# Gary Koop
**Gary Koop** is a Professor of Economics at the [University of Strathclyde](https://www.edgepedia.org/university-of-strathclyde) in Glasgow whose research specialty is [Bayesian econometrics](https://www.edgepedia.org/bayesian-econometrics), the use of Bayesian statistical methods for economic data.[1](https://www.strath.ac.uk/staff/koopgaryprof/) In recent years most of his research has applied Bayesian tools to macroeconomic models, and his headline contributions include shrinkage-prior Bayesian vector autoregressions (VARs), large time-varying parameter VARs, and dynamic model averaging and selection for forecasting.[3](https://www.escoe.ac.uk/people/gary-koop/) • [2](https://www.strath.ac.uk/media/1newwebsite/ref/ref2021/impactcasestudies/business/Improving_policy-relevant_analysis.pdf) His work is widely used: [Google Scholar](https://www.edgepedia.org/google-scholar) records about 35,000 citations, and the RePEc all-time ranking of economists places him #387 worldwide with a score of 442.80.[5](https://scholar.google.com/citations?hl=en&user=pr5F5oQAAAAJ) • [8](https://ideas.repec.org/top/top.person.all.html)
| Key fact | Detail |
|---|---|
| Position | Professor of Economics, Department of Economics and Fraser of Allander Institute, University of Strathclyde, since 1 January 2006[1](https://www.strath.ac.uk/staff/koopgaryprof/) • [2](https://www.strath.ac.uk/media/1newwebsite/ref/ref2021/impactcasestudies/business/Improving_policy-relevant_analysis.pdf) |
| Speciality | Bayesian econometrics, applied in recent years mainly to macroeconomic models[1](https://www.strath.ac.uk/staff/koopgaryprof/) • [3](https://www.escoe.ac.uk/people/gary-koop/) |
| Signature methods | Shrinkage priors for over-parameterized VARs; large TVP-VARs with forgetting factors; dynamic model averaging/selection[2](https://www.strath.ac.uk/media/1newwebsite/ref/ref2021/impactcasestudies/business/Improving_policy-relevant_analysis.pdf) • [4](https://hedibert.org/wp-content/uploads/2019/03/koop-korobilis-2013-JOE.pdf) |
| Most-cited paper | "Impulse response analysis in nonlinear multivariate models" (Koop, Pesaran, Potter, Journal of Econometrics, 1996), 5,807 citations[5](https://scholar.google.com/citations?hl=en&user=pr5F5oQAAAAJ) |
| Textbooks | *Bayesian Econometrics* (Wiley, 2003); *Bayesian Econometric Methods* (Chan, Koop, Poirier, Tobias, Cambridge University Press, 2019)[6](https://strathprints.strath.ac.uk/view/author/513093.html) |
| RePEc rank | #387 all-time (score 442.80)[8](https://ideas.repec.org/top/top.person.all.html) |
## Career and affiliations
Koop has been employed at the University of Strathclyde as Professor since 1 January 2006, in the Department of Economics and the Fraser of Allander Institute.[2](https://www.strath.ac.uk/media/1newwebsite/ref/ref2021/impactcasestudies/business/Improving_policy-relevant_analysis.pdf) • [3](https://www.escoe.ac.uk/people/gary-koop/) He is a Research Associate of the Economic Statistics Centre of Excellence (ESCoE).[3](https://www.escoe.ac.uk/people/gary-koop/)
His research is supported by a series of funded projects. These include the ESRC project "Macroeconomic Forecasting in Turbulent Times" (2010–2013, £325,755), "Transforming Forecasting Capacity in Government" (2023–2028), "Central Banks' Inflation Gamble: Good Luck versus Good Models" (£134,525, 2025–2028), a 2025–2028 geopolitical risk modeling award (£90,000), and the 2026–2029 "Addressing the Economic Data Crisis" project (£134,525, John Anderson Research Studentship Scheme).[2](https://www.strath.ac.uk/media/1newwebsite/ref/ref2021/impactcasestudies/business/Improving_policy-relevant_analysis.pdf) • [1](https://www.strath.ac.uk/staff/koopgaryprof/) He also organized the Workshop on Macroeconomic Analysis and Forecasting for Policy and Practice on 28 November 2024.[1](https://www.strath.ac.uk/staff/koopgaryprof/)
## Research contributions: Bayesian VARs, TVP-VARs, and model averaging
**Shrinkage priors for large VARs.** Since Christopher Sims's 1980 work, vector autoregressions have been among the most popular multivariate models in macroeconomics, but a VAR with many variables has so many coefficients that it is seriously over-parameterized. Koop's central methodological contribution is a family of Bayesian estimation methods based on shrinkage priors, such as the Minnesota prior and stochastic search variable selection, which overcome this over-parameterization by pulling coefficients toward prior values; these methods performed well in empirical forecast-accuracy evaluations.[9](https://mpra.ub.uni-muenchen.de/20125/1/kk3_final.pdf) • [2](https://www.strath.ac.uk/media/1newwebsite/ref/ref2021/impactcasestudies/business/Improving_policy-relevant_analysis.pdf) In a forecasting exercise on a US dataset of 168 variables, Koop found that Bayesian VARs tend to forecast better than factor methods, and that the simple Minnesota prior forecasts well in medium and large VARs; he also argued for evaluating forecasts using the entire predictive density rather than point forecasts alone.[10](https://onlinelibrary.wiley.com/doi/10.1002/jae.1270)
**Time-varying parameters and macroeconomic instability.** Evidence that the macroeconomy of the 1960s and 1970s differed from that of the 1980s and 1990s, including the [Great Moderation](https://www.edgepedia.org/great-moderation), motivated time-varying parameter VARs, in which coefficients and volatilities change over time.[9](https://mpra.ub.uni-muenchen.de/20125/1/kk3_final.pdf) Fully Bayesian estimation of such models by [Markov chain Monte Carlo](https://www.edgepedia.org/markov-chain-monte-carlo) (MCMC) can be computationally infeasible for large systems, so Koop and Dimitris Korobilis drew on forgetting-factor approximations from Raftery, Karny, and Ettler (2010) to make dynamic model averaging and selection (DMA/DMS) feasible even in very large models.[4](https://hedibert.org/wp-content/uploads/2019/03/koop-korobilis-2013-JOE.pdf) • [11](https://strathprints.strath.ac.uk/42740/1/koop_VARs_TVP_VARs.pdf) Their 2013 *Journal of Econometrics* paper defines small (trivariate), medium (seven-variable), and large (25-variable) TVP-VARs and lets the forecasting model switch dimension over time, forecasting US inflation, real output, and interest rates; the application highlights the importance of both time-varying model dimension and stochastic volatility.[4](https://hedibert.org/wp-content/uploads/2019/03/koop-korobilis-2013-JOE.pdf)
**Bayesian model averaging in macroeconomics.** Koop and Korobilis published "Forecasting Inflation using Dynamic Model Averaging" (*International Economic Review*, 2012).[12](https://sites.google.com/site/garykoop/research) The practical reach of this line of work is documented in a REF 2021 impact case study: the European Commission's Directorate-General for Economic and Financial Affairs described Koop's model averaging techniques as "the backbone" of its nowcasting framework, combining traditional data and large quantities of Big Data to produce a weekly nowcast update sent to DG-ECFIN and the Cabinets during COVID-19.[2](https://www.strath.ac.uk/media/1newwebsite/ref/ref2021/impactcasestudies/business/Improving_policy-relevant_analysis.pdf) Koop and Korobilis also developed [Bayesian model averaging](https://www.edgepedia.org/bayesian-model-averaging) and selection methods (an approach they call S4) for panel VARs, applied to the euro area sovereign debt crisis; their impulse responses were much more precisely estimated, and the findings contradicted a simple view of the crisis as contagion within the periphery, with spillovers running largely from core to periphery.[13](https://www.gla.ac.uk/media/Media_355850_smxx.pdf) A 2017 review of Bayesian methods for empirical macroeconomics with Big Data culminates in a large multi-country VAR with 133 variables, seven for each of 19 countries.[14](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3098649)
## Textbooks, code, and teaching
Koop has written two widely used graduate texts. *Bayesian Econometrics* (John Wiley & Sons, 2003, ISBN 978-0-470-84567-7) covers the Bayesian basics, and *Bayesian Econometric Methods* (with Joshua Chan, Dale J. Poirier, and Justin L. Tobias, [Cambridge University Press](https://www.edgepedia.org/cambridge-university-press), 2019, ISBN 9781108423380) is the second edition of their graduate methods book.[6](https://strathprints.strath.ac.uk/view/author/513093.html) His own graduate course materials assign the Wiley book for Bayesian fundamentals and his monograph with Korobilis, *Bayesian Multivariate Time Series Methods for Empirical Macroeconomics*, for the Bayesian VAR and TVP-VAR portion, alongside his "Bayesian Methods for Fat Data" (2016) material covering shrinkage priors, SSVS, and LASSO for Big Data.[15](https://www.diw.de/sixcms/media.php/demo_diw_0.a.128.de/upload/GC_syllabus_ws1718_koop_bayesian%20methods.pdf) Estima has run courses using *Bayesian Econometrics* and created RATS code for it.[16](https://sites.google.com/site/garykoop/home/computer-code-2)
**Code distribution.** Koop maintains an official computer-code page with Matlab implementations of BVARs with six priors, TVP-VAR code using the Carter and Kohn (1994) algorithm as implemented in Primiceri (2005) and Durbin and Koopman (2002), FAVAR and TVP-FAVAR code, and Dynamic Model Averaging code for the inflation-forecasting paper; the TVP-VAR stochastic volatility code was amended in 2014 to reflect the Del Negro and Primiceri (2013) corrigendum.[16](https://sites.google.com/site/garykoop/home/computer-code-2)
## Applied impact: central banks, statistical agencies, and government
The REF 2021 impact case study documents how Koop's methods entered policy institutions on three continents. With the [European Central Bank](https://www.edgepedia.org/european-central-bank), his dynamic model selection methodology used "Google probabilities" derived from internet search data; tests on nine major monthly US macroeconomic variables showed that DMS methods provide large improvements in nowcasting, with Google probabilities often further increasing performance.[2](https://www.strath.ac.uk/media/1newwebsite/ref/ref2021/impactcasestudies/business/Improving_policy-relevant_analysis.pdf) His 2019 work with the ECB confirmed the existence of a [Phillips curve](https://www.edgepedia.org/phillips-curve) in the euro area and provided reassurance that it "is still a valid policy instrument once it is robustly estimated".[2](https://www.strath.ac.uk/media/1newwebsite/ref/ref2021/impactcasestudies/business/Improving_policy-relevant_analysis.pdf)
In the United States, Koop's inflation research was used by the Federal Reserve Bank of New York for FOMC briefing analysis, and the [Federal Reserve Bank of Cleveland](https://www.edgepedia.org/federal-reserve-bank-of-cleveland) developed a model for assessing and forecasting inflation trends based on his work.[2](https://www.strath.ac.uk/media/1newwebsite/ref/ref2021/impactcasestudies/business/Improving_policy-relevant_analysis.pdf) In the UK, Koop and Stuart McIntyre developed mixed-frequency VAR models that enabled the [Office for National Statistics](https://www.edgepedia.org/office-for-national-statistics) to produce faster regional GDP estimates dating back to 1970.[2](https://www.strath.ac.uk/media/1newwebsite/ref/ref2021/impactcasestudies/business/Improving_policy-relevant_analysis.pdf) With Korobilis he also created a financial conditions index (*European Economic Review*, 2014, 71: 101–116) using factor-augmented VARs with time-varying coefficients and stochastic volatility to compress many financial variables into fewer variables.[2](https://www.strath.ac.uk/media/1newwebsite/ref/ref2021/impactcasestudies/business/Improving_policy-relevant_analysis.pdf) A 2024 paper with Tony Chernis, Emily Tallman, and [Mike West](https://www.edgepedia.org/mike-west), "Decision synthesis in monetary policy" (arXiv 2406.03321, also a Bank of Canada staff working paper), addresses how monetary policy decision-making can synthesize model outputs.[17](https://ideas.repec.org/e/c/pko8.html)
## By the numbers
Citation counts differ across databases, and both figures are given here. The AD Scientific Index, a scientometrics aggregator, reports 44,672 total citations (21,869 recent), an h-index of 72 (recent 41), and an i10-index of 163 (recent 108), placing Koop #12 at Strathclyde by total h-index and #4 by total citations.[7](https://topgenerator.adscientificindex.com/scientist/gary-koop/1062839/)
His most-cited works span his career. "Impulse response analysis in nonlinear multivariate models" with Hashem Pesaran and Simon Potter (*Journal of Econometrics*, 1996) has 5,807 citations.[5](https://scholar.google.com/citations?hl=en&user=pr5F5oQAAAAJ) Other highly cited items include *Bayesian Econometrics* (2003), *Bayesian multivariate time series methods for empirical macroeconomics* (2010, 1,173), "Do recessions permanently change output?" (*Journal of Monetary Economics*, 1993, 664), "Large time-varying parameter VARs" (2013, 531), and *Bayesian Econometric Methods* (2019, 612).[5](https://scholar.google.com/citations?hl=en&user=pr5F5oQAAAAJ) In the RePEc all-time ranking he stands at #387 with a score of 442.80.[8](https://ideas.repec.org/top/top.person.all.html)
## References
1. [Prof Gary Koop, University of Strathclyde staff page](https://www.strath.ac.uk/staff/koopgaryprof/)
2. [REF 2021 impact case study: Improving policy-relevant analysis in the UK, Europe and USA through novel macroeconometric methods](https://www.strath.ac.uk/media/1newwebsite/ref/ref2021/impactcasestudies/business/Improving_policy-relevant_analysis.pdf)
3. [Gary Koop, ESCoE profile](https://www.escoe.ac.uk/people/gary-koop/)
4. [Koop & Korobilis (2013), Large time-varying parameter VARs, Journal of Econometrics 177(2)](https://hedibert.org/wp-content/uploads/2019/03/koop-korobilis-2013-JOE.pdf)
5. [Gary Koop, Google Scholar](https://scholar.google.com/citations?hl=en&user=pr5F5oQAAAAJ)
6. [Strathprints author record, Gary Koop](https://strathprints.strath.ac.uk/view/author/513093.html)
7. [Gary Koop, AD Scientific Index](https://topgenerator.adscientificindex.com/scientist/gary-koop/1062839/)
8. [Top Economists, IDEAS/RePEc](https://ideas.repec.org/top/top.person.all.html)
9. [Koop & Korobilis, Bayesian Multivariate Time Series Methods for Empirical Macroeconomics (MPRA working paper)](https://mpra.ub.uni-muenchen.de/20125/1/kk3_final.pdf)
10. [Koop (2013), Forecasting with Medium and Large Bayesian VARs, Journal of Applied Econometrics](https://onlinelibrary.wiley.com/doi/10.1002/jae.1270)
11. [Koop (2013), Using VARs and TVP-VARs with Many Macroeconomic Variables](https://strathprints.strath.ac.uk/42740/1/koop_VARs_TVP_VARs.pdf)
12. [Gary Koop, Research page (personal website)](https://sites.google.com/site/garykoop/research)
13. [Koop & Korobilis, Model Uncertainty in Panel Vector Autoregressive Models](https://www.gla.ac.uk/media/Media_355850_smxx.pdf)
14. [Koop, Bayesian Methods for Empirical Macroeconomics with Big Data, Review of Economic Analysis 9(1)](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3098649)
15. [Bayesian Methods for Empirical Macroeconomics course syllabus, DIW Berlin](https://www.diw.de/sixcms/media.php/demo_diw_0.a.128.de/upload/GC_syllabus_ws1718_koop_bayesian%20methods.pdf)
16. [Gary Koop, Computer Code page](https://sites.google.com/site/garykoop/home/computer-code-2)
17. [Gary Koop, IDEAS/RePEc author record](https://ideas.repec.org/e/c/pko8.html)
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*Topic: Encyclopedia › Society and history › Social and behavioral scientists › Macroeconomists and monetary economists › Macroeconometricians and time-series analysts*
*Initially written Oct 10, 2026 · Edited: Oct 11, 2026; Oct 15, 2026*
*Copyright 2026 EdgeChat AI, a subsidiary of Biostate AI.*
License: Edgepedia Community License 1.0, https://www.edgepedia.org/edgepedia/license