URP 513– Quantitative Methods for Planning
Course No.: 20674
Department: Urban and Regional Planning
Semester: 2018 Fall
Location: Hayes Hall – 106
Meeting Day(s): Monday
Meeting Time: 3:00 PM - 5:40 PM
This course is an advanced empirical methods course for graduate students in planning, policy, geography, economics, or related fields. For many graduate students, the first research project of their academic career can be challenging. At first, it may be difficult to apply theoretic models students have learned to real world projects. The objectives of this course are to facilitate the practical application of the methods students have been taught in previous basic method courses and introduce students to the empirical applications most widely used in social science research.
This course consists of three topics: linear and binary logit regression models, dummy-combined regression models and matrix calculation using input-output (IO) models. To achieve learning these models, this class applies the learned concepts empirically, emphasizing the use of MS-Excel. In the linear and logit regression models, students will learn how to run various models and predict a simulated situation based on the current data sets and conditions. Using the dummy-combined models, students will learn periodical regression models with limited observations, focusing on economic condition changes and its impacts using Excel. Finally, exposed to IO models, students are expected to apply BEA’s Social Account Matrix or other IO models and methods for their own economic projects. It is usually useful to be applied for estimating indirect and total economic impacts, based on direct impacts calculated from a researcher using the regression models learned previously.
The applications covered in this course differ from those in other statistics or econometrics courses. The focus will not be to discuss statistical issues in econometric models, but to learn how to apply the introduced tools to research projects in the social science fields of planning, policy, geography, and economics with secondary empirical datasets. Because most statistics programs provide all computation and results, this course emphasizes understanding the calculation process and interpreting the results appropriately.