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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)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Wang Peng & Heng-fu Zou, 2012. "International Macroeconomic Policy: When Wealth Affects People's Impatience," CEMA Working Papers 530, China Economics and Management Academy, Central University of Finance and Economics.
  2. Wang Peng & Heng-fu Zou, 2012. "Capital Accumulation And Present-biased Preference," CEMA Working Papers 531, China Economics and Management Academy, Central University of Finance and Economics.
  3. Wang Peng & Heng-fu Zou, 2012. "When Wealth Affects People's Impatience," CEMA Working Papers 529, China Economics and Management Academy, Central University of Finance and Economics.
  4. Bai, Jushan & Wang, Peng, 2011. "Conditional Markov chain and its application in economic time series analysis," MPRA Paper 33369, University Library of Munich, Germany.

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.
  2. Zhuangxiong Yu & Meijin Wang & Peng Wang, 2013. "Residual-based IV estimation of dynamic panel data models with fixed effects," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 67(2), pages 121-144, May.
  3. 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.
  4. Chow, Gregory C. & Wang, Peng, 2010. "The empirics of inflation in China," Economics Letters, Elsevier, vol. 109(1), pages 28-30, October.
  5. 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.

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.

    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.

    Cited by:

    1. 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.
    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.
    3. 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.
    4. 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.
    5. Danilo Leiva-Leon, 2017. "Measuring business cycles intra-synchronization in us: a regime-switching interdependence framework," Working Papers 1726, Banco de España.
    6. Stéphane Goutte & Benteng Zou, 2012. "Continuous time regime switching model applied to foreign exchange rate," Working Papers hal-00643900, HAL.
    7. 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.
    8. 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.
    9. 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.
    10. Kenneth Martínez & David Claudio & Jasmina Burek, 2025. "Modeling resilience and survivability as stochastic processes with techno-human-economic systems under stress," Journal of Computational Social Science, Springer, vol. 8(1), pages 1-42, February.
    11. Goutte, Stéphane, 2014. "Conditional Markov regime switching model applied to economic modelling," Economic Modelling, Elsevier, vol. 38(C), pages 258-269.
    12. 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.
    13. Chevallier, Julien, 2011. "A model of carbon price interactions with macroeconomic and energy dynamics," Energy Economics, Elsevier, vol. 33(6), pages 1295-1312.
    14. Sylvia Kaufmann, 2016. "Hidden Markov models in time series, with applications in economics," Working Papers 16.06, Swiss National Bank, Study Center Gerzensee.
    15. 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.

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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Wang,Dieter, 2021. "Natural Capital and Sovereign Bonds," Policy Research Working Paper Series 9606, The World Bank.
    6. Xiao Huang, 2023. "Composite Quantile Factor Model," Papers 2308.02450, arXiv.org, revised Nov 2024.
    7. 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.
    8. 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).
    9. Hugo Freeman & Martin Weidner, 2021. "Linear Panel Regressions with Two-Way Unobserved Heterogeneity," Papers 2109.11911, arXiv.org, revised Aug 2022.
    10. Raffaella Giacomini & Katja Smetanina & Jason Lu, 2024. "Perceived shocks and impulse responses," IFS Working Papers WCWP21/24, Institute for Fiscal Studies.
    11. Milda Norkuté & Vasilis Sarafidis & Takashi Yamagata, 2018. "Instrumental Variable Estimation of Dynamic Linear Panel Data Models with Defactored Regressors and a Multifactor Error Structure," ISER Discussion Paper 1019, Institute of Social and Economic Research, The University of Osaka.
    12. 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.
    13. Jianqing Fan & Yuling Yan & Yuheng Zheng, 2024. "When can weak latent factors be statistically inferred?," Papers 2407.03616, arXiv.org, revised Sep 2024.
    14. Thomaidis, Nikolaos S. & Christodoulou, Theodoros & Santos-Alamillos, Francisco J., 2023. "Handling the risk dimensions of wind energy generation," Applied Energy, Elsevier, vol. 339(C).
    15. 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.
    16. Philipp Gersing & Matteo Barigozzi & Christoph Rust & Manfred Deistler, 2023. "The Canonical Decomposition of Factor Models: Weak Factors are Everywhere," Papers 2307.10067, arXiv.org, revised Feb 2025.
    17. Raffaella Giacomini & Jason Lu & Katja Smetanina, 2024. "Perceived shocks and impulse responses," CeMMAP working papers 21/24, Institute for Fiscal Studies.
    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).
    19. Christian Glocker & Serguei Kaniovski, 2022. "Macroeconometric forecasting using a cluster of dynamic factor models," Empirical Economics, Springer, vol. 63(1), pages 43-91, July.
    20. 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).
    21. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers 2019-4, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    22. 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.
    23. Valdério Anselmo Reisen & Céline Lévy-Leduc & Edson Zambon Monte & Pascal Bondon, 2024. "A dimension reduction factor approach for multivariate time series with long-memory: a robust alternative method," Statistical Papers, Springer, vol. 65(5), pages 2865-2886, July.
    24. 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.
    25. Beyhum, Jad & Gautier, Eric, 2021. "Factor and factor loading augmented estimators for panel regression," TSE Working Papers 21-1219, Toulouse School of Economics (TSE).
    26. Corona, Francisco & Poncela, Pilar, 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.
    27. 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.
    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. 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.
    30. Freeman, Hugo & Weidner, Martin, 2023. "Linear panel regressions with two-way unobserved heterogeneity," Journal of Econometrics, Elsevier, vol. 237(1).
    31. Steven Campbell & Ting-Kam Leonard Wong, 2022. "Efficient convex PCA with applications to Wasserstein GPCA and ranked data," Papers 2211.02990, arXiv.org, revised Aug 2024.
    32. 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.
    33. 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).
    34. 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.
    35. Ekvall, Karl Oskar, 2022. "Targeted principal components regression," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
    36. Sokbae Lee & Yuan Liao & Myung Hwan Seo & Youngki Shin, 2018. "Factor-Driven Two-Regime Regression," Department of Economics Working Papers 2018-14, McMaster University.
    37. Jianqing Fan & Kunpeng Li & Yuan Liao, 2020. "Recent Developments on Factor Models and its Applications in Econometric Learning," Papers 2009.10103, arXiv.org.
    38. Liliana Forzani & Daniela Rodriguez & Mariela Sued, 2025. "A penalization method to estimate the intrinsic dimensionality of data," Statistical Papers, Springer, vol. 66(2), pages 1-20, February.
    39. Ansgar Steland, 2024. "Are minimum variance portfolios in multi-factor models long in low-beta assets?," Mathematics and Financial Economics, Springer, volume 18, number 6, February.
    40. 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.
    41. Timothy B. Armstrong & Martin Weidner & Andrei Zeleneev, 2024. "Robust estimation and inference in panels with interactive fixed effects," CeMMAP working papers 28/24, Institute for Fiscal Studies.
    42. 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.

  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.
    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. 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.
    2. Chow, Gregory C., 2012. "A model of inflation in Taiwan," Economics Letters, Elsevier, vol. 117(2), pages 464-466.
    3. Gregory C. Chow, 2011. "A Model of Inflation in Taiwan," Working Papers 1333, Princeton University, Department of Economics, Center for Economic Policy Studies..
    4. Zhang, Lingxiang, 2013. "Modeling China's inflation dynamics: An MRSTAR approach," Economic Modelling, Elsevier, vol. 31(C), pages 440-446.
    5. Wang, Ying & Tu, Yundong & Chen, Song Xi, 2016. "Improving inflation prediction with the quantity theory," Economics Letters, Elsevier, vol. 149(C), pages 112-115.
    6. 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.
    7. 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..
    8. 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.
    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.
    3. 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.
    4. 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.

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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