Time Series and Spatial Analysis II

Prof. Hays is such a passionate and inspiring methods teacher. He did not just provide us with a 'toolkit', but his hands-on approach let us develop the skills to evaluate its content and to apply it to our own substantive research. — graduate student at the University of Pittsburgh

This course covers methods for the analysis of temporal and spatial relations in time-series cross-section (TSCS) data. The focus is on advanced spatial and spatial-temporal econometric models that allow for multiple sources of spatial clustering and parameter heterogeneity.

This course is the second part in a two-course sequence. It requires participants to be familiar with the material covered by Time Series and Spatial Analysis I or have prior experience with time series and spatial analysis.


Dates

This one-week, 20-hour course runs Monday-Friday, 9:00 am-1:00 pm, June 26-30, 2017.


Instructor

Jude C. Hays (picture), University of Pittsburgh


Detailed Description

Building on the material covered by the first course in the two-course time series and spatial analysis sequence (cf. Time Series and Spatial Analysis I), this course teaches participants advanced spatial and spatial-temporal econometric models that models are critical for drawing valid statistical inferences from samples of TSCS data and useful for understanding how outcomes respond dynamically to stimuli and diffuse geographically.

The course begins with two-source spatial models that allow for both geographical spillovers and spatial clustering in unobservables and finishes with spatial-temporal models that allow for parameter heterogeneity across units.

Participants will learn how to specify, estimate, and interpret time series and spatial econometric models using the popular statistical software packages Stata and R.


Prerequisites

We strongly encourage participants to combine this course with the introductory Time Series and Spatial Analysis I. The course presumes a working knowledge of statistics and mathematics, and participants should be familiar with the material coverd by Time Series and Spatial Analysis I in addition to basic calculus, matrix algebra, and regression.


Requirements

Participants are expected to bring a WiFi-enabled laptop computer. Access to data, temporary licenses for the course software, and installation support will be provided by the Methods School.


Core Readings

Will be provided.


Suggested Readings

Pesaran, M. Hashem. 2015. Time Series and Panel Data Econometrics. Oxford: Oxford University Press.

Elhorst, Jean Paul. 2014. Spatial Econometrics: From Cross-Sectional Data to Spatial Panels. Heidelberg: Springer.

LeSage, James, and R. Kelley Pace. 2009. Introduction to Spatial Econometrics. Boca Raton, FL: CRC Press.


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