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Miguel de Carvalho

Personal Details

First Name:Miguel
Middle Name:
Last Name:de Carvalho
Suffix:
RePEc Short-ID:pde821
http://www.maths.ed.ac.uk/~mdecarv/

Affiliation

School of Mathematics, University of Edinburgh

http://www.maths.ed.ac.uk/
Edinburgh, United Kingdom

Research output

as
Jump to: Working papers Articles

Working papers

  1. Daniela Castro Camilo & Miguel de Carvalho & Jennifer Wadsworth, 2017. "Time-Varying Extreme Value Dependence with Application to Leading European Stock Markets," Papers 1709.01198, arXiv.org.
  2. António Rua & Miguel de Carvalho, 2014. "Real-time nowcasting the US output gap: Singular spectrum analysis at work," Working Papers w201416, Banco de Portugal, Economics and Research Department.
  3. António Rua & Miguel de Carvalho, 2010. "Extremal Dependence in International Output Growth: Tales from the Tails," Working Papers w201008, Banco de Portugal, Economics and Research Department.
  4. António Rua & Miguel de Carvalho, 2010. "Tracking the US Business Cycle With a Singular Spectrum Analysis," Working Papers w201009, Banco de Portugal, Economics and Research Department.
  5. António Rua & Miguel de Carvalho, 2010. "Nonstationary Extremes and the US Business Cycle," Working Papers w201003, Banco de Portugal, Economics and Research Department.
  6. Miguel de Carvalho & Paulo Júlio, 2010. "Digging Out the PPP Hypothesis: an Integrated Empirical Coverage," GEE Papers 0024, Gabinete de Estratégia e Estudos, Ministério da Economia, revised Sep 2010.

Articles

  1. Sitsofe Tsagbey & Miguel de Carvalho & Garritt L. Page, 2017. "All Data are Wrong, but Some are Useful? Advocating the Need for Data Auditing," The American Statistician, Taylor & Francis Journals, vol. 71(3), pages 231-235, July.
  2. Hanson, Timothy E. & de Carvalho, Miguel & Chen, Yuhui, 2017. "Bernstein polynomial angular densities of multivariate extreme value distributions," Statistics & Probability Letters, Elsevier, vol. 128(C), pages 60-66.
  3. de Carvalho, Miguel & Rua, António, 2017. "Real-time nowcasting the US output gap: Singular spectrum analysis at work," International Journal of Forecasting, Elsevier, vol. 33(1), pages 185-198.
  4. Miguel de Carvalho, 2016. "Mean, What do You Mean?," The American Statistician, Taylor & Francis Journals, vol. 70(3), pages 270-274, July.
  5. Miguel Carvalho & António Rua, 2014. "Extremal Dependence in International Output Growth: Tales from the Tails," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(4), pages 605-620, August.
  6. Miguel de Carvalho & Anthony C. Davison, 2014. "Spectral Density Ratio Models for Multivariate Extremes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(506), pages 764-776, June.
  7. M. de Carvalho & K. F. Turkman & A. Rua, 2013. "Dynamic threshold modelling and the US business cycle," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(4), pages 535-550, August.
  8. Miguel Carvalho & Paulo Júlio, 2012. "Digging out the PPP hypothesis: an integrated empirical coverage," Empirical Economics, Springer, vol. 42(3), pages 713-744, June.
  9. Miguel de Carvalho & Filipe Marques, 2012. "Jackknife Euclidean Likelihood-Based Inference for Spearman's Rho," North American Actuarial Journal, Taylor & Francis Journals, vol. 16(4), pages 487-492.
  10. de Carvalho, Miguel & Rodrigues, Paulo C. & Rua, António, 2012. "Tracking the US business cycle with a singular spectrum analysis," Economics Letters, Elsevier, vol. 114(1), pages 32-35.

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

  1. Daniela Castro Camilo & Miguel de Carvalho & Jennifer Wadsworth, 2017. "Time-Varying Extreme Value Dependence with Application to Leading European Stock Markets," Papers 1709.01198, arXiv.org.

    Cited by:

    1. Raphaël Huser & Marc G. Genton, 2016. "Non-Stationary Dependence Structures for Spatial Extremes," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(3), pages 470-491, September.

  2. António Rua & Miguel de Carvalho, 2014. "Real-time nowcasting the US output gap: Singular spectrum analysis at work," Working Papers w201416, Banco de Portugal, Economics and Research Department.

    Cited by:

    1. Paulo Canas Rodrigues & Olushina Olawale Awe & Jonatha Sousa Pimentel & Rahim Mahmoudvand, 2020. "Modelling the Behaviour of Currency Exchange Rates with Singular Spectrum Analysis and Artificial Neural Networks," Stats, MDPI, vol. 3(2), pages 1-21, June.
    2. Jiang, Ping & Liu, Zhenkun & Niu, Xinsong & Zhang, Lifang, 2021. "A combined forecasting system based on statistical method, artificial neural networks, and deep learning methods for short-term wind speed forecasting," Energy, Elsevier, vol. 217(C).
    3. Hassani, Hossein & Rua, António & Silva, Emmanuel Sirimal & Thomakos, Dimitrios, 2019. "Monthly forecasting of GDP with mixed-frequency multivariate singular spectrum analysis," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1263-1272.
    4. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Working Papers 22-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    5. Bógalo, Juan & Poncela, Pilar & Senra, Eva, 2017. "Automatic Signal Extraction for Stationary and Non-Stationary Time Series by Circulant SSA," MPRA Paper 76023, University Library of Munich, Germany.
    6. Marcelo C. Medeiros & Henrique F. Pires, 2021. "The Proper Use of Google Trends in Forecasting Models," Papers 2104.03065, arXiv.org, revised Apr 2021.
    7. de Carvalho, Miguel & Martos, Gabriel, 2020. "Brexit: Tracking and disentangling the sentiment towards leaving the EU," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1128-1137.
    8. Juan B'ogalo & Pilar Poncela & Eva Senra, 2020. "Understanding fluctuations through Multivariate Circulant Singular Spectrum Analysis," Papers 2007.07561, arXiv.org, revised Aug 2023.
    9. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Papers 2202.04146, arXiv.org, revised Oct 2024.

  3. António Rua & Miguel de Carvalho, 2010. "Extremal Dependence in International Output Growth: Tales from the Tails," Working Papers w201008, Banco de Portugal, Economics and Research Department.

    Cited by:

    1. António Rua & Miguel de Carvalho, 2014. "Real-time nowcasting the US output gap: Singular spectrum analysis at work," Working Papers w201416, Banco de Portugal, Economics and Research Department.

  4. António Rua & Miguel de Carvalho, 2010. "Tracking the US Business Cycle With a Singular Spectrum Analysis," Working Papers w201009, Banco de Portugal, Economics and Research Department.

    Cited by:

    1. Paulo Canas Rodrigues & Olushina Olawale Awe & Jonatha Sousa Pimentel & Rahim Mahmoudvand, 2020. "Modelling the Behaviour of Currency Exchange Rates with Singular Spectrum Analysis and Artificial Neural Networks," Stats, MDPI, vol. 3(2), pages 1-21, June.
    2. Rocco S, Claudio M., 2013. "Singular spectrum analysis and forecasting of failure time series," Reliability Engineering and System Safety, Elsevier, vol. 114(C), pages 126-136.
    3. Miguel de Carvalho & Gabriel Martos, 2022. "Modeling interval trendlines: Symbolic singular spectrum analysis for interval time series," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 167-180, January.
    4. Lisa Sella & Gianna Vivaldo & Andreas Groth & Michael Ghil, 2016. "Economic Cycles and Their Synchronization: A Comparison of Cyclic Modes in Three European Countries," Post-Print hal-01701122, HAL.
    5. Andreas Groth & Michael Ghil & Stéphane Hallegatte & Patrice Dumas, 2015. "The role of oscillatory modes in US business cycles," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2015(1), pages 63-81.
    6. Svatopluk KAPOUNEK & Jitka POMĚNKOVÁ, 2013. "The endogeneity of optimum currency area criteria in the context of financial crisis: Evidence from the time-frequency domain analysis," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 59(9), pages 389-395.
    7. Hassani, Hossein & Rua, António & Silva, Emmanuel Sirimal & Thomakos, Dimitrios, 2019. "Monthly forecasting of GDP with mixed-frequency multivariate singular spectrum analysis," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1263-1272.
    8. António Rua & Miguel de Carvalho, 2014. "Real-time nowcasting the US output gap: Singular spectrum analysis at work," Working Papers w201416, Banco de Portugal, Economics and Research Department.
    9. Hua, Jia-Chen & Roy, Sukesh & McCauley, Joseph L. & Gunaratne, Gemunu H., 2016. "Using dynamic mode decomposition to extract cyclic behavior in the stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 448(C), pages 172-180.
    10. Bógalo, Juan & Poncela, Pilar & Senra, Eva, 2017. "Automatic Signal Extraction for Stationary and Non-Stationary Time Series by Circulant SSA," MPRA Paper 76023, University Library of Munich, Germany.
    11. Juan Bógalo & Pilar Poncela & Eva Senra, 2021. "Circulant Singular Spectrum Analysis to Monitor the State of the Economy in Real Time," Mathematics, MDPI, vol. 9(11), pages 1-17, May.
    12. Hicham M. Hachem, 2017. "How Moderate was the Great Moderation and how Destabilizing is Secular Stagnation? Fiscal and monetary policy implications based on åvidence from US macro data," Economic Alternatives, University of National and World Economy, Sofia, Bulgaria, issue 2, pages 226-236, June.
    13. Papailias, Fotis & Thomakos, Dimitrios, 2017. "EXSSA: SSA-based reconstruction of time series via exponential smoothing of covariance eigenvalues," International Journal of Forecasting, Elsevier, vol. 33(1), pages 214-229.
    14. Coussin, Maximilien, 2022. "Singular spectrum analysis for real-time financial cycles measurement," Journal of International Money and Finance, Elsevier, vol. 120(C).
    15. Josu Arteche & Javier García‐Enríquez, 2022. "Singular spectrum analysis for value at risk in stochastic volatility models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 3-16, January.
    16. Roman Marsalek & Jitka Pomenkova & Svatopluk Kapounek, 2014. "A Wavelet-Based Approach to Filter Out Symmetric Macroeconomic Shocks," Computational Economics, Springer;Society for Computational Economics, vol. 44(4), pages 477-488, December.
    17. Lisa Sella & Gianna Vivaldo & Andreas Groth & Michael Ghil, 2016. "Economic Cycles and Their Synchronization: A Comparison of Cyclic Modes in Three European Countries," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(1), pages 25-48, September.

  5. Miguel de Carvalho & Paulo Júlio, 2010. "Digging Out the PPP Hypothesis: an Integrated Empirical Coverage," GEE Papers 0024, Gabinete de Estratégia e Estudos, Ministério da Economia, revised Sep 2010.

    Cited by:

    1. Aysegul Corakcı & Furkan Emirmahmutoglu & Omay Tolga, 2017. "PPP hypothesis and temporary structural breaks," Economics Bulletin, AccessEcon, vol. 37(3), pages 1541-1548.
    2. Raihan, Selim & Abdullah, S M & Barkat, Aroni & Siddiqua, Salina, 2017. "Mean Reversion of the Real Exchange Rate and the validity of PPP Hypothesis in the context of Bangladesh: A Holistic Approach," MPRA Paper 77172, University Library of Munich, Germany.
    3. Mücahit Aydın, 2019. "Investigation of the Validity of Purchasing Power Parity Hypothesis with Fourier Unit Root Tests: The Case of Turkey," EKOIST Journal of Econometrics and Statistics, Istanbul University, Faculty of Economics, vol. 30(0), pages 35-48, June.
    4. Xie, Zixiong & Chen, Shyh-Wei & Hsieh, Chun-Kuei, 2021. "Facing up to the polysemy of purchasing power parity: New international evidence," Economic Modelling, Elsevier, vol. 98(C), pages 247-265.
    5. M. de Carvalho & K. F. Turkman & A. Rua, 2013. "Dynamic threshold modelling and the US business cycle," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(4), pages 535-550, August.
    6. Fumitaka Furuoka, 2015. "Electricity consumption and economic development in Asia: new data and new methods," Asian-Pacific Economic Literature, The Crawford School, The Australian National University, vol. 29(1), pages 102-125, May.
    7. Asuamah Yeboah, Samuel, 2017. "Is purchasing power parity hypothesis valid in Ghana? An empirical assessment," MPRA Paper 99394, University Library of Munich, Germany.

Articles

  1. Sitsofe Tsagbey & Miguel de Carvalho & Garritt L. Page, 2017. "All Data are Wrong, but Some are Useful? Advocating the Need for Data Auditing," The American Statistician, Taylor & Francis Journals, vol. 71(3), pages 231-235, July.

    Cited by:

    1. Roy Cerqueti & Claudio Lupi, 2023. "Severe testing of Benford’s law," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(2), pages 677-694, June.
    2. Junho Lee & Miguel de Carvalho, 2019. "Technological improvements or climate change? Bayesian modeling of time-varying conformance to Benford’s Law," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-11, April.
    3. Sylwestrzak Marek, 2023. "Applying Benford’s law to detect earnings management," Journal of Economics and Management, Sciendo, vol. 45(1), pages 216-236, January.

  2. Hanson, Timothy E. & de Carvalho, Miguel & Chen, Yuhui, 2017. "Bernstein polynomial angular densities of multivariate extreme value distributions," Statistics & Probability Letters, Elsevier, vol. 128(C), pages 60-66.

    Cited by:

    1. Miguel de Carvalho & Manuele Leonelli & Alex Rossi, 2020. "Tracking change-points in multivariate extremes," Papers 2011.05067, arXiv.org.
    2. Hu, Shuang & Peng, Zuoxiang & Segers, Johan, 2022. "Modelling multivariate extreme value distributions via Markov trees," LIDAM Discussion Papers ISBA 2022021, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

  3. de Carvalho, Miguel & Rua, António, 2017. "Real-time nowcasting the US output gap: Singular spectrum analysis at work," International Journal of Forecasting, Elsevier, vol. 33(1), pages 185-198.
    See citations under working paper version above.
  4. Miguel de Carvalho, 2016. "Mean, What do You Mean?," The American Statistician, Taylor & Francis Journals, vol. 70(3), pages 270-274, July.

    Cited by:

    1. Guerrero, Victor M. & Solis-Lemus, Claudia, 2020. "A generalized measure of dispersion," Statistics & Probability Letters, Elsevier, vol. 164(C).
    2. Curto, José Dias & Serrasqueiro, Pedro, 2022. "Averaging financial ratios," Finance Research Letters, Elsevier, vol. 48(C).
    3. Feehan, Dennis & Wrigley-Field, Elizabeth, 2020. "How do populations aggregate?," SocArXiv 2fkw3, Center for Open Science.
    4. Mátyás Barczy & Zsolt Páles, 2023. "Limit Theorems for Deviation Means of Independent and Identically Distributed Random Variables," Journal of Theoretical Probability, Springer, vol. 36(3), pages 1626-1666, September.
    5. Adam Gorajek, 2022. "Quasilinear‐mean regression," Journal of Economic Surveys, Wiley Blackwell, vol. 36(5), pages 1288-1310, December.
    6. Clive Hunt & Ross Taplin, 2019. "Aggregation of Incidence and Intensity Risk Variables to Achieve Reconciliation," Risks, MDPI, vol. 7(4), pages 1-14, October.

  5. Miguel Carvalho & António Rua, 2014. "Extremal Dependence in International Output Growth: Tales from the Tails," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(4), pages 605-620, August.
    See citations under working paper version above.
  6. Miguel de Carvalho & Anthony C. Davison, 2014. "Spectral Density Ratio Models for Multivariate Extremes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(506), pages 764-776, June.

    Cited by:

    1. Mao, Shanjun & Fan, Xiaodan & Hu, Jie, 2021. "Correlation for tree-shaped datasets and its Bayesian estimation," Computational Statistics & Data Analysis, Elsevier, vol. 164(C).
    2. Hanson, Timothy E. & de Carvalho, Miguel & Chen, Yuhui, 2017. "Bernstein polynomial angular densities of multivariate extreme value distributions," Statistics & Probability Letters, Elsevier, vol. 128(C), pages 60-66.
    3. Zhang, Archer Gong & Chen, Jiahua, 2022. "Density ratio model with data-adaptive basis function," Journal of Multivariate Analysis, Elsevier, vol. 191(C).
    4. Raphaël Huser & Marc G. Genton, 2016. "Non-Stationary Dependence Structures for Spatial Extremes," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(3), pages 470-491, September.
    5. Mhalla, Linda & Chavez-Demoulin, Valérie & Naveau, Philippe, 2017. "Non-linear models for extremal dependence," Journal of Multivariate Analysis, Elsevier, vol. 159(C), pages 49-66.
    6. Pengfei Li & Yukun Liu & Jing Qin, 2017. "Semiparametric Inference in a Genetic Mixture Model," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 1250-1260, July.
    7. Daniela Castro Camilo & Miguel de Carvalho & Jennifer Wadsworth, 2017. "Time-Varying Extreme Value Dependence with Application to Leading European Stock Markets," Papers 1709.01198, arXiv.org.
    8. Wang, Chunlin & Marriott, Paul & Li, Pengfei, 2017. "Testing homogeneity for multiple nonnegative distributions with excess zero observations," Computational Statistics & Data Analysis, Elsevier, vol. 114(C), pages 146-157.

  7. M. de Carvalho & K. F. Turkman & A. Rua, 2013. "Dynamic threshold modelling and the US business cycle," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(4), pages 535-550, August.

    Cited by:

    1. António Rua & Miguel de Carvalho, 2010. "Extremal Dependence in International Output Growth: Tales from the Tails," Working Papers w201008, Banco de Portugal, Economics and Research Department.

  8. Miguel Carvalho & Paulo Júlio, 2012. "Digging out the PPP hypothesis: an integrated empirical coverage," Empirical Economics, Springer, vol. 42(3), pages 713-744, June.
    See citations under working paper version above.
  9. de Carvalho, Miguel & Rodrigues, Paulo C. & Rua, António, 2012. "Tracking the US business cycle with a singular spectrum analysis," Economics Letters, Elsevier, vol. 114(1), pages 32-35.
    See citations under working paper version above.

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 6 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-CBA: Central Banking (3) 2010-07-31 2010-07-31 2010-11-06
  2. NEP-ECM: Econometrics (3) 2010-07-31 2010-07-31 2010-07-31
  3. NEP-ETS: Econometric Time Series (2) 2010-07-31 2017-09-10
  4. NEP-BEC: Business Economics (1) 2010-07-31
  5. NEP-FDG: Financial Development and Growth (1) 2010-07-31
  6. NEP-MAC: Macroeconomics (1) 2014-11-22
  7. NEP-OPM: Open Economy Macroeconomics (1) 2010-11-06

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