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 "excerpt": "Gary Koop is a Professor of Economics at the University of Strathclyde in Glasgow known for Bayesian econometrics, including Bayesian vector autoregressions and dynamic model averaging for forecasting.",
 "snippet": "Gary Koop is a Professor of Economics at the University of Strathclyde in Glasgow known for Bayesian econometrics, including Bayesian vector autoregressions and dynamic model averaging for forecasting.",
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 "markdown": "# Gary Koop\n\n**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.<sup>[1](https://www.strath.ac.uk/staff/koopgaryprof/)</sup> 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.<sup>[3](https://www.escoe.ac.uk/people/gary-koop/)</sup><sup> • </sup><sup>[2](https://www.strath.ac.uk/media/1newwebsite/ref/ref2021/impactcasestudies/business/Improving_policy-relevant_analysis.pdf)</sup> 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.<sup>[5](https://scholar.google.com/citations?hl=en&user=pr5F5oQAAAAJ)</sup><sup> • </sup><sup>[8](https://ideas.repec.org/top/top.person.all.html)</sup>\n\n| Key fact | Detail |\n|---|---|\n| Position | Professor of Economics, Department of Economics and Fraser of Allander Institute, University of Strathclyde, since 1 January 2006<sup>[1](https://www.strath.ac.uk/staff/koopgaryprof/)</sup><sup> • </sup><sup>[2](https://www.strath.ac.uk/media/1newwebsite/ref/ref2021/impactcasestudies/business/Improving_policy-relevant_analysis.pdf)</sup> |\n| Speciality | Bayesian econometrics, applied in recent years mainly to macroeconomic models<sup>[1](https://www.strath.ac.uk/staff/koopgaryprof/)</sup><sup> • </sup><sup>[3](https://www.escoe.ac.uk/people/gary-koop/)</sup> |\n| Signature methods | Shrinkage priors for over-parameterized VARs; large TVP-VARs with forgetting factors; dynamic model averaging/selection<sup>[2](https://www.strath.ac.uk/media/1newwebsite/ref/ref2021/impactcasestudies/business/Improving_policy-relevant_analysis.pdf)</sup><sup> • </sup><sup>[4](https://hedibert.org/wp-content/uploads/2019/03/koop-korobilis-2013-JOE.pdf)</sup> |\n| Most-cited paper | \"Impulse response analysis in nonlinear multivariate models\" (Koop, Pesaran, Potter, Journal of Econometrics, 1996), 5,807 citations<sup>[5](https://scholar.google.com/citations?hl=en&user=pr5F5oQAAAAJ)</sup> |\n| Textbooks | *Bayesian Econometrics* (Wiley, 2003); *Bayesian Econometric Methods* (Chan, Koop, Poirier, Tobias, Cambridge University Press, 2019)<sup>[6](https://strathprints.strath.ac.uk/view/author/513093.html)</sup> |\n| RePEc rank | #387 all-time (score 442.80)<sup>[8](https://ideas.repec.org/top/top.person.all.html)</sup> |\n\n## Career and affiliations\n\nKoop 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.<sup>[2](https://www.strath.ac.uk/media/1newwebsite/ref/ref2021/impactcasestudies/business/Improving_policy-relevant_analysis.pdf)</sup><sup> • </sup><sup>[3](https://www.escoe.ac.uk/people/gary-koop/)</sup> He is a Research Associate of the Economic Statistics Centre of Excellence (ESCoE).<sup>[3](https://www.escoe.ac.uk/people/gary-koop/)</sup>\n\nHis 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).<sup>[2](https://www.strath.ac.uk/media/1newwebsite/ref/ref2021/impactcasestudies/business/Improving_policy-relevant_analysis.pdf)</sup><sup> • </sup><sup>[1](https://www.strath.ac.uk/staff/koopgaryprof/)</sup> He also organized the Workshop on Macroeconomic Analysis and Forecasting for Policy and Practice on 28 November 2024.<sup>[1](https://www.strath.ac.uk/staff/koopgaryprof/)</sup>\n\n## Research contributions: Bayesian VARs, TVP-VARs, and model averaging\n\n**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.<sup>[9](https://mpra.ub.uni-muenchen.de/20125/1/kk3_final.pdf)</sup><sup> • </sup><sup>[2](https://www.strath.ac.uk/media/1newwebsite/ref/ref2021/impactcasestudies/business/Improving_policy-relevant_analysis.pdf)</sup> 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.<sup>[10](https://onlinelibrary.wiley.com/doi/10.1002/jae.1270)</sup>\n\n**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.<sup>[9](https://mpra.ub.uni-muenchen.de/20125/1/kk3_final.pdf)</sup> 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.<sup>[4](https://hedibert.org/wp-content/uploads/2019/03/koop-korobilis-2013-JOE.pdf)</sup><sup> • </sup><sup>[11](https://strathprints.strath.ac.uk/42740/1/koop_VARs_TVP_VARs.pdf)</sup> 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.<sup>[4](https://hedibert.org/wp-content/uploads/2019/03/koop-korobilis-2013-JOE.pdf)</sup>\n\n**Bayesian model averaging in macroeconomics.** Koop and Korobilis published \"Forecasting Inflation using Dynamic Model Averaging\" (*International Economic Review*, 2012).<sup>[12](https://sites.google.com/site/garykoop/research)</sup> 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.<sup>[2](https://www.strath.ac.uk/media/1newwebsite/ref/ref2021/impactcasestudies/business/Improving_policy-relevant_analysis.pdf)</sup> 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.<sup>[13](https://www.gla.ac.uk/media/Media_355850_smxx.pdf)</sup> 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.<sup>[14](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3098649)</sup>\n\n## Textbooks, code, and teaching\n\nKoop 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.<sup>[6](https://strathprints.strath.ac.uk/view/author/513093.html)</sup> 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.<sup>[15](https://www.diw.de/sixcms/media.php/demo_diw_0.a.128.de/upload/GC_syllabus_ws1718_koop_bayesian%20methods.pdf)</sup> Estima has run courses using *Bayesian Econometrics* and created RATS code for it.<sup>[16](https://sites.google.com/site/garykoop/home/computer-code-2)</sup>\n\n**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.<sup>[16](https://sites.google.com/site/garykoop/home/computer-code-2)</sup>\n\n## Applied impact: central banks, statistical agencies, and government\n\nThe 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.<sup>[2](https://www.strath.ac.uk/media/1newwebsite/ref/ref2021/impactcasestudies/business/Improving_policy-relevant_analysis.pdf)</sup> 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\".<sup>[2](https://www.strath.ac.uk/media/1newwebsite/ref/ref2021/impactcasestudies/business/Improving_policy-relevant_analysis.pdf)</sup>\n\nIn 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.<sup>[2](https://www.strath.ac.uk/media/1newwebsite/ref/ref2021/impactcasestudies/business/Improving_policy-relevant_analysis.pdf)</sup> 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.<sup>[2](https://www.strath.ac.uk/media/1newwebsite/ref/ref2021/impactcasestudies/business/Improving_policy-relevant_analysis.pdf)</sup> 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.<sup>[2](https://www.strath.ac.uk/media/1newwebsite/ref/ref2021/impactcasestudies/business/Improving_policy-relevant_analysis.pdf)</sup> 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.<sup>[17](https://ideas.repec.org/e/c/pko8.html)</sup>\n\n## By the numbers\n\nCitation 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.<sup>[7](https://topgenerator.adscientificindex.com/scientist/gary-koop/1062839/)</sup>\n\nHis 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.<sup>[5](https://scholar.google.com/citations?hl=en&user=pr5F5oQAAAAJ)</sup> 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).<sup>[5](https://scholar.google.com/citations?hl=en&user=pr5F5oQAAAAJ)</sup> In the RePEc all-time ranking he stands at #387 with a score of 442.80.<sup>[8](https://ideas.repec.org/top/top.person.all.html)</sup>\n\n## References\n\n1. [Prof Gary Koop, University of Strathclyde staff page](https://www.strath.ac.uk/staff/koopgaryprof/)\n2. [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)\n3. [Gary Koop, ESCoE profile](https://www.escoe.ac.uk/people/gary-koop/)\n4. [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)\n5. [Gary Koop, Google Scholar](https://scholar.google.com/citations?hl=en&user=pr5F5oQAAAAJ)\n6. [Strathprints author record, Gary Koop](https://strathprints.strath.ac.uk/view/author/513093.html)\n7. [Gary Koop, AD Scientific Index](https://topgenerator.adscientificindex.com/scientist/gary-koop/1062839/)\n8. [Top Economists, IDEAS/RePEc](https://ideas.repec.org/top/top.person.all.html)\n9. [Koop & Korobilis, Bayesian Multivariate Time Series Methods for Empirical Macroeconomics (MPRA working paper)](https://mpra.ub.uni-muenchen.de/20125/1/kk3_final.pdf)\n10. [Koop (2013), Forecasting with Medium and Large Bayesian VARs, Journal of Applied Econometrics](https://onlinelibrary.wiley.com/doi/10.1002/jae.1270)\n11. [Koop (2013), Using VARs and TVP-VARs with Many Macroeconomic Variables](https://strathprints.strath.ac.uk/42740/1/koop_VARs_TVP_VARs.pdf)\n12. [Gary Koop, Research page (personal website)](https://sites.google.com/site/garykoop/research)\n13. [Koop & Korobilis, Model Uncertainty in Panel Vector Autoregressive Models](https://www.gla.ac.uk/media/Media_355850_smxx.pdf)\n14. [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)\n15. [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)\n16. [Gary Koop, Computer Code page](https://sites.google.com/site/garykoop/home/computer-code-2)\n17. [Gary Koop, IDEAS/RePEc author record](https://ideas.repec.org/e/c/pko8.html)\n\n---\n*Topic: Encyclopedia › Society and history › Social and behavioral scientists › Macroeconomists and monetary economists › Macroeconometricians and time-series analysts*\n\n*Initially written Oct 10, 2026 · Edited: Oct 11, 2026; Oct 15, 2026*\n\n*Copyright 2026 EdgeChat AI, a subsidiary of Biostate AI.*\n\nLicense: Edgepedia Community License 1.0, https://www.edgepedia.org/edgepedia/license\n",
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