use Excel spreadsheets powered by add-ins to guide the student through the core concepts of econometrics. A central part of the exposition is based on Monte Carlo simulations allowing students to work interactively in the provided examples. Students thus get a visual explanation in their hands-on work and an opportunity to observe directly the consequences of alternative specifications, alterations, etc. A persistent theme in the book is that successful econometric analysis depends on economic and statistical theory as well as a careful interpretation of the data. The core of the book is the idea that students are in vital need of recognising the non-experimental character of data and the crucial role of random elements feeding into the data. The book is divided into two parts: descriptive data analysis and inferential econometrics. The first part enables insights into how the ordinary least squares regression method as such can be used as a descriptive tool without using its inferential properties. The exposition in this part includes bivariate as well as multiple regression, functional forms and dummy variables (as intercept shifters and interaction terms). The second part of the book takes off with a focus on the effects of the data-generation process (i.e. randomness) in the estimation and interpretation of regression results. With a start in the box model for stochastic processes, the first three chapters in this part of the book builds up to the classical econometric model and a thorough treatment of the Gauss–Markov theorem. The book then proceeds to inference and hypothesis testing. Additional chapters in this part include an extensive demonstration of elementary violations of the classical linear model (including a serious discussion of omitted variable biases), time series, dummy dependent variables, bootstrapping and simultaneous equations modelling.