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Leonardo Rocha Souza

Personal Details

First Name:Leonardo
Middle Name:Rocha
Last Name:Souza
Suffix:
RePEc Short-ID:pso147
[This author has chosen not to make the email address public]

Affiliation

Department of Economic and Social Affairs
United Nations

New York City, New York (United States)
http://www.un.org/esa/
RePEc:edi:desunus (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. MArcelo Carvalho & MArco Aurelio Freire & Marcelo Cunha Medeiros & Leonardo Souza, 2006. "Modeling and forecasting the volatility of Brazilian asset returns," Textos para discussão 530, Department of Economics PUC-Rio (Brazil).
  2. Souza, Leonardo Rocha, 2003. "A note on Chambers's 'long memory and aggregation in macroeconomic time series'," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 503, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
  3. Souza, Leonardo Rocha, 2003. "The aliasing effect, the Fejer Kernel and temporally aggregated long memory processes," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 470, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
  4. Souza, Leonardo Rocha & Soares, Lacir Jorge, 2003. "Forecasting electricity load demand: analysis of the 2001 rationing period in Brazil," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 491, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
  5. Souza, Leonardo Rocha & Smith, Jeremy & Souza, Reinaldo Castro, 2003. "Convex combinations of long memory estimates from different sampling rates," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 489, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
  6. Soares, Lacir Jorge & Souza, Leonardo Rocha, 2003. "Forecasting electricity demand using generalized long memory," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 486, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
  7. Leonardo Souza & Gustavo Raposo, 2003. "Valuing Interest Rates Derivatives," Computing in Economics and Finance 2003 179, Society for Computational Economics.
  8. Souza, Leonardo Rocha, 2003. "Temporal aggregation and bandwidth selection in estimating long memory," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 478, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
  9. Veiga, Alvaro & Souza, Leonardo Rocha, 2003. "Using irregularly spaced returns to estimate multi-factor models: application to Brazilian equity data," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 487, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
  10. Leonardo Souza & Alvaro Veiga & Marcelo C. Medeiros, 2002. "Evaluating the performance of GARCH models using White´s Reality Check," Textos para discussão 453, Department of Economics PUC-Rio (Brazil).
  11. Alvaro Veiga & Leonardo Souza, 2002. "A Multi-Factor Model with Irregular Returns for missing values imputation in emergent markets: Application to Brazilian Equity Data," Computing in Economics and Finance 2002 280, Society for Computational Economics.

Articles

  1. Leonardo Rocha Souza, 2008. "Why Aggregate Long Memory Time Series?," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 298-316.
  2. Rocha Souza, Leonardo & Jorge Soares, Lacir, 2007. "Electricity rationing and public response," Energy Economics, Elsevier, vol. 29(2), pages 296-311, March.
  3. Leonardo Rocha Souza, 2007. "Temporal Aggregation and Bandwidth selection in estimating long memory," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(5), pages 701-722, September.
  4. Soares, Lacir Jorge & Souza, Leonardo Rocha, 2006. "Forecasting electricity demand using generalized long memory," International Journal of Forecasting, Elsevier, vol. 22(1), pages 17-28.
  5. Alvaro Veiga & Leonardo Souza, 2006. "Using Irregularly Spaced Returns to Estimate Multi-factor Models: Application to Brazilian Equity Data," The European Journal of Finance, Taylor & Francis Journals, vol. 12(6-7), pages 605-626.
  6. Marcelo C. Carvalho & Marco Aurélio S. Freire & Marcelo Cunha Medeiros & Leonardo R. Souza, 2006. "Modeling and Forecasting the Volatility of Brazilian Asset Returns: a Realized Variance Approach," Brazilian Review of Finance, Brazilian Society of Finance, vol. 4(1), pages 55-77.
  7. Leonardo Souza & Jeremy Smith & Reinaldo Souza, 2006. "Convex combinations of long memory estimates from different sampling rates," Computational Statistics, Springer, vol. 21(3), pages 399-413, December.
  8. Leonardo Rocha Souza, 2005. "A Note On Chambers'S "Long Memory And Aggregation In Macroeconomic Time Series"," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 46(3), pages 1059-1062, August.
  9. Souza, Leonardo & Veiga, Alvaro & Medeiros, Marcelo C., 2005. "Evaluating the Forecasting Performance of GARCH Models Using White’s Reality Check," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 25(1), May.
  10. Souza, Leonardo R. & Smith, Jeremy, 2004. "Effects of temporal aggregation on estimates and forecasts of fractionally integrated processes: a Monte-Carlo study," International Journal of Forecasting, Elsevier, vol. 20(3), pages 487-502.
  11. Souza, Leonardo R. & Smith, Jeremy, 2002. "Bias in the memory parameter for different sampling rates," International Journal of Forecasting, Elsevier, vol. 18(2), pages 299-313.

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. MArcelo Carvalho & MArco Aurelio Freire & Marcelo Cunha Medeiros & Leonardo Souza, 2006. "Modeling and forecasting the volatility of Brazilian asset returns," Textos para discussão 530, Department of Economics PUC-Rio (Brazil).

    Cited by:

    1. Márcio Gomes Pinto Garcia & Marcelo Cunha Medeiros & Francisco Eduardo de Luna e Almeida Santos, 2014. "Economic gains of realized volatility in the Brazilian stock market," Brazilian Review of Finance, Brazilian Society of Finance, vol. 12(3), pages 319-349.
    2. Naseem Al Rahahleh & Robert Kao, 2018. "Forecasting Volatility: Evidence from the Saudi Stock Market," JRFM, MDPI, vol. 11(4), pages 1-18, November.
    3. Michael McAleer & Marcelo Medeiros, 2008. "Realized Volatility: A Review," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 10-45.
    4. Wink Junior, Marcos Vinício & Pereira, Pedro Luiz Valls, 2011. "Modeling and Forecasting of Realized Volatility: Evidence from Brazil," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 31(2), December.
    5. Goodness C. Aye & Mehmet Balcilar & Rangan Gupta & Nicholas Kilimani & Amandine Nakumuryango & Siobhan Redford, 2012. "Predicting BRICS Stock Returns Using ARFIMA Models," Working Papers 201235, University of Pretoria, Department of Economics.
    6. Leandro Maciel, 2012. "A Hybrid Fuzzy GJR-GARCH Modeling Approach for Stock Market Volatility Forecasting," Brazilian Review of Finance, Brazilian Society of Finance, vol. 10(3), pages 337-367.

  2. Souza, Leonardo Rocha, 2003. "A note on Chambers's 'long memory and aggregation in macroeconomic time series'," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 503, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).

    Cited by:

    1. Khan, Ali & Mitra, Tapan, 2003. "On choice of technique in the Robinson-Solow-Srinivasan model," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 504, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    2. Hassler Uwe & Tsai Henghsiu, 2013. "Asymptotic Behavior of Temporal Aggregates in the Frequency Domain," Journal of Time Series Econometrics, De Gruyter, vol. 5(1), pages 47-60, January.
    3. Davidson James & Rambaccussing Dooruj, 2015. "A Test of the Long Memory Hypothesis Based on Self-Similarity," Journal of Time Series Econometrics, De Gruyter, vol. 7(2), pages 115-141, July.
    4. Guglielmo Maria Caporale & Luis A. Gil-Alana, 2011. "Long Memory and Fractional Integration in High-Frequency British Pound / Dollar Spot Exchange Rates," Faculty Working Papers 02/11, School of Economics and Business Administration, University of Navarra.
    5. Guglielmo Maria Caporale & Luis A. Gil-Alana, 2013. "Long Memory and Fractional Integration in High Frequency Data on the US Dollar / British Pound Spot Exchange Rate," Discussion Papers of DIW Berlin 1294, DIW Berlin, German Institute for Economic Research.
    6. Cavalcanti Ferreira, Pedro & Facchini, Giovanni, 2005. "Trade liberalization and industrial concentration: Evidence from Brazil," The Quarterly Review of Economics and Finance, Elsevier, vol. 45(2-3), pages 432-446, May.
    7. Guglielmo Maria Caporale & Luis A. Gil-Alana, 2009. "Long Memory in US Real Output per Capita," Discussion Papers of DIW Berlin 891, DIW Berlin, German Institute for Economic Research.
    8. Pierre Perron & Wendong Shi, 2014. "Temporal Aggregation, Bandwidth Selection and Long Memory for Volatility Models," Boston University - Department of Economics - Working Papers Series wp2014-009, Boston University - Department of Economics.
    9. Raquel Ayestarán & Juan Infante & Juan José Tenorio & Luis Alberiko Gil-Alana, 2023. "Evidence of Inflation Using Harmonized Consumer Price Indices in Some Euro Countries: France, Germany, Italy, and Spain, along with the Euro Zone," Mathematics, MDPI, vol. 11(10), pages 1-12, May.
    10. Mark J. Jensen, 2009. "The Long-Run Fisher Effect: Can It Be Tested?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(1), pages 221-231, February.
    11. Gil-Alana, Luis Alberiko & Poza, Carlos, 2024. "Volatility persistence in metal prices," Resources Policy, Elsevier, vol. 88(C).
    12. Monteiro, Paulo Klinger, 2006. "The set of equilibria of first-price auctions," Journal of Mathematical Economics, Elsevier, vol. 42(3), pages 364-372, June.
    13. Caporale, Guglielmo Maria & Gil-Alana, Luis A. & You, Kefei, 2018. "Exchange rate linkages between the ASEAN currencies, the US dollar and the Chinese RMB," Research in International Business and Finance, Elsevier, vol. 44(C), pages 227-238.
    14. Pierre Perron & Wendong Shi, 2020. "Temporal Aggregation and Long Memory for Asset Price Volatility," JRFM, MDPI, vol. 13(8), pages 1-18, August.
    15. Sun, Jingwei & Shi, Wendong, 2014. "Aggregation of the generalized fractional processes," Economics Letters, Elsevier, vol. 124(2), pages 258-262.
    16. Hassler, Uwe, 2014. "Persistence under temporal aggregation and differencing," Economics Letters, Elsevier, vol. 124(2), pages 318-322.
    17. Shi, Wendong & Sun, Jingwei, 2016. "Aggregation and long-memory: An analysis based on the discrete Fourier transform," Economic Modelling, Elsevier, vol. 53(C), pages 470-476.

  3. Souza, Leonardo Rocha, 2003. "The aliasing effect, the Fejer Kernel and temporally aggregated long memory processes," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 470, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).

    Cited by:

    1. Khan, Ali & Mitra, Tapan, 2003. "On choice of technique in the Robinson-Solow-Srinivasan model," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 504, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    2. Leonardo Rocha Souza, 2007. "Temporal Aggregation and Bandwidth selection in estimating long memory," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(5), pages 701-722, September.
    3. Souza, Leonardo Rocha & Smith, Jeremy & Souza, Reinaldo Castro, 2003. "Convex combinations of long memory estimates from different sampling rates," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 489, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    4. Per Frederiksen & Frank S. Nielsen, 2008. "Estimation of Dynamic Models with Nonparametric Simulated Maximum Likelihood," CREATES Research Papers 2008-59, Department of Economics and Business Economics, Aarhus University.

  4. Souza, Leonardo Rocha & Soares, Lacir Jorge, 2003. "Forecasting electricity load demand: analysis of the 2001 rationing period in Brazil," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 491, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).

    Cited by:

    1. Taylor, James W. & de Menezes, Lilian M. & McSharry, Patrick E., 2006. "A comparison of univariate methods for forecasting electricity demand up to a day ahead," International Journal of Forecasting, Elsevier, vol. 22(1), pages 1-16.

  5. Souza, Leonardo Rocha & Smith, Jeremy & Souza, Reinaldo Castro, 2003. "Convex combinations of long memory estimates from different sampling rates," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 489, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).

    Cited by:

    1. Cavalcanti Ferreira, Pedro & Facchini, Giovanni, 2005. "Trade liberalization and industrial concentration: Evidence from Brazil," The Quarterly Review of Economics and Finance, Elsevier, vol. 45(2-3), pages 432-446, May.
    2. Monteiro, Paulo Klinger, 2006. "The set of equilibria of first-price auctions," Journal of Mathematical Economics, Elsevier, vol. 42(3), pages 364-372, June.

  6. Soares, Lacir Jorge & Souza, Leonardo Rocha, 2003. "Forecasting electricity demand using generalized long memory," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 486, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).

    Cited by:

    1. Guglielmo Maria Caporale & Juncal Cunado & Luis A. Gil-Alana, 2008. "Modelling Long-Run Trends and Cycles in Financial Time Series Data," CESifo Working Paper Series 2330, CESifo.
    2. Artiach, Miguel & Arteche, Josu, 2012. "Doubly fractional models for dynamic heteroscedastic cycles," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 2139-2158.
    3. Carlo Fezzi & Derek Bunn, 2010. "Structural Analysis of Electricity Demand and Supply Interactions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(6), pages 827-856, December.
    4. Leschinski, Christian & Sibbertsen, Philipp, 2014. "Model Order Selection in Seasonal/Cyclical Long Memory Models," Hannover Economic Papers (HEP) dp-535, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    5. Rubin, Ofir D. & Babcock, Bruce A., 2011. "A novel approach for modeling deregulated electricity markets," Energy Policy, Elsevier, vol. 39(5), pages 2711-2721, May.
    6. Abdou Kâ Diongue & Dominique Guegan, 2008. "The k-factor Gegenbauer asymmetric Power GARCH approach for modelling electricity spot price dynamics," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00259225, HAL.
    7. Trotter, Ian Michael & Féres, José Gustavo & Bolkesjø, Torjus Folsland & de Hollanda, Lavínia Rocha, 2015. "Simulating Brazilian Electricity Demand Under Climate Change Scenarios," Working Papers in Applied Economics 208689, Universidade Federal de Vicosa, Departamento de Economia Rural.
    8. Niels Haldrup & Oskar Knapik & Tommaso Proietti, 2016. "A generalized exponential time series regression model for electricity prices," CREATES Research Papers 2016-08, Department of Economics and Business Economics, Aarhus University.
    9. Jaume Rosselló Nadal & Mohcine Bakhat, 2009. "A new approach to estimating tourism-induced electricity consumption," CRE Working Papers (Documents de treball del CRE) 2009/6, Centre de Recerca Econòmica (UIB ·"Sa Nostra").
    10. Shao, Zhen & Chao, Fu & Yang, Shan-Lin & Zhou, Kai-Le, 2017. "A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 123-136.
    11. Chan, Kam Fong & Gray, Philip & van Campen, Bart, 2008. "A new approach to characterizing and forecasting electricity price volatility," International Journal of Forecasting, Elsevier, vol. 24(4), pages 728-743.
    12. Rocha Souza, Leonardo & Jorge Soares, Lacir, 2007. "Electricity rationing and public response," Energy Economics, Elsevier, vol. 29(2), pages 296-311, March.
    13. Magnano, L. & Boland, J.W., 2007. "Generation of synthetic sequences of electricity demand: Application in South Australia," Energy, Elsevier, vol. 32(11), pages 2230-2243.
    14. Webel, Karsten, 2022. "A review of some recent developments in the modelling and seasonal adjustment of infra-monthly time series," Discussion Papers 31/2022, Deutsche Bundesbank.
    15. Hahn, Heiko & Meyer-Nieberg, Silja & Pickl, Stefan, 2009. "Electric load forecasting methods: Tools for decision making," European Journal of Operational Research, Elsevier, vol. 199(3), pages 902-907, December.
    16. Guglielmo Maria Caporale & Luis A. Gil-Alana, 2012. "Persistence and Cycles in the US Federal Funds Rate," CESifo Working Paper Series 4035, CESifo.
    17. V. Dordonnat & S.J. Koopman & M. Ooms & A. Dessertaine & J. Collet, 2008. "An Hourly Periodic State Space Model for Modelling French National Electricity Load," Tinbergen Institute Discussion Papers 08-008/4, Tinbergen Institute.
    18. Erdal Atukeren & Yngve Abrahamsen, 2012. "Der schweizerische Aussenhandel mit elektrischer Energie," KOF Analysen, KOF Swiss Economic Institute, ETH Zurich, vol. 6(4), pages 57-70, December.
    19. Vaz, Lucélia Viviane & Filho, Getulio Borges da Silveira, 2017. "Functional Autoregressive Models: An Application to Brazilian Hourly Electricity Load," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 37(2), November.
    20. Cancelo, José Ramón & Grafe, Rosmarie, 2007. "Forecasting from one day to one week ahead for the Spanish system operator," DES - Working Papers. Statistics and Econometrics. WS ws078418, Universidad Carlos III de Madrid. Departamento de Estadística.
    21. Alex Gonzaga & Michael Hauser, 2011. "A wavelet Whittle estimator of generalized long-memory stochastic volatility," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(1), pages 23-48, March.
    22. Sigauke, C. & Chikobvu, D., 2011. "Prediction of daily peak electricity demand in South Africa using volatility forecasting models," Energy Economics, Elsevier, vol. 33(5), pages 882-888, September.
    23. Niematallah Elamin & Mototsugu Fukushige, 2017. "The 2011 Japanese energy crisis: Effects on the magnitude and pattern of load demand," Discussion Papers in Economics and Business 17-19, Osaka University, Graduate School of Economics.
    24. Elamin, Niematallah & Fukushige, Mototsugu, 2018. "Modeling and forecasting hourly electricity demand by SARIMAX with interactions," Energy, Elsevier, vol. 165(PB), pages 257-268.
    25. Safiullah, Hameed, 2011. "Evaluation of Grid Level Impacts of Electric Vehicles," MPRA Paper 58517, University Library of Munich, Germany.
    26. Cancelo, José Ramón & Espasa, Antoni & Grafe, Rosmarie, 2008. "Forecasting the electricity load from one day to one week ahead for the Spanish system operator," International Journal of Forecasting, Elsevier, vol. 24(4), pages 588-602.
    27. Tommaso Proietti & Niels Haldrup & Oskar Knapik, 2017. "Spikes and memory in (Nord Pool) electricity price spot prices," CREATES Research Papers 2017-39, Department of Economics and Business Economics, Aarhus University.
    28. Bakhat, Mohcine & Rosselló, Jaume, 2011. "Estimation of tourism-induced electricity consumption: The case study of Balearics Islands, Spain," Energy Economics, Elsevier, vol. 33(3), pages 437-444, May.
    29. Moral-Carcedo, Julián & Pérez-García, Julián, 2019. "Time of day effects of temperature and daylight on short term electricity load," Energy, Elsevier, vol. 174(C), pages 169-183.
    30. Souza, Leonardo Rocha & Soares, Lacir Jorge, 2003. "Forecasting electricity load demand: analysis of the 2001 rationing period in Brazil," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 491, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    31. Soares, Lacir J. & Medeiros, Marcelo C., 2008. "Modeling and forecasting short-term electricity load: A comparison of methods with an application to Brazilian data," International Journal of Forecasting, Elsevier, vol. 24(4), pages 630-644.
    32. Wang, Chi-hsiang & Grozev, George & Seo, Seongwon, 2012. "Decomposition and statistical analysis for regional electricity demand forecasting," Energy, Elsevier, vol. 41(1), pages 313-325.
    33. McElroy, Tucker S. & Holan, Scott H., 2016. "Computation of the autocovariances for time series with multiple long-range persistencies," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 44-56.
    34. Safiullah, Hameed, 2011. "Evaluation of Grid Level Impacts of Electric Vehicles," MPRA Paper 59175, University Library of Munich, Germany.
    35. Kamal Chapagain & Somsak Kittipiyakul & Pisut Kulthanavit, 2020. "Short-Term Electricity Demand Forecasting: Impact Analysis of Temperature for Thailand," Energies, MDPI, vol. 13(10), pages 1-29, May.
    36. Cruz E. Borges & Yoseba K. Penya & Iván Fernández & Juan Prieto & Oscar Bretos, 2013. "Assessing Tolerance-Based Robust Short-Term Load Forecasting in Buildings," Energies, MDPI, vol. 6(4), pages 1-20, April.
    37. Souhir Ben Amor & Heni Boubaker & Lotfi Belkacem, 2022. "A Dual Generalized Long Memory Modelling for Forecasting Electricity Spot Price: Neural Network and Wavelet Estimate," Papers 2204.08289, arXiv.org.
    38. Moral-Carcedo, Julián & Pérez-García, Julián, 2017. "Integrating long-term economic scenarios into peak load forecasting: An application to Spain," Energy, Elsevier, vol. 140(P1), pages 682-695.
    39. Leschinski, Christian & Sibbertsen, Philipp, 2019. "Model order selection in periodic long memory models," Econometrics and Statistics, Elsevier, vol. 9(C), pages 78-94.
    40. Palacio, Sebastián M., 2020. "Predicting collusive patterns in a liberalized electricity market with mandatory auctions of forward contracts," Energy Policy, Elsevier, vol. 139(C).
    41. Souhir Ben Amor & Heni Boubaker & Lotfi Belkacem, 2022. "Predictive Accuracy of a Hybrid Generalized Long Memory Model for Short Term Electricity Price Forecasting," Papers 2204.09568, arXiv.org.
    42. Amaral, Luiz Felipe & Souza, Reinaldo Castro & Stevenson, Maxwell, 2008. "A smooth transition periodic autoregressive (STPAR) model for short-term load forecasting," International Journal of Forecasting, Elsevier, vol. 24(4), pages 603-615.
    43. Stéphane Goutte & David Guerreiro & Bilel Sanhaji & Sophie Saglio & Julien Chevallier, 2019. "International Financial Markets," Post-Print halshs-02183053, HAL.

  7. Souza, Leonardo Rocha, 2003. "Temporal aggregation and bandwidth selection in estimating long memory," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 478, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).

    Cited by:

    1. Khan, Ali & Mitra, Tapan, 2003. "On choice of technique in the Robinson-Solow-Srinivasan model," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 504, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    2. Guglielmo Maria Caporale & Luis A. Gil-Alana, 2013. "Long Memory and Fractional Integration in High Frequency Data on the US Dollar / British Pound Spot Exchange Rate," Discussion Papers of DIW Berlin 1294, DIW Berlin, German Institute for Economic Research.
    3. Guglielmo Maria Caporale & Luis A. Gil-Alana, 2009. "Long Memory in US Real Output per Capita," Discussion Papers of DIW Berlin 891, DIW Berlin, German Institute for Economic Research.
    4. Kang, Sang Hoon & Cheong, Chongcheul & Yoon, Seong-Min, 2010. "Contemporaneous aggregation and long-memory property of returns and volatility in the Korean stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4844-4854.
    5. Uwe Hassler, 2011. "Estimation of fractional integration under temporal aggregation," Post-Print hal-00815563, HAL.
    6. Avishek Bhandari & Bandi Kamaiah, 2021. "Long Memory and Fractality Among Global Equity Markets: a Multivariate Wavelet Approach," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 23-37, March.
    7. Pierre Perron & Wendong Shi, 2014. "Temporal Aggregation, Bandwidth Selection and Long Memory for Volatility Models," Boston University - Department of Economics - Working Papers Series wp2014-009, Boston University - Department of Economics.
    8. Kuswanto, Heri, 2009. "A New Simple Test Against Spurious Long Memory Using Temporal Aggregation," Hannover Economic Papers (HEP) dp-425, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    9. Gil-Alana, Luis Alberiko & Poza, Carlos, 2024. "Volatility persistence in metal prices," Resources Policy, Elsevier, vol. 88(C).
    10. Ye, Xunyu & Gao, Ping & Li, Handong, 2015. "Improving estimation of the fractionally differencing parameter in the SARFIMA model using tapered periodogram," Economic Modelling, Elsevier, vol. 46(C), pages 167-179.
    11. Souza, Leonardo Rocha & Smith, Jeremy & Souza, Reinaldo Castro, 2003. "Convex combinations of long memory estimates from different sampling rates," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 489, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    12. Pierre Perron & Wendong Shi, 2020. "Temporal Aggregation and Long Memory for Asset Price Volatility," JRFM, MDPI, vol. 13(8), pages 1-18, August.
    13. Sun, Jingwei & Shi, Wendong, 2014. "Aggregation of the generalized fractional processes," Economics Letters, Elsevier, vol. 124(2), pages 258-262.
    14. Per Frederiksen & Frank S. Nielsen, 2008. "Estimation of Dynamic Models with Nonparametric Simulated Maximum Likelihood," CREATES Research Papers 2008-59, Department of Economics and Business Economics, Aarhus University.
    15. Giorgio Canarella & Stephen M. Miller, 2016. "Inflation Targeting: New Evidence from Fractional Integration and Cointegration," Working papers 2016-08, University of Connecticut, Department of Economics.
    16. Bhandari, Avishek, 2020. "Long memory and fractality among global equity markets: A multivariate wavelet approach," MPRA Paper 99653, University Library of Munich, Germany.
    17. Uwe Hassler, 2013. "Effect of temporal aggregation on multiple time series in the frequency domain," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(5), pages 562-573, September.

  8. Leonardo Souza & Alvaro Veiga & Marcelo C. Medeiros, 2002. "Evaluating the performance of GARCH models using White´s Reality Check," Textos para discussão 453, Department of Economics PUC-Rio (Brazil).

    Cited by:

    1. Cifter, Atilla, 2012. "Volatility Forecasting with Asymmetric Normal Mixture Garch Model: Evidence from South Africa," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 127-142, June.
    2. Marcelo de Paiva Abreu, 2003. "The political economy of economic integration in the Americas: Latin American interests," Textos para discussão 468, Department of Economics PUC-Rio (Brazil).

Articles

  1. Leonardo Rocha Souza, 2008. "Why Aggregate Long Memory Time Series?," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 298-316.

    Cited by:

    1. Kunal Saha & Vinodh Madhavan & Chandrashekhar G. R. & David McMillan, 2020. "Pitfalls in long memory research," Cogent Economics & Finance, Taylor & Francis Journals, vol. 8(1), pages 1733280-173, January.
    2. Guglielmo Maria Caporale & Silvia García Tapia & Luis Alberiko Gil-Alana, 2023. "Persistence in Tax Revenues: Evidence from Some OECD Countries," CESifo Working Paper Series 10682, CESifo.
    3. Arturo Leccadito & Omar Rachedi & Giovanni Urga, 2015. "True Versus Spurious Long Memory: Some Theoretical Results and a Monte Carlo Comparison," Econometric Reviews, Taylor & Francis Journals, vol. 34(4), pages 452-479, April.
    4. Guglielmo Maria Caporale & Luis A. Gil-Alana, 2013. "Long Memory and Fractional Integration in High Frequency Data on the US Dollar / British Pound Spot Exchange Rate," Discussion Papers of DIW Berlin 1294, DIW Berlin, German Institute for Economic Research.
    5. Uwe Hassler, 2011. "Estimation of fractional integration under temporal aggregation," Post-Print hal-00815563, HAL.
    6. Pierre Perron & Wendong Shi, 2014. "Temporal Aggregation, Bandwidth Selection and Long Memory for Volatility Models," Boston University - Department of Economics - Working Papers Series wp2014-009, Boston University - Department of Economics.
    7. Pierre Perron & Wendong Shi, 2020. "Temporal Aggregation and Long Memory for Asset Price Volatility," JRFM, MDPI, vol. 13(8), pages 1-18, August.
    8. Sun, Jingwei & Shi, Wendong, 2014. "Aggregation of the generalized fractional processes," Economics Letters, Elsevier, vol. 124(2), pages 258-262.
    9. Shi, Wendong & Sun, Jingwei, 2016. "Aggregation and long-memory: An analysis based on the discrete Fourier transform," Economic Modelling, Elsevier, vol. 53(C), pages 470-476.

  2. Rocha Souza, Leonardo & Jorge Soares, Lacir, 2007. "Electricity rationing and public response," Energy Economics, Elsevier, vol. 29(2), pages 296-311, March.

    Cited by:

    1. Arthur Charpentier, 2011. "On the return period of the 2003 heat wave," Climatic Change, Springer, vol. 109(3), pages 245-260, December.
    2. Hunt., Julian David & Stilpen, Daniel & de Freitas, Marcos Aurélio Vasconcelos, 2018. "A review of the causes, impacts and solutions for electricity supply crises in Brazil," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 208-222.
    3. Oskar Lindgren & Erik Elwing & Mikael Karlsson & Sverker C. Jagers, 2024. "Public acceptability of climate-motivated rationing," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-9, December.

  3. Leonardo Rocha Souza, 2007. "Temporal Aggregation and Bandwidth selection in estimating long memory," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(5), pages 701-722, September.
    See citations under working paper version above.
  4. Soares, Lacir Jorge & Souza, Leonardo Rocha, 2006. "Forecasting electricity demand using generalized long memory," International Journal of Forecasting, Elsevier, vol. 22(1), pages 17-28.
    See citations under working paper version above.
  5. Marcelo C. Carvalho & Marco Aurélio S. Freire & Marcelo Cunha Medeiros & Leonardo R. Souza, 2006. "Modeling and Forecasting the Volatility of Brazilian Asset Returns: a Realized Variance Approach," Brazilian Review of Finance, Brazilian Society of Finance, vol. 4(1), pages 55-77.

    Cited by:

    1. Márcio Gomes Pinto Garcia & Marcelo Cunha Medeiros & Francisco Eduardo de Luna e Almeida Santos, 2014. "Economic gains of realized volatility in the Brazilian stock market," Brazilian Review of Finance, Brazilian Society of Finance, vol. 12(3), pages 319-349.
    2. Naseem Al Rahahleh & Robert Kao, 2018. "Forecasting Volatility: Evidence from the Saudi Stock Market," JRFM, MDPI, vol. 11(4), pages 1-18, November.
    3. Michael McAleer & Marcelo Medeiros, 2008. "Realized Volatility: A Review," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 10-45.
    4. Wink Junior, Marcos Vinício & Pereira, Pedro Luiz Valls, 2011. "Modeling and Forecasting of Realized Volatility: Evidence from Brazil," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 31(2), December.
    5. Goodness C. Aye & Mehmet Balcilar & Rangan Gupta & Nicholas Kilimani & Amandine Nakumuryango & Siobhan Redford, 2012. "Predicting BRICS Stock Returns Using ARFIMA Models," Working Papers 201235, University of Pretoria, Department of Economics.

  6. Leonardo Souza & Jeremy Smith & Reinaldo Souza, 2006. "Convex combinations of long memory estimates from different sampling rates," Computational Statistics, Springer, vol. 21(3), pages 399-413, December.
    See citations under working paper version above.
  7. Leonardo Rocha Souza, 2005. "A Note On Chambers'S "Long Memory And Aggregation In Macroeconomic Time Series"," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 46(3), pages 1059-1062, August.
    See citations under working paper version above.
  8. Souza, Leonardo & Veiga, Alvaro & Medeiros, Marcelo C., 2005. "Evaluating the Forecasting Performance of GARCH Models Using White’s Reality Check," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 25(1), May.

    Cited by:

    1. Cifter, Atilla, 2012. "Volatility Forecasting with Asymmetric Normal Mixture Garch Model: Evidence from South Africa," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 127-142, June.

  9. Souza, Leonardo R. & Smith, Jeremy, 2004. "Effects of temporal aggregation on estimates and forecasts of fractionally integrated processes: a Monte-Carlo study," International Journal of Forecasting, Elsevier, vol. 20(3), pages 487-502.

    Cited by:

    1. Leonardo Rocha Souza, 2005. "A Note On Chambers'S "Long Memory And Aggregation In Macroeconomic Time Series"," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 46(3), pages 1059-1062, August.
    2. Man Kasing, 2010. "Extended Fractional Gaussian Noise and Simple ARFIMA Approximations," Journal of Time Series Econometrics, De Gruyter, vol. 2(1), pages 1-26, September.
    3. Davidson James & Rambaccussing Dooruj, 2015. "A Test of the Long Memory Hypothesis Based on Self-Similarity," Journal of Time Series Econometrics, De Gruyter, vol. 7(2), pages 115-141, July.
    4. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
    5. Leonardo Rocha Souza, 2007. "Temporal Aggregation and Bandwidth selection in estimating long memory," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(5), pages 701-722, September.
    6. Chan, Wai-Sum & Chan, Yin-Ting, 2008. "A note on the autocorrelation properties of temporally aggregated Markov switching Gaussian models," Statistics & Probability Letters, Elsevier, vol. 78(6), pages 728-735, April.
    7. George Athanasopoulos & Rob J Hyndman & Nikolaos Kourentzes & Fotios Petropoulos, 2015. "Forecasting with Temporal Hierarchies," Monash Econometrics and Business Statistics Working Papers 16/15, Monash University, Department of Econometrics and Business Statistics.
    8. Chan, Wai-Sum, 2022. "On temporal aggregation of some nonlinear time-series models," Econometrics and Statistics, Elsevier, vol. 21(C), pages 38-49.
    9. Wai‐Sum Chan & Li‐Xin Zhang & Siu Hung Cheung, 2009. "Temporal aggregation of Markov‐switching financial return models," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 25(3), pages 359-383, May.
    10. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    11. Babai, M. Zied & Ali, Mohammad M. & Nikolopoulos, Konstantinos, 2012. "Impact of temporal aggregation on stock control performance of intermittent demand estimators: Empirical analysis," Omega, Elsevier, vol. 40(6), pages 713-721.
    12. Henghsiu Tsai & K. S. Chan, 2005. "Temporal Aggregation of Stationary And Nonstationary Discrete‐Time Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(4), pages 613-624, July.
    13. Jože Martin Rožanec & Blaž Fortuna & Dunja Mladenić, 2022. "Reframing Demand Forecasting: A Two-Fold Approach for Lumpy and Intermittent Demand," Sustainability, MDPI, vol. 14(15), pages 1-21, July.
    14. Bahman Rostami‐Tabar & M. Zied Babai & Aris Syntetos & Yves Ducq, 2013. "Demand forecasting by temporal aggregation," Naval Research Logistics (NRL), John Wiley & Sons, vol. 60(6), pages 479-498, September.
    15. Jan G. De Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Monash Econometrics and Business Statistics Working Papers 12/05, Monash University, Department of Econometrics and Business Statistics.
    16. Man, K.S. & Tiao, G.C., 2006. "Aggregation effect and forecasting temporal aggregates of long memory processes," International Journal of Forecasting, Elsevier, vol. 22(2), pages 267-281.
    17. Souza, Leonardo Rocha & Smith, Jeremy & Souza, Reinaldo Castro, 2003. "Convex combinations of long memory estimates from different sampling rates," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 489, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).

  10. Souza, Leonardo R. & Smith, Jeremy, 2002. "Bias in the memory parameter for different sampling rates," International Journal of Forecasting, Elsevier, vol. 18(2), pages 299-313.

    Cited by:

    1. Leonardo Rocha Souza, 2005. "A Note On Chambers'S "Long Memory And Aggregation In Macroeconomic Time Series"," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 46(3), pages 1059-1062, August.
    2. R Jea & C-T Su & J-L Lin, 2005. "Time aggregation effect on the correlation coefficient: added-systematically sampled framework," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(11), pages 1303-1309, November.
    3. Kunal Saha & Vinodh Madhavan & Chandrashekhar G. R. & David McMillan, 2020. "Pitfalls in long memory research," Cogent Economics & Finance, Taylor & Francis Journals, vol. 8(1), pages 1733280-173, January.
    4. Davidson James & Rambaccussing Dooruj, 2015. "A Test of the Long Memory Hypothesis Based on Self-Similarity," Journal of Time Series Econometrics, De Gruyter, vol. 7(2), pages 115-141, July.
    5. Guglielmo Maria Caporale & Luis A. Gil-Alana, 2011. "Long Memory and Fractional Integration in High-Frequency British Pound / Dollar Spot Exchange Rates," Faculty Working Papers 02/11, School of Economics and Business Administration, University of Navarra.
    6. Leonardo Rocha Souza, 2007. "Temporal Aggregation and Bandwidth selection in estimating long memory," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(5), pages 701-722, September.
    7. Guglielmo Maria Caporale & Luis A. Gil-Alana, 2013. "Long Memory and Fractional Integration in High Frequency Data on the US Dollar / British Pound Spot Exchange Rate," Discussion Papers of DIW Berlin 1294, DIW Berlin, German Institute for Economic Research.
    8. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    9. Uwe Hassler, 2011. "Estimation of fractional integration under temporal aggregation," Post-Print hal-00815563, HAL.
    10. Jea, Rong & Lin, Jin-Lung & Su, Chao-Ton, 2005. "Correlation and the time interval in multiple regression models," European Journal of Operational Research, Elsevier, vol. 162(2), pages 433-441, April.
    11. Souza, Leonardo R. & Smith, Jeremy, 2004. "Effects of temporal aggregation on estimates and forecasts of fractionally integrated processes: a Monte-Carlo study," International Journal of Forecasting, Elsevier, vol. 20(3), pages 487-502.
    12. Jan G. De Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Monash Econometrics and Business Statistics Working Papers 12/05, Monash University, Department of Econometrics and Business Statistics.
    13. Souza, Leonardo Rocha & Smith, Jeremy & Souza, Reinaldo Castro, 2003. "Convex combinations of long memory estimates from different sampling rates," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 489, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    14. Steven Clark & T. Coggin, 2011. "Are U.S. stock prices mean reverting? Some new tests using fractional integration models with overlapping data and structural breaks," Empirical Economics, Springer, vol. 40(2), pages 373-391, April.

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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 8 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-ETS: Econometric Time Series (6) 2002-04-25 2004-06-02 2004-06-02 2004-06-02 2004-06-02 2006-12-01. Author is listed
  2. NEP-ECM: Econometrics (4) 2002-04-25 2004-06-09 2004-06-09 2004-06-09
  3. NEP-FIN: Finance (1) 2004-06-02
  4. NEP-FOR: Forecasting (1) 2006-12-01
  5. NEP-LAB: Labour Economics (1) 2002-04-25
  6. NEP-MST: Market Microstructure (1) 2006-12-01
  7. NEP-RMG: Risk Management (1) 2006-12-01

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