Time Series and Spatial Analysis I   

I love Prof. Hays, and his love for methods is infectious! He makes challenging material seem easy with his extremely clear presentations and his willingness to help. — 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. It begins with the fundamentals of time series and spatial analysis, using pure time series and cross-sectional data respectively to introduce workhorse models from both time series and spatial econometrics. Following this introduction, participants are taught to integrate the different analytical frameworks.

This course is the first part in a two-course sequence. Part two (cf. Time Series and Spatial Analysis II) covers more advanced topics, such as models that allow for multiple sources of spatial clustering and parameter heterogeneity.


Dates

This one-week, 20-hour course runs Monday-Friday, 9:00 am-1:00 pm, June 19-23, 2017.


Instructor

Jude C. Hays (picture), University of Pittsburgh


Detailed Description

Time series and spatial econometric models are critical for drawing valid statistical inferences from samples of time-series cross-section (TSCS) data and useful for understanding how outcomes respond dynamically to stimuli and diffuse geographically.

The course introduces participants to the workhorse models in time series and spatial econometrics. The emphasis is on autoregressive, moving average, and distributed lag processes in time series and spatially autoregressive models in areal data. It also introduces participants to basic spatial-temporal autoregressive models.

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

This course is the first part in a two-course sequence. More advanced methods for the analysis of temporal and spatial relations are covered by the advanced Time Series and Spatial Analysis II course.


Prerequisites

The course presumes a working knowledge of statistics and mathematics. Participants without any prior knowledge of basic calculus, matrix algebra, and regression should consider taking Regression Analysis instead.


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

Ward, Michael D., and Kristian S. Gleditsch. 2008. Spatial Regression Models. Thousand Oaks, CA: Sage Publications.

Pickup, Mark. 2014. Introduction to Time Series Analysis. Thousand Oaks, CA: Sage Publications.

Box-Steffensmeier, Janet M., John R. Freeman, and Matthew P. Hitt. 2014. Time Series Analysis for the Social Sciences. New York, NY: Cambridge University Press.


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