Allow me to (shamelessly) piggyback on this recent ICPSR Summer Program announcement (below) to plug my new & expanded Spatial-Econometrics workshop this summer, 20-24 July ONLINE.
Please feel free, encouraged even!, to distribute widely across your networks this announcement & call for applications.
And please send, and encourage, any and all inquiries to me at [log in to unmask] <mailto:[log in to unmask]> .
THANKS! Stay safe & be well!
Rob
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Robert (Rob) J. Franzese, Jr.
Professor & Associate Chair, Department of Political Science,
The University of Michigan, Ann Arbor
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Fellow & 15th (ex)President, The Society for Political Methodology
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As you may have heard, ICPSR is doing all its courses & workshops online/remotely/virtually this summer, including my Spatial-Econometrics workshop. ICPSR is discounting these online editions of courses & workshops also (see below).
While I will miss working with this year’s cohort in person, there are some upsides perhaps for scholars who may be considering it. No travel & lodging costs if they’re outside of Ann Arbor, of course, but also the instruction will be delivered online, live and also recorded for asynchronous viewing (to accommodate time-zone variation, e.g.) and therefore also accessible for later reviewing. And, as always, participants in my workshops will retain access to all materials from the workshop—slides, notes, readings, & labs—and email access to me if I can be of future assistance, in perpetuity.
Spatial Econometrics: Empirical Analysis of Geospatial Association and Cross-Unit Interdependence*
ICPSR 5-Day Workshop, ONLINE in 2020, July 20-24 (and recorded for asynchronous participation)
Professor Robert (Rob) J. Franzese, Jr. (please send any & all inquiries about the workshop to: [log in to unmask] <mailto:[log in to unmask]> )
*NEWLY EXPANDED! Now with more intro to models and methods for spatial or geospatial _clustering_ (as opposed to contagion/interdependence) and distinguishing between the alternate sources of spatial association (common exposure, contagion, network selection).
ADVERTISEMENT: Do the outcomes in the units or individuals of your research analyses cluster geospatially or within networks? Do some units’ or individuals’ outcomes depend on outcomes in other units/individuals? That is, are the outcomes of interest in your studies likely contagious from units to neighboring or otherwise proximate or connected units? Do the processes you study diffuse across units in some manner? Are there spillovers across subjects? If you study anything in the social sciences, and likely most things beyond, almost certainly there/they are/do.
This workshop (July 20-24 ONLINE Live & Recorded) teaches empirical methods for modeling, for estimating, and for the interpretation of such spatial or cross-unit clustering (correlation) or interdependence (a.k.a., contagion/diffusion/spillover/network-dependence...).
Applied (computer-lab) sessions and exercises are bilingual, i.e. with lab scripts in Stata and R both available, and students are of course welcome to use other software as they prefer.
Register through the ICPSR Summer School portal, linked here: http://www.icpsr.umich.edu/icpsrweb/sumprog <http://www.icpsr.umich.edu/icpsrweb/sumprog/>
The course description from the ICPSR website is linked here & copied below: http://www.icpsr.umich.edu/icpsrweb/sumprog/courses/0129
Registration fees reduced: Members: Was $1800, NOW $1500; Non-Members: Was $3300, NOW $3000
Scholarships & Discounts: Please refer to our <https://umich.us3.list-manage.com/track/click?u=4de0acf23f8cd9a943b256ebd <https://umich.us3.list-manage.com/track/click?u=4de0acf23f8cd9a943b256ebd&id=59850bca74&e=e7314cdd71> &id=59850bca74&e=e7314cdd71> scholarships page for a full list of funding opportunities, and discounts: 15% discount for returning participants; 15% discount for enrolling in 2 or more short workshops, or a short workshop and a four-week session; Group discount for academic departments or programs that send 5 or more participants; Early payment discount for our four-week sessions
Please refer to our <https://umich.us3.list-manage.com/track/click?u=4de0acf23f8cd9a943b256ebd <https://umich.us3.list-manage.com/track/click?u=4de0acf23f8cd9a943b256ebd&id=a7f5904d94&e=e7314cdd71> &id=a7f5904d94&e=e7314cdd71> registration page for more information.
The fuller workshop description follows:
Spatial Econometrics: Empirical Analysis of Geospatial Association and Cross-Unit Interdependence
20-24 July 2020 (online, live and recorded for asynchronous participation &/or later review)
PLEASE DO EMAIL ([log in to unmask] <mailto:[log in to unmask]> ) with any & all questions
Description: Spatial (i.e., geospatial or otherwise cross-unit) association and interdependence are ubiquitous throughout the social sciences, and beyond. That is, events or outcomes in one observational unit are almost always related to similar occurrences in other observational units. This is so for such diverse phenomena as disturbances and conflicts within and among nations; crime, health, and environmental outcomes; economic and other policies in political jurisdictions; consumer, investor, and producer choices in markets; individuals’ opinions and behavior in societies; and voting by citizens in elections or by legislators in legislatures. In contexts where this omnipresent cross-unit association (or correlation) arises from interdependence (or contagion), "standard" statistical methods (which assume independent observations) are inappropriate, and design-based methods of "nonparametric causal-inference" are (at best) inadequate. This workshop introduces strategies appropriate for distinguishing spatial association (correlation) from spatial interdependence (contagion) and for proper estimation of processes involving interdependent observations, emphasizing spatial and spatiotemporal models of interdependent continuous and limited outcomes.
The main objective of the workshop is to demonstrate how such spatial, i.e. geo-spatial or otherwise cross-unit, interdependence can be incorporated into empirical analysis most productively. Course participants will learn how to: diagnose spatial-correlation patterns; estimate spatial-regression models; distinguish between different sources of spatial correlation (common exposure, contagion, and selection); and calculate and present the spatial and spatiotemporal effects that empirical models which incorporate interdependence imply. Methods to be covered include: measures of spatial association; models and methods for (exogenous) spatial correlation; instrumental-variable and maximum-likelihood estimators for models with (endogenous) spatial interdependence; multiple-spatial-lag models; spatial interdependence in models with limited and qualitative dependent-variables; and models for coevolutionary processes (i.e., processes with both spatial-cum-network interdependence and endogenous-connectivity/network-selection).
Prerequisites: None; in particular, although participants should be familiar with linear regression and models for qualitative/limited dependent variables (e.g., logit, probit, etc.), this workshop does not assume any prior knowledge of, or experience with, spatial statistics. Indeed, all necessary mathematical, statistical, geospatial-analytic, and spatial-econometric background will be reviewed as needed, albeit (obviously) very quickly.
From: ICPSR Summer Program in Quantitative Methods of Social Research <[log in to unmask]>
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Subject: Upcoming workshops in Bayesian and Dynamic Panel Models
Please forward this email to students, faculty, and researchers who would benefit from ICPSR courses!
<https://mailchi.mp/176c08251a59/upcoming-workshops-in-bayesian-and-dynamic-panel-models-3967421?e=e7314cdd71> View this email in your browser
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Less than 100 days left of the 2020 Summer Program!
We're almost through the second week of the program! Considering that just two months ago we announced our move to online-only instruction, we couldn't be happier with the way our workshops have run so far. In case you missed our announcement last week, we have a lot of information about how online courses function with recording, platform, and time zones <https://umich.us3.list-manage.com/track/click?u=4de0acf23f8cd9a943b256ebd&id=a05f558ada&e=e7314cdd71> posted on our homepage. Today though we're looking into the future a bit with these upcoming workshops.
<https://umich.us3.list-manage.com/track/click?u=4de0acf23f8cd9a943b256ebd&id=d710ae85ba&e=e7314cdd71> Dynamical Systems Analysis (June 15-18)
Instructors: Brian Baucom, University of Utah; Jonathan Butner, University of Utah
Dynamic systems models provide a unique lens for studying change over time; the focus in these models is on patterns of fluctuations about the mean, the stability of these patterns, and how these patterns change under different conditions. Research focused on how individuals, families, groups, and communities grow and change over time is increasingly recognizing the vast potential of dynamic systems models for advancing understanding of complex temporal phenomena.
This four day short course includes a balance of didactic instruction in background material and guided practice in estimating and interpreting models.
<https://umich.us3.list-manage.com/track/click?u=4de0acf23f8cd9a943b256ebd&id=96529d738d&e=e7314cdd71> Bayesian Multilevel Models (June 15-19)
Instructor: Ryan Bakker, University of Essex
Bayesian methods allow for an extremely flexible approach for estimating hierarchical models with a variety different types of dependent variables. The Bayesian approach simplifies several of the assumptions of the classical techniques for MLMS and directly estimates a variety of quantities of interest that require post-estimation methods in the non-Bayesian framework. Topics covered will be the hierarchical linear model, as well as a models with limited dependent variables, summarizing results, in and out of sample predictions, and measures of model fit. No prior knowledge of Bayesian modeling is required, but will be beneficial.
<https://umich.us3.list-manage.com/track/click?u=4de0acf23f8cd9a943b256ebd&id=644876a083&e=e7314cdd71> Advanced Topics in Dynamic Panel Models (July 20-22)
Instructors: Mark Pickup, Simon Fraser University; Andrew Philips, University of Colorado at Boulder
Data collected over both units (e.g., individuals, states, countries) and time (e.g., days, months, years) — known as time series cross-sectional data or panel data — are common in the social sciences. By gaining leverage both across units and over time, these data help us answer important questions that would be difficult if we only looked at a single point in time (e.g., cross section) or single unit (e.g., time series): the relationship between growth and democracy, whether or not the resource curse exists, or how economic perceptions shape support for the government. Despite these advantages, panel data often show types of heterogeneity and dynamics that make standard regression approaches inappropriate.
This course is designed to survey some advanced topics in panel data. After a review of panel data fundamentals, we will cover topics such as panel unit root and cointegration tests, panel error correction models, and approaches to modeling dynamics in panel data with a small T.
REMINDER: All 2020 Summer Program courses are being offered virtually!
<https://umich.us3.list-manage.com/track/click?u=4de0acf23f8cd9a943b256ebd&id=349ad18c25&e=e7314cdd71> View all 2020 courses
Registration now open, reduced fees!
To register for these or any of our courses visit our <https://umich.us3.list-manage.com/track/click?u=4de0acf23f8cd9a943b256ebd&id=50f58100df&e=e7314cdd71> Registration page to get started. Also on that page is our new fee structure, reduced to reflect our move online for 2020.
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