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Gary Joseph Cornwall

Personal Details

First Name:Gary
Middle Name:Joseph
Last Name:Cornwall
Suffix:
RePEc Short-ID:pco974
[This author has chosen not to make the email address public]
https://garycornwall.weebly.com/
513-384-3855

Affiliation

Bureau of Economic Analysis
Department of Commerce
Government of the United States

Washington, District of Columbia (United States)
http://www.bea.gov/
RePEc:edi:beagvus (more details at EDIRC)

Research output

as
Jump to: Working papers Articles Chapters

Working papers

  1. Gary Cornwall & Jeff Chen & Beau Sauley, 2021. "Standing on the Shoulders of Machine Learning: Can We Improve Hypothesis Testing?," Papers 2103.01368, arXiv.org.

Articles

  1. Gary Cornwall & Beau Sauley, 2021. "Indirect effects and causal inference: reconsidering regression discontinuity," Journal of Spatial Econometrics, Springer, vol. 2(1), pages 1-28, December.
  2. Cornwall, Gary J. & Parent, Olivier, 2017. "Embracing heterogeneity: the spatial autoregressive mixture model," Regional Science and Urban Economics, Elsevier, vol. 64(C), pages 148-161.

Chapters

  1. Gary J. Cornwall & Jeffrey A. Mills & Beau A. Sauley & Huibin Weng, 2019. "Predictive Testing for Granger Causality via Posterior Simulation and Cross-validation," Advances in Econometrics, in: Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part A, volume 40, pages 275-292, Emerald Group Publishing Limited.
  2. Gary Cornwall & Changjoo Kim & Olivier Parent, 2017. "At the Frontier Between Local and Global Interactions in Regional Sciences," Advances in Spatial Science, in: Randall Jackson & Peter Schaeffer (ed.), Regional Research Frontiers - Vol. 2, chapter 0, pages 141-149, Springer.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

    Sorry, no citations of working papers recorded.

Articles

  1. Cornwall, Gary J. & Parent, Olivier, 2017. "Embracing heterogeneity: the spatial autoregressive mixture model," Regional Science and Urban Economics, Elsevier, vol. 64(C), pages 148-161.

    Cited by:

    1. Tamás Krisztin & Philipp Piribauer, 2021. "A Bayesian Spatial Autoregressive Logit Model With An Empirical Application to European Regional FDI Flows," WIFO Working Papers 586, WIFO.
    2. Gude, Alberto & Álvarez, Inmaculada C. & Orea, Luis, 2017. "Heterogeneous spillovers among Spanish provinces: A generalized spatial stochastic frontier model," Efficiency Series Papers 2017/03, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    3. Kuschnig, Nikolas, 2021. "Bayesian Spatial Econometrics and the Need for Software," Department of Economics Working Paper Series 318, WU Vienna University of Economics and Business.
    4. Niko Hauzenberger & Michael Pfarrhofer, 2021. "Bayesian State‐Space Modeling for Analyzing Heterogeneous Network Effects of US Monetary Policy," Scandinavian Journal of Economics, Wiley Blackwell, vol. 123(4), pages 1261-1291, October.
    5. Nikolas Kuschnig, 2022. "Bayesian spatial econometrics: a software architecture," Journal of Spatial Econometrics, Springer, vol. 3(1), pages 1-25, December.
    6. Michael Alexeev & Yao-Yu Chih, 2017. "Oil Price Shocks and Economic Growth in the Us," CAEPR Working Papers 2017-011, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    7. Michael Pfarrhofer & Philipp Piribauer, 2018. "Flexible shrinkage in high-dimensional Bayesian spatial autoregressive models," Papers 1805.10822, arXiv.org.

Chapters

    Sorry, no citations of chapters recorded.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 1 paper announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-BIG: Big Data (1) 2021-03-15. Author is listed
  2. NEP-CMP: Computational Economics (1) 2021-03-15. Author is listed
  3. NEP-ECM: Econometrics (1) 2021-03-15. Author is listed
  4. NEP-ETS: Econometric Time Series (1) 2021-03-15. Author is listed

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