Peng Wang

Personal Details

First Name:	Peng
Middle Name:
Last Name:	Wang
Suffix:
RePEc Short-ID:	pwa513
	[This author has chosen not to make the email address public]
	http://www.bm.ust.hk/econ/staff/pwang.html
Terminal Degree:	2009 Department of Economics; New York University (NYU) (from RePEc Genealogy)

Affiliation

Department of Economics
Business School
Hong Kong University of Science and Technology (HKUST)

Kowloon, Hong Kong
http://www.bm.ust.hk/~econ/
RePEc:edi:deusthk (more details at EDIRC)

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.

Wikipedia or ReplicationWiki mentions

(Only mentions on Wikipedia that link back to a page on a RePEc service)

1. Jushan Bai & Peng Wang, 2011. "Conditional Markov chain and its application in economic time series analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(5), pages 715-734, August.
   • Bai, Jushan & Wang, Peng, 2011. "Conditional Markov chain and its application in economic time series analysis," MPRA Paper 33369, University Library of Munich, Germany.

   Mentioned in:

   1. Conditional Markov chain and its application in economic time series analysis (Journal of Applied Econometrics 2011) in ReplicationWiki ()

Working papers

1. Bai, Jushan & Wang, Peng, 2011. "Conditional Markov chain and its application in economic time series analysis," MPRA Paper 33369, University Library of Munich, Germany.
   • Jushan Bai & Peng Wang, 2011. "Conditional Markov chain and its application in economic time series analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(5), pages 715-734, August.

   Cited by:

   1. Chang, Kuang-Liang, 2016. "Does the return-state-varying relationship between risk and return matter in modeling the time series process of stock return?," International Review of Economics & Finance, Elsevier, vol. 42(C), pages 72-87.
   2. Raphael Homayoun Boroumand & Stéphane Goutte & Simon Porcher & Thomas Porcher, 2015. "A Conditional Markov Regime Switching Model To Study Margins: Application To The French Fuel Retail Markets," Post-Print hal-02148309, HAL.
      • Raphaël Homayoun Boroumand & Stéphane Goutte & Simon Porcher & Thomas Porcher, 2014. "A Conditional Markov Regime Switching Model to Study Margins: Application to the French Fuel Retail Markets," Working Papers hal-01090837, HAL.
   3. Danilo Leiva-Leon, 2017. "Measuring business cycles intra-synchronization in us: a regime-switching interdependence framework," Working Papers 1726, Banco de España.
      • Danilo Leiva-Leon, 2017. "Measuring Business Cycles Intra-Synchronization in US: A Regime-switching Interdependence Framework," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(4), pages 513-545, August.
   4. Goutte, Stéphane, 2014. "Conditional Markov regime switching model applied to economic modelling," Economic Modelling, Elsevier, vol. 38(C), pages 258-269.
      • Stéphane Goutte, 2012. "Conditional Markov regime switching model applied to economic modelling," Working Papers hal-00747479, HAL.
   5. Catherine Doz & Laurent Ferrara & Pierre-Alain Pionnier, 2020. "Business cycle dynamics after the Great Recession: An Extended Markov-Switching Dynamic Factor Model," PSE Working Papers halshs-02443364, HAL.
      • Catherine Doz & Laurent Ferrara & Pierre-Alain Pionnier, 2020. "Business cycle dynamics after the Great Recession: An Extended Markov-Switching Dynamic Factor Model," Working Papers halshs-02443364, HAL.
      • Catherine Doz & Laurent Ferrara & Pierre-Alain Pionnier, 2020. "Business cycle dynamics after the Great Recession: An extended Markov-Switching Dynamic Factor Model," OECD Statistics Working Papers 2020/01, OECD Publishing.
   6. Stéphane Goutte & Benteng Zou, 2012. "Continuous time regime switching model applied to foreign exchange rate," Working Papers hal-00643900, HAL.
   7. Chevallier, Julien, 2011. "A model of carbon price interactions with macroeconomic and energy dynamics," Energy Economics, Elsevier, vol. 33(6), pages 1295-1312.
   8. Leiva-Leon, Danilo, 2013. "A New Approach to Infer Changes in the Synchronization of Business Cycle Phases," MPRA Paper 54452, University Library of Munich, Germany.
      • Danilo Leiva-Leon, 2014. "A New Approach to Infer Changes in the Synchronization of Business Cycle Phases," Staff Working Papers 14-38, Bank of Canada.
   9. Shu-Ping Shi, 2013. "Specification sensitivities in the Markov-switching unit root test for bubbles," Empirical Economics, Springer, vol. 45(2), pages 697-713, October.
   10. Sylvia Kaufmann, 2016. "Hidden Markov models in time series, with applications in economics," Working Papers 16.06, Swiss National Bank, Study Center Gerzensee.
   11. Gilbert Mbara, 2017. "Business Cycle Dating after the Great Moderation: A Consistent Two – Stage Maximum Likelihood Method," Working Papers 2017-13, Faculty of Economic Sciences, University of Warsaw.
   12. Malika Hamadi & Andreas Heinen, 2011. "Ownership Structure and Firm Performance : Evidence from a non-parametric panel," DEM Discussion Paper Series 11-16, Department of Economics at the University of Luxembourg.
   13. Chevallier, Julien, 2011. "Evaluating the carbon-macroeconomy relationship: Evidence from threshold vector error-correction and Markov-switching VAR models," Economic Modelling, Elsevier, vol. 28(6), pages 2634-2656.
   14. Troy Davig, 2008. "Detecting recessions in the Great Moderation: a real-time analysis," Economic Review, Federal Reserve Bank of Kansas City, vol. 93(Q IV), pages 5-33.

Articles

1. Jushan Bai & Peng Wang, 2016. "Econometric Analysis of Large Factor Models," Annual Review of Economics, Annual Reviews, vol. 8(1), pages 53-80, October.

   Cited by:

   1. Ivan Fernandez-Val & Martin Weidner, 2017. "Fixed effect estimation of large T panel data models," CeMMAP working papers CWP42/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
      • Iv'an Fern'andez-Val & Martin Weidner, 2017. "Fixed Effect Estimation of Large T Panel Data Models," Papers 1709.08980, arXiv.org, revised Mar 2018.
      • Ivan Fernandez-Val & Martin Weidner, 2018. "Fixed effect estimation of large T panel data models," CeMMAP working papers CWP22/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
      • Iván Fernández-Val & Martin Weidner, 2018. "Fixed Effects Estimation of Large-TPanel Data Models," Annual Review of Economics, Annual Reviews, vol. 10(1), pages 109-138, August.
   2. Jianqing Fan & Kunpeng Li & Yuan Liao, 2020. "Recent Developments on Factor Models and its Applications in Econometric Learning," Papers 2009.10103, arXiv.org.
   3. Christian Glocker & Serguei Kaniovski, 2020. "Macroeconometric Forecasting Using a Cluster of Dynamic Factor Models," WIFO Working Papers 614, WIFO.
      • Christian Glocker & Serguei Kaniovski, 2022. "Macroeconometric forecasting using a cluster of dynamic factor models," Empirical Economics, Springer, vol. 63(1), pages 43-91, July.
   4. Wang,Dieter, 2021. "Natural Capital and Sovereign Bonds," Policy Research Working Paper Series 9606, The World Bank.
   5. Maldonado, Javier & Ruiz Ortega, Esther, 2017. "Accurate Subsampling Intervals of Principal Components Factors," DES - Working Papers. Statistics and Econometrics. WS 23974, Universidad Carlos III de Madrid. Departamento de Estadística.
   6. Ferrari, Davide & Ravazzolo, Francesco & Vespignani, Joaquin, 2019. "Forecasting energy commodity prices: a large global dataset sparse approach," Working Papers 2019-09, University of Tasmania, Tasmanian School of Business and Economics.
      • Ferrari, Davide & Ravazzolo, Francesco & Vespignani, Joaquin, 2021. "Forecasting energy commodity prices: A large global dataset sparse approach," Energy Economics, Elsevier, vol. 98(C).
      • Davide Ferrari & Francesco Ravazzolo & Joaquin Vespignani, 2021. "Forecasting Energy Commodity Prices: A Large Global Dataset Sparse Approach," BEMPS - Bozen Economics & Management Paper Series BEMPS83, Faculty of Economics and Management at the Free University of Bozen.
      • Davide Ferrari & Francesco Ravazzolo & Joaquin Vespignani, 2019. "Forecasting Energy Commodity Prices: A Large Global Dataset Sparse Approach," Working Papers No 11/2019, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
      • Davide Ferrari & Francesco Ravazzolo & Joaquin L. Vespignani, 2019. "Forecasting Energy Commodity Prices: A Large Global Dataset Sparse Approach," Globalization Institute Working Papers 376, Federal Reserve Bank of Dallas.
      • Davide Ferrari & Francesco Ravazzolo & Joaquin Vespignani, 2019. "Forecasting energy commodity prices: A large global dataset sparse approach," CAMA Working Papers 2019-90, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
   7. Hugo Freeman & Martin Weidner, 2021. "Linear panel regressions with two-way unobserved heterogeneity," CeMMAP working papers CWP39/21, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
   8. Milda Norkute & Vasilis Sarafidis & Takashi Yamagata & Guowei Cui, 2019. "Instrumental Variable Estimation of Dynamic Linear Panel Data Models with Defactored Regressors and a Multifactor Error Structure," Monash Econometrics and Business Statistics Working Papers 32/19, Monash University, Department of Econometrics and Business Statistics.
      • Milda Norkuté & Vasilis Sarafidis & Takashi Yamagata & Guowei Cui, 2018. "Instrumental Variable Estimation of Dynamic Linear Panel Data Models with Defactored Regressors and a Multifactor Error Structure," ISER Discussion Paper 1019r, Institute of Social and Economic Research, Osaka University, revised Apr 2019.
      • Norkutė, Milda & Sarafidis, Vasilis & Yamagata, Takashi & Cui, Guowei, 2021. "Instrumental variable estimation of dynamic linear panel data models with defactored regressors and a multifactor error structure," Journal of Econometrics, Elsevier, vol. 220(2), pages 416-446.
   9. Marc Hallin & Luis K. Hotta & João H. G Mazzeu & Carlos Cesar Trucios-Maza & Pedro L. Valls Pereira & Mauricio Zevallos, 2019. "Forecasting Conditional Covariance Matrices in High-Dimensional Time Series: a General Dynamic Factor Approach," Working Papers ECARES 2019-14, ULB -- Universite Libre de Bruxelles.
      • Trucíos Maza, Carlos César & Mazzeu, João H. G. & Hallin, Marc & Hotta, Luiz Koodi & Pereira, Pedro L. Valls & Zevallos, Mauricio, 2019. "Forecasting conditional covariance matrices in high-dimensional time series: a general dynamic factor approach," Textos para discussão 505, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
      • Carlos Trucíos & João H. G. Mazzeu & Marc Hallin & Luiz K. Hotta & Pedro L. Valls Pereira & Mauricio Zevallos, 2022. "Forecasting Conditional Covariance Matrices in High-Dimensional Time Series: A General Dynamic Factor Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(1), pages 40-52, December.
   10. Daniel Wochner, 2020. "Dynamic Factor Trees and Forests – A Theory-led Machine Learning Framework for Non-Linear and State-Dependent Short-Term U.S. GDP Growth Predictions," KOF Working papers 20-472, KOF Swiss Economic Institute, ETH Zurich.
   11. Vasilis Sarafidis & Tom Wansbeek, 2020. "Celebrating 40 Years of Panel Data Analysis: Past, Present and Future," Monash Econometrics and Business Statistics Working Papers 6/20, Monash University, Department of Econometrics and Business Statistics.
   12. Mingli Chen & Ivan Fernandez-Val & Martin Weidner, 2019. "Nonlinear factor models for network and panel data," CeMMAP working papers CWP18/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
       • Mingli Chen & Ivan Fernandez-Val & Martin Weidner, 2018. "Nonlinear factor models for network and panel data," CeMMAP working papers CWP38/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
       • Chen, Mingli & Fernández-Val, Iván & Weidner, Martin, 2021. "Nonlinear factor models for network and panel data," Journal of Econometrics, Elsevier, vol. 220(2), pages 296-324.
       • Mingli Chen & Iv'an Fern'andez-Val & Martin Weidner, 2014. "Nonlinear Factor Models for Network and Panel Data," Papers 1412.5647, arXiv.org, revised Oct 2019.
   13. Ekvall, Karl Oskar, 2022. "Targeted principal components regression," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
   14. Poncela Blanco, Maria Pilar & Ruiz Ortega, Esther & Miranda Gualdrón, Karen Alejandra, 2020. "Factor extraction using Kalman filter and smoothing: this is not just another survey," DES - Working Papers. Statistics and Econometrics. WS 30644, Universidad Carlos III de Madrid. Departamento de Estadística.
       • Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
   15. Thomaidis, Nikolaos S. & Christodoulou, Theodoros & Santos-Alamillos, Francisco J., 2023. "Handling the risk dimensions of wind energy generation," Applied Energy, Elsevier, vol. 339(C).
   16. Philipp Gersing & Christoph Rust & Manfred Deistler, 2023. "Weak Factors are Everywhere," Papers 2307.10067, arXiv.org, revised Jan 2024.
   17. Jad Beyhum & Eric Gautier, 2020. "Factor and factor loading augmented estimators for panel regression," Working Papers hal-02957008, HAL.
       • Beyhum, Jad & Gautier, Eric, 2021. "Factor and factor loading augmented estimators for panel regression," TSE Working Papers 21-1219, Toulouse School of Economics (TSE).
   18. Trucíos Maza, Carlos César & Mazzeu, João H. G. & Hotta, Luiz Koodi & Pereira, Pedro L. Valls & Hallin, Marc, 2020. "Robustness and the general dynamic factor model with infinite-dimensional space: identification, estimation, and forecasting," Textos para discussão 521, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
       • Trucíos, Carlos & Mazzeu, João H.G. & Hotta, Luiz K. & Valls Pereira, Pedro L. & Hallin, Marc, 2021. "Robustness and the general dynamic factor model with infinite-dimensional space: Identification, estimation, and forecasting," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1520-1534.
   19. Sokbae Lee & Yuan Liao & Myung Hwan Seo & Youngki Shin, 2018. "Factor-Driven Two-Regime Regression," Department of Economics Working Papers 2018-14, McMaster University.
       • Sokbae Lee & Yuan Liao & Myung Hwan Seo & Youngki Shin, 2019. "Factor-Driven Two-Regime Regression," Working Paper Series no128, Institute of Economic Research, Seoul National University.
       • Sokbae Lee & Yuan Liao & Myung Hwan Seo & Youngki Shin, 2018. "Factor-Driven Two-Regime Regression," Papers 1810.11109, arXiv.org, revised Sep 2020.
   20. Vidal-Llana, Xenxo & Uribe, Jorge M. & Guillén, Montserrat, 2023. "European stock market volatility connectedness: The role of country and sector membership," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 82(C).
   21. He, Yong & Kong, Xinbing & Trapani, Lorenzo & Yu, Long, 2023. "One-way or two-way factor model for matrix sequences?," Journal of Econometrics, Elsevier, vol. 235(2), pages 1981-2004.
   22. Francisco Corona & Pilar Poncela & Esther Ruiz, 2020. "Estimating Non-stationary Common Factors: Implications for Risk Sharing," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 37-60, January.
       • Corona, Francisco & Poncela, Pilar & Ruiz Ortega, Esther, 2017. "Estimating non-stationary common factors : Implications for risk sharing," DES - Working Papers. Statistics and Econometrics. WS 24585, Universidad Carlos III de Madrid. Departamento de Estadística.
   23. Steven Campbell & Ting-Kam Leonard Wong, 2022. "Efficient Convex PCA with applications to Wasserstein geodesic PCA and ranked data," Papers 2211.02990, arXiv.org, revised Aug 2023.
   24. Francisco Corona & Graciela Gonz'alez-Far'ias & Jes'us L'opez-P'erez, 2021. "A nowcasting approach to generate timely estimates of Mexican economic activity: An application to the period of COVID-19," Papers 2101.10383, arXiv.org.
   25. Xiao Huang, 2023. "Composite Quantile Factor Models," Papers 2308.02450, arXiv.org.
   26. Jia Chen Author-Name-First: Jia & Yongcheol Shin & Chaowen Zheng, 2023. "Dynamic Quantile Panel Data Models with Interactive Effects," Economics Discussion Papers em-dp2023-06, Department of Economics, University of Reading.
   27. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers halshs-02262202, HAL.
   28. Matthew Harding & Carlos Lamarche & Chris Muris, 2022. "Estimation of a Factor-Augmented Linear Model with Applications Using Student Achievement Data," Papers 2203.03051, arXiv.org.
   29. Carlos Cesar Trucios-Maza & João H. G Mazzeu & Luis K. Hotta & Pedro L. Valls Pereira & Marc Hallin, 2019. "On the robustness of the general dynamic factor model with infinite-dimensional space: identification, estimation, and forecasting," Working Papers ECARES 2019-32, ULB -- Universite Libre de Bruxelles.
   30. Li, Yan & Gao, Zhigen & Huang, Wei & Guo, Jianhua, 2023. "Matrix-variate data analysis by two-way factor model with replicated observations," Statistics & Probability Letters, Elsevier, vol. 202(C).
   31. Lukoianove, Tatiana & Agarwal, James & Osiyevskyy, Oleksiy, 2022. "Modeling a country's political environment using dynamic factor analysis (DFA): A new methodology for IB research," Journal of World Business, Elsevier, vol. 57(5).
   32. Ivan Fernandez-Val & Martin Weidner, 2017. "Fixed effect estimation of large T panel data models," CeMMAP working papers 42/17, Institute for Fiscal Studies.
   33. Yuki Takara & Shingo Takagi, 2023. "An empirical approach to measure unobserved cultural relations using music trade data," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 47(2), pages 205-245, June.
   34. Hugo Freeman & Martin Weidner, 2021. "Linear Panel Regressions with Two-Way Unobserved Heterogeneity," Papers 2109.11911, arXiv.org, revised Aug 2022.
   35. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," PSE Working Papers halshs-02262202, HAL.
   36. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers 2019-4, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
   37. Dimitar EFTIMOSKI, 2019. "Improving Short-Term Forecasting of Macedonian GDP: Comparing the Factor Model with the Macroeconomic Structural Equation Model," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 32-53, June.

2. Jushan Bai & Peng Wang, 2011. "Conditional Markov chain and its application in economic time series analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(5), pages 715-734, August.
   • Bai, Jushan & Wang, Peng, 2011. "Conditional Markov chain and its application in economic time series analysis," MPRA Paper 33369, University Library of Munich, Germany.
   See citations under working paper version above.
3. Chow, Gregory C. & Wang, Peng, 2010. "The empirics of inflation in China," Economics Letters, Elsevier, vol. 109(1), pages 28-30, October.

   Cited by:

   1. Muhammad Umar Draz, 2011. "Impact Of Financial Crises On Pakistan And China: A Comparative Study Of Six Decades," Journal of Global Business and Economics, Global Research Agency, vol. 3(1), pages 174-186, July.
   2. Mie Augier & Robert McNab & Jerry Guo & Phillip Karber, 2017. "Defense spending and economic growth: evidence from China, 1952–2012," Defence and Peace Economics, Taylor & Francis Journals, vol. 28(1), pages 65-90, January.
   3. Zhang, Lingxiang, 2013. "Modeling China's inflation dynamics: An MRSTAR approach," Economic Modelling, Elsevier, vol. 31(C), pages 440-446.
   4. Gregory C. Chow, 2011. "A Model for National Income Determination in Taiwan," Working Papers 1335, Princeton University, Department of Economics, Center for Economic Policy Studies..
   5. Chow, Gregory C., 2012. "A model of inflation in Taiwan," Economics Letters, Elsevier, vol. 117(2), pages 464-466.
   6. Gregory C. Chow, 2011. "A Model of Inflation in Taiwan," Working Papers 1333, Princeton University, Department of Economics, Center for Economic Policy Studies..
   7. Wang, Ying & Tu, Yundong & Chen, Song Xi, 2016. "Improving inflation prediction with the quantity theory," Economics Letters, Elsevier, vol. 149(C), pages 112-115.
   8. Tie Ying Liu & Chi Wei Su & Xu Zhao Jiang & Tsangyao Chang, 2015. "Is There Excess Liquidity in China?," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 23(3), pages 110-126, May.
   9. Zhenzhong Wang & Yundong Tu & Song Xi Chen, 2019. "Analyzing China's Consumer Price Index Comparatively with that of United States," Papers 1910.13301, arXiv.org.
   10. Zhang, Lingxiang, 2013. "Revisiting the empirics of inflation in China: A smooth transition error correction approach," Economics Letters, Elsevier, vol. 119(1), pages 68-71.

4. Wang, Shaoping & Wang, Peng & Yang, Jisheng & Li, Zinai, 2010. "A generalized nonlinear IV unit root test for panel data with cross-sectional dependence," Journal of Econometrics, Elsevier, vol. 157(1), pages 101-109, July.

   Cited by:

   1. Mao, Guangyu & Shen, Yan, 2019. "Bubbles or fundamentals? Modeling provincial house prices in China allowing for cross-sectional dependence," China Economic Review, Elsevier, vol. 53(C), pages 53-64.
   2. Hanck, Christoph & Demetrescu, Matei & Tarcolea, Adina, 2012. "IV-Based Cointegration Testing in Dependent Panels with Time-Varying Variance," VfS Annual Conference 2012 (Goettingen): New Approaches and Challenges for the Labor Market of the 21st Century 62072, Verein für Socialpolitik / German Economic Association.
      • Matei Demetrescu & Christoph Hanck & Adina I. Tarcolea, 2014. "Iv-Based Cointegration Testing In Dependent Panels With Time-Varying Variance," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(5), pages 393-406, August.
   3. Lee, Hyejin & Meng, Ming & Lee, Junsoo, 2012. "Performance of nonlinear instrumental variable unit root tests using recursive detrending methods," Economics Letters, Elsevier, vol. 117(1), pages 214-216.
   4. Xiangjun Wu & Juan Xu, 2021. "Drivers of food price in China: A heterogeneous panel SVAR approach," Agricultural Economics, International Association of Agricultural Economists, vol. 52(1), pages 67-79, January.

More information

Research fields, statistics, top rankings, if available.

Statistics

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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 3 papers 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-DGE: Dynamic General Equilibrium (2) 2012-02-01 2012-02-01
2. NEP-CBA: Central Banking (1) 2012-02-01
3. NEP-ECM: Econometrics (1) 2011-09-22
4. NEP-ETS: Econometric Time Series (1) 2011-09-22
5. NEP-MAC: Macroeconomics (1) 2012-02-01
6. NEP-OPM: Open Economy Macroeconomics (1) 2012-02-01
7. NEP-ORE: Operations Research (1) 2011-09-22

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