Maximiano Pinheiro

Personal Details

First Name:	Maximiano
Middle Name:
Last Name:	Pinheiro
Suffix:
RePEc Short-ID:	ppi237
	[This author has chosen not to make the email address public]

Affiliation

Banco de Portugal

Lisboa, Portugal
http://www.bportugal.pt/
RePEc:edi:bdpgvpt (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.

Working papers

1. Maximiano Pinheiro, 2012. "Market perception of fiscal sustainability: An application to the largest euro area economies," Working Papers w201209, Banco de Portugal, Economics and Research Department.

   Cited by:

   1. Jordi Paniagua & Juan Sapena & Cecilio Tamarit, 2016. "Fiscal Sustainability in EMU contries: A continued Fiscal commitment?," Working Papers 1608, Department of Applied Economics II, Universidad de Valencia.
      • Paniagua, Jordi & Sapena, Juan & Tamarit, Cecilio, 2017. "Fiscal sustainability in EMU countries: A continued fiscal commitment?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 50(C), pages 85-97.

2. António Rua & Maximiano Pinheiro, 2009. "Dynamic factor models with jagged edge panel data: Taking on board the dynamics of the idiosyncratic components," Working Papers w200913, Banco de Portugal, Economics and Research Department.
   • Maximiano Pinheiro & António Rua & Francisco Dias, 2013. "Dynamic Factor Models with Jagged Edge Panel Data: Taking on Board the Dynamics of the Idiosyncratic Components," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(1), pages 80-102, February.

   Cited by:

   1. Poncela, Pilar, 2021. "Dynamic factor models: does the specification matter?," DES - Working Papers. Statistics and Econometrics. WS 32210, Universidad Carlos III de Madrid. Departamento de Estadística.
   2. Francisco Corona & Pilar Poncela & Esther Ruiz, 2017. "Determining the number of factors after stationary univariate transformations," Empirical Economics, Springer, vol. 53(1), pages 351-372, August.
      • Corona, Francisco & Poncela, Maria Pilar, 2016. "Determining the number of factors after stationary univariate transformations," DES - Working Papers. Statistics and Econometrics. WS ws1602, Universidad Carlos III de Madrid. Departamento de Estadística.
   3. Pilar Poncela & Esther Ruiz, 2016. "Small- Versus Big-Data Factor Extraction in Dynamic Factor Models: An Empirical Assessment," Advances in Econometrics, in: Dynamic Factor Models, volume 35, pages 401-434, Emerald Group Publishing Limited.
      • Poncela, Pilar, 2015. "Small versus big-data factor extraction in Dynamic Factor Models: An empirical assessment," DES - Working Papers. Statistics and Econometrics. WS ws1502, Universidad Carlos III de Madrid. Departamento de Estadística.
   4. Jin, Sainan & Miao, Ke & Su, Liangjun, 2021. "On factor models with random missing: EM estimation, inference, and cross validation," Journal of Econometrics, Elsevier, vol. 222(1), pages 745-777.
      • Su, Liangjun & Miao, Ke & Jin, Sainan, 2019. "On Factor Models with Random Missing: EM Estimation, Inference, and Cross Validation," Economics and Statistics Working Papers 4-2019, Singapore Management University, School of Economics.
   5. 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.
      • Poncela Blanco, Maria Pilar, 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.
   6. António Rua, 2016. "A wavelet-based multivariate multiscale approach for forecasting," Working Papers w201612, Banco de Portugal, Economics and Research Department.
      • Rua, António, 2017. "A wavelet-based multivariate multiscale approach for forecasting," International Journal of Forecasting, Elsevier, vol. 33(3), pages 581-590.
   7. Alvarez, Rocio & Camacho, Maximo & Perez-Quiros, Gabriel, 2016. "Aggregate versus disaggregate information in dynamic factor models," International Journal of Forecasting, Elsevier, vol. 32(3), pages 680-694.
   8. António Rua & Francisco Dias, 2020. "A non-hierarchical dynamic factor model for three-way data," Working Papers w202007, Banco de Portugal, Economics and Research Department.
   9. Dias, Francisco & Pinheiro, Maximiano & Rua, António, 2015. "Forecasting Portuguese GDP with factor models: Pre- and post-crisis evidence," Economic Modelling, Elsevier, vol. 44(C), pages 266-272.

3. Paulo Esteves & Maximiano Pinheiro, 2008. "On the uncertainty and risks of macroeconomic forecasts: Combining judgements with sample and model information," Working Papers w200821, Banco de Portugal, Economics and Research Department.
   • Maximiano Pinheiro & Paulo Esteves, 2012. "On the uncertainty and risks of macroeconomic forecasts: combining judgements with sample and model information," Empirical Economics, Springer, vol. 42(3), pages 639-665, June.

   Cited by:

   1. Michal Franta & Jozef Barunik & Roman Horvath & Katerina Smidkova, 2011. "Are Bayesian Fan Charts Useful for Central Banks? Uncertainty, Forecasting, and Financial Stability Stress Tests," Working Papers 2011/10, Czech National Bank.
   2. Fabio Busetti & Michele Caivano & Davide Delle Monache & Claudia Pacella, 2020. "The time-varying risk of Italian GDP," Temi di discussione (Economic working papers) 1288, Bank of Italy, Economic Research and International Relations Area.
      • Busetti, Fabio & Caivano, Michele & Delle Monache, Davide & Pacella, Claudia, 2021. "The time-varying risk of Italian GDP," Economic Modelling, Elsevier, vol. 101(C).
   3. Knüppel, Malte & Schultefrankenfeld, Guido, 2011. "How informative are central bank assessments of macroeconomic risks?," Discussion Paper Series 1: Economic Studies 2011,13, Deutsche Bundesbank.
      • Malte Knüppel & Guido Schultefrankenfeld, 2012. "How Informative Are Central Bank Assessments of Macroeconomic Risks?," International Journal of Central Banking, International Journal of Central Banking, vol. 8(3), pages 87-139, September.
   4. Wojciech Charemza & Carlos Diaz Vela & Svetlana Makarova, 2013. "Inflation fan charts, monetary policy and skew normal distribution," Discussion Papers in Economics 13/06, Division of Economics, School of Business, University of Leicester.
      • Wojciech Charemza & Carlos Diaz & Svetlana Makarova, 2014. "Term Structure Of Inflation Forecast Uncertainties And Skew Normal Distributions," Discussion Papers in Economics 14/01, Division of Economics, School of Business, University of Leicester.
      • Wojciech Charemza & Carlos Díaz & Svetlana Makarova, 2015. "Ex-post Inflation Forecast Uncertainty and Skew Normal Distribution: ‘Back from the Future’ Approach," Discussion Papers in Economics 15/09, Division of Economics, School of Business, University of Leicester.
   5. Liao, Xin & Peng, Zuoxiang & Nadarajah, Saralees, 2013. "Asymptotic expansions for moments of skew-normal extremes," Statistics & Probability Letters, Elsevier, vol. 83(5), pages 1321-1329.
   6. Michal Franta & Jozef Baruník & Roman Horváth & Katerina Smídková, 2014. "Are Bayesian Fan Charts Useful? The Effect of Zero Lower Bound and Evaluation of Financial Stability Stress Tests," International Journal of Central Banking, International Journal of Central Banking, vol. 10(1), pages 159-188, March.
   7. Liao, Xin & Peng, Zuoxiang & Nadarajah, Saralees & Wang, Xiaoqian, 2014. "Rates of convergence of extremes from skew-normal samples," Statistics & Probability Letters, Elsevier, vol. 84(C), pages 40-47.

4. António Rua & Francisco Craveiro Dias, 2008. "Forecasting Using Targeted Diffusion Indexes," Working Papers w200807, Banco de Portugal, Economics and Research Department.
   • Francisco Dias & Maximiano Pinheiro & António Rua, 2010. "Forecasting using targeted diffusion indexes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(3), pages 341-352.

   Cited by:

   1. Jo~ao B. Assunc{c}~ao & Pedro Afonso Fernandes, 2022. "Nowcasting the Portuguese GDP with Monthly Data," Papers 2206.06823, arXiv.org.
   2. José R. Maria & Sara Serra, 2008. "Forecasting investment: A fishing contest using survey data," Working Papers w200818, Banco de Portugal, Economics and Research Department.
   3. Demetrescu, Matei & Hacioglu Hoke, Sinem, 2018. "Predictive regressions under asymmetric loss: factor augmentation and model selection," Bank of England working papers 723, Bank of England.
      • Demetrescu, Matei & Hacıoğlu Hoke, Sinem, 2019. "Predictive regressions under asymmetric loss: Factor augmentation and model selection," International Journal of Forecasting, Elsevier, vol. 35(1), pages 80-99.
   4. João B. Assunção & Pedro Afonso Fernandes, 2022. "Nowcasting GDP: An Application to Portugal," Forecasting, MDPI, vol. 4(3), pages 1-15, August.
   5. Aye, Goodness C. & Balcilar, Mehmet & Gupta, Rangan & Majumdar, Anandamayee, 2015. "Forecasting aggregate retail sales: The case of South Africa," International Journal of Production Economics, Elsevier, vol. 160(C), pages 66-79.
      • Goodness C. Aye & Mehmet Balcilar Author-Name-First Mehmet & Rangan Gupta & Anandamayee Majumdar, 2014. "Forecasting Aggregate Retail Sales: The Case of South Africa," Working Papers 15-21, Eastern Mediterranean University, Department of Economics.
      • Goodness C. Aye & Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar, 2013. "Forecasting Aggregate Retail Sales: The Case of South Africa," Working Papers 201312, University of Pretoria, Department of Economics.
   6. Johannes Tang Kristensen, 2013. "Diffusion Indexes with Sparse Loadings," CREATES Research Papers 2013-22, Department of Economics and Business Economics, Aarhus University.
   7. Karim Barhoumi & Olivier Darné & Laurent Ferrara, 2010. "Are disaggregate data useful for factor analysis in forecasting French GDP?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 132-144.
      • Barhoumi, K. & Darné, O. & Ferrara, L., 2009. "Are disaggregate data useful for factor analysis in forecasting French GDP?," Working papers 232, Banque de France.
   8. Dias, Francisco & Pinheiro, Maximiano & Rua, António, 2015. "Forecasting Portuguese GDP with factor models: Pre- and post-crisis evidence," Economic Modelling, Elsevier, vol. 44(C), pages 266-272.
   9. Francisco Dias & Maximiano Pinheiro & António Rua, 2018. "A bottom-up approach for forecasting GDP in a data-rich environment," Applied Economics Letters, Taylor & Francis Journals, vol. 25(10), pages 718-723, June.

5. Maximiano Pinheiro, 2007. "MISS: A model for assessing the sustainability of public social security in Portugal," Working Papers o200703, Banco de Portugal, Economics and Research Department.

   Cited by:

   1. Ionel Bostan & Carmen Toderașcu & Anca Florentina Gavriluţă (Vatamanu), 2018. "Challenges and Vulnerabilities on Public Finance Sustainability. A Romanian Case Study," JRFM, MDPI, vol. 11(3), pages 1-24, September.
   2. Zaman, Constantin, 2011. "Assessing the Sustainability of Public Finances in Romania," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 106-115, June.

6. Maximiano Pinheiro, 2003. "Uncertainty And Risk Analysis Of Macroeconomic Forecasts: Fan Charts Revisited," Working Papers w200319, Banco de Portugal, Economics and Research Department.

   Cited by:

   1. Maximiano Pinheiro & Paulo Esteves, 2012. "On the uncertainty and risks of macroeconomic forecasts: combining judgements with sample and model information," Empirical Economics, Springer, vol. 42(3), pages 639-665, June.
      • Paulo Esteves & Maximiano Pinheiro, 2008. "On the uncertainty and risks of macroeconomic forecasts: Combining judgements with sample and model information," Working Papers w200821, Banco de Portugal, Economics and Research Department.
   2. Schultefrankenfeld, Guido, 2010. "Forecast uncertainty and the Bank of England interest rate decisions," Discussion Paper Series 1: Economic Studies 2010,27, Deutsche Bundesbank.
      • Schultefrankenfeld Guido, 2013. "Forecast uncertainty and the Bank of England’s interest rate decisions," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(1), pages 1-20, February.
   3. Paloviita, Maritta & Mayes, David, 2005. "The use of real-time information in Phillips-curve relationships for the euro area," The North American Journal of Economics and Finance, Elsevier, vol. 16(3), pages 415-434, December.
      • Paloviita, Maritta & Mayes, David, 2004. "The use of real-time information in Phillips curve relationships for the euro area," Discussion Paper Series 1: Economic Studies 2004,28, Deutsche Bundesbank.
      • Paloviita, Maritta & Mayes, David G., 2004. "The use of real time information in Phillips curve relationships for the euro area," Bank of Finland Research Discussion Papers 16/2004, Bank of Finland.
   4. Bohdan Klos & Ryszard Kokoszczynski & Tomasz Lyziak & Jan Przystupa & Ewa Wrobel, 2005. "Structural Econometric Models in Forecasting Inflation at the National Bank of Poland," NBP Working Papers 31, Narodowy Bank Polski.
   5. Ohnsorge,Franziska Lieselotte & Stocker,Marc & Some,Modeste Y., 2016. "Quantifying uncertainties in global growth forecasts," Policy Research Working Paper Series 7770, The World Bank.

Articles

1. Francisco Dias & Maximiano Pinheiro & António Rua, 2018. "A bottom-up approach for forecasting GDP in a data-rich environment," Applied Economics Letters, Taylor & Francis Journals, vol. 25(10), pages 718-723, June.

   Cited by:

   1. Lourenço, Nuno & Gouveia, Carlos Melo & Rua, António, 2021. "Forecasting tourism with targeted predictors in a data-rich environment," Economic Modelling, Elsevier, vol. 96(C), pages 445-454.
   2. Yutaka Kurihara & Akio Fukushima, 2019. "AR Model or Machine Learning for Forecasting GDP and Consumer Price for G7 Countries," Applied Economics and Finance, Redfame publishing, vol. 6(3), pages 1-6, May.

2. Dias, Francisco & Pinheiro, Maximiano & Rua, António, 2015. "Forecasting Portuguese GDP with factor models: Pre- and post-crisis evidence," Economic Modelling, Elsevier, vol. 44(C), pages 266-272.

   Cited by:

   1. Dušan Marković & Igor Mladenović & Miloš Milovančević, 2017. "RETRACTED ARTICLE: Estimation of the most influential science and technology factors for economic growth forecasting by soft computing technique," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(3), pages 1133-1146, May.
   2. Rusnák, Marek, 2016. "Nowcasting Czech GDP in real time," Economic Modelling, Elsevier, vol. 54(C), pages 26-39.
      • Marek Rusnak, 2013. "Nowcasting Czech GDP in Real Time," Working Papers 2013/06, Czech National Bank.
   3. Jo~ao B. Assunc{c}~ao & Pedro Afonso Fernandes, 2022. "Nowcasting the Portuguese GDP with Monthly Data," Papers 2206.06823, arXiv.org.
   4. Duarte, Cláudia & Rodrigues, Paulo M.M. & Rua, António, 2017. "A mixed frequency approach to the forecasting of private consumption with ATM/POS data," International Journal of Forecasting, Elsevier, vol. 33(1), pages 61-75.
   5. Sokolov-Mladenović, Svetlana & Milovančević, Milos & Mladenović, Igor, 2017. "Evaluation of trade influence on economic growth rate by computational intelligence approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 358-362.
   6. Eric W. K. See-To & Eric W. T. Ngai, 2018. "Customer reviews for demand distribution and sales nowcasting: a big data approach," Annals of Operations Research, Springer, vol. 270(1), pages 415-431, November.
   7. João B. Assunção & Pedro Afonso Fernandes, 2022. "Nowcasting GDP: An Application to Portugal," Forecasting, MDPI, vol. 4(3), pages 1-15, August.
   8. Petra Karanikić & Igor Mladenović & Svetlana Sokolov-Mladenović & Meysam Alizamir, 2017. "RETRACTED ARTICLE: Prediction of economic growth by extreme learning approach based on science and technology transfer," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(3), pages 1395-1401, May.
   9. Marković, Dušan & Petković, Dalibor & Nikolić, Vlastimir & Milovančević, Miloš & Petković, Biljana, 2017. "Soft computing prediction of economic growth based in science and technology factors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 217-220.
   10. Karen Poghosyan & Ruben Poghosyan, 2021. "On the Applicability of Dynamic Factor Models for Forecasting Real GDP Growth in Armenia," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 71(1), pages 52-79, June.
       • Poghosyan, Karen & Poghosyan, Ruben, 2021. "On the applicability of dynamic factor models for forecasting real GDP growth in Armenia," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 61, pages 28-46.
   11. Soybilgen, Barış & Yazgan, Ege, 2018. "Evaluating nowcasts of bridge equations with advanced combination schemes for the Turkish unemployment rate," Economic Modelling, Elsevier, vol. 72(C), pages 99-108.
   12. Samvel S. Lazaryan & Nikita E. German, 2018. "Forecasting Current GDP Dynamics With Google Search Data," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 6, pages 83-94, December.
   13. Jiang, Yu & Guo, Yongji & Zhang, Yihao, 2017. "Forecasting China's GDP growth using dynamic factors and mixed-frequency data," Economic Modelling, Elsevier, vol. 66(C), pages 132-138.
   14. Boriss Siliverstovs, 2015. "Dissecting Models' Forecasting Performance," KOF Working papers 15-397, KOF Swiss Economic Institute, ETH Zurich.
       • Siliverstovs, Boriss, 2017. "Dissecting models' forecasting performance," Economic Modelling, Elsevier, vol. 67(C), pages 294-299.
   15. Milačić, Ljubiša & Jović, Srđan & Vujović, Tanja & Miljković, Jovica, 2017. "Application of artificial neural network with extreme learning machine for economic growth estimation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 285-288.
   16. 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.
   17. Abdić Ademir & Resić Emina & Abdić Adem, 2020. "Modelling and forecasting GDP using factor model: An empirical study from Bosnia and Herzegovina," Croatian Review of Economic, Business and Social Statistics, Sciendo, vol. 6(1), pages 10-26, May.
   18. Abdić Ademir & Resić Emina & Abdić Adem & Rovčanin Adnan, 2020. "Nowcasting GDP of Bosnia and Herzegovina: A Comparison of Forecast Accuracy Models," South East European Journal of Economics and Business, Sciendo, vol. 15(2), pages 1-14, December.
   19. Đokić, Aleksandar & Jović, Srđan, 2017. "Evaluation of agriculture and industry effect on economic health by ANFIS approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 396-399.
   20. Igor Mladenović & Miloš Milovančević & Svetlana Sokolov-Mladenović, 2017. "RETRACTED ARTICLE: Analyzing of innovations influence on economic growth by fuzzy system," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(3), pages 1297-1304, May.
   21. Maksimović, Goran & Jović, Srđan & Jovanović, Radomir, 2017. "Economic growth rate management by soft computing approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 520-524.
   22. Kordanuli, Bojana & Barjaktarović, Lidija & Jeremić, Ljiljana & Alizamir, Meysam, 2017. "Appraisal of artificial neural network for forecasting of economic parameters," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 515-519.
   23. Francisco Dias & Maximiano Pinheiro & António Rua, 2018. "A bottom-up approach for forecasting GDP in a data-rich environment," Applied Economics Letters, Taylor & Francis Journals, vol. 25(10), pages 718-723, June.

3. Dias Francisco & Pinheiro Maximiano & Rua António, 2013. "Determining the number of global and country-specific factors in the euro area," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(5), pages 573-617, December.

   Cited by:

   1. In Choi & Dukpa Kim & Yun Jung Kim & Noh-Sun Kwark, 2016. "A Multilevel Factor Model: Identification, Asymptotic Theory and Applications," Working Papers 1609, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
      • In Choi & Dukpa Kim & Yun Jung Kim & Noh‐Sun Kwark, 2018. "A multilevel factor model: Identification, asymptotic theory and applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(3), pages 355-377, April.
   2. Freyaldenhoven, Simon, 2022. "Factor models with local factors — Determining the number of relevant factors," Journal of Econometrics, Elsevier, vol. 229(1), pages 80-102.
      • Simon Freyaldenhoven, 2021. "Factor Models with Local Factors—Determining the Number of Relevant Factors," Working Papers 21-15, Federal Reserve Bank of Philadelphia.
   3. Simon Freyaldenhoven, 2017. "A Generalized Factor Model with Local Factors," 2017 Papers pfr361, Job Market Papers.
      • Simon Freyaldenhoven, 2019. "A Generalized Factor Model with Local Factors," Working Papers 19-23, Federal Reserve Bank of Philadelphia.
   4. Choi, In & Lin, Rui & Shin, Yongcheol, 2023. "Canonical correlation-based model selection for the multilevel factors," Journal of Econometrics, Elsevier, vol. 233(1), pages 22-44.
      • In Choi & Rui Lin & Yongcheol Shin, 2020. "Canonical Correlation-based Model Selection for the Multilevel Factors," Working Papers 2008, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
   5. Byoungsoo Cho, 2020. "The Monetary Policy Reaction Function in Korea with Multi-level Factors," Korean Economic Review, Korean Economic Association, vol. 36, pages 353-376.
   6. Simon Freyaldenhoven, 2020. "Identification Through Sparsity in Factor Models," Working Papers 20-25, Federal Reserve Bank of Philadelphia.
   7. Bai, Jushan & Liao, Yuan, 2016. "Efficient estimation of approximate factor models via penalized maximum likelihood," Journal of Econometrics, Elsevier, vol. 191(1), pages 1-18.
   8. Kim Dukpa & Kim Yunjung & Bak Yuhyeon, 2017. "Multi-level factor analysis of bond risk premia," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(5), pages 1-19, December.
   9. António Rua & Francisco Dias, 2020. "A non-hierarchical dynamic factor model for three-way data," Working Papers w202007, Banco de Portugal, Economics and Research Department.
   10. Matteo Barigozzi & Marc Hallin & Stefano Soccorsi, 2017. "Identification of Global and National Shocks in International Financial Markets via General Dynamic Factor Models," Working Papers ECARES ECARES 2017-10, ULB -- Universite Libre de Bruxelles.

4. Maximiano Pinheiro & António Rua & Francisco Dias, 2013. "Dynamic Factor Models with Jagged Edge Panel Data: Taking on Board the Dynamics of the Idiosyncratic Components," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(1), pages 80-102, February.
   • António Rua & Maximiano Pinheiro, 2009. "Dynamic factor models with jagged edge panel data: Taking on board the dynamics of the idiosyncratic components," Working Papers w200913, Banco de Portugal, Economics and Research Department.
   See citations under working paper version above.
5. Maximiano Pinheiro & Paulo Esteves, 2012. "On the uncertainty and risks of macroeconomic forecasts: combining judgements with sample and model information," Empirical Economics, Springer, vol. 42(3), pages 639-665, June.
   • Paulo Esteves & Maximiano Pinheiro, 2008. "On the uncertainty and risks of macroeconomic forecasts: Combining judgements with sample and model information," Working Papers w200821, Banco de Portugal, Economics and Research Department.
   See citations under working paper version above.
6. Francisco Dias & Maximiano Pinheiro & António Rua, 2010. "Forecasting using targeted diffusion indexes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(3), pages 341-352.
   • António Rua & Francisco Craveiro Dias, 2008. "Forecasting Using Targeted Diffusion Indexes," Working Papers w200807, Banco de Portugal, Economics and Research Department.
   See citations under working paper version above.
7. Dias, Francisco C & Machado, Jose A F & Pinheiro, Maximiano R, 1996. "Structural VAR Estimation with Exogeneity Restrictions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 58(2), pages 417-422, May.

   Cited by:

   1. Alfred A. Haug & Christie Smith, 2007. "Local linear impulse responses for a small open economy," Working Papers 0707, University of Otago, Department of Economics, revised Apr 2007.
      • Alfred A. Haug & Christie Smith, 2012. "Local Linear Impulse Responses for a Small Open Economy," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 74(3), pages 470-492, June.
      • Alfred A Haug & Christie Smith, 2007. "Local linear impulse responses for a small open economy," Reserve Bank of New Zealand Discussion Paper Series DP2007/09, Reserve Bank of New Zealand.
   2. Jorge Fornero & Markus Kirchner & Carlos Molina, 2021. "Estimating Shadow Policy Rates in a Small Open Economy and the Role of Foreign Factors," Working Papers Central Bank of Chile 915, Central Bank of Chile.
      • Fornero, Jorge & Kirchner, Markus & Molina, Carlos, 2024. "Estimating shadow policy rates in a small open economy and the role of foreign factors," Journal of International Money and Finance, Elsevier, vol. 140(C).
   3. Zha, Tao, 1999. "Block recursion and structural vector autoregressions," Journal of Econometrics, Elsevier, vol. 90(2), pages 291-316, June.
   4. Tao Zha, 1997. "Identifying monetary policy: a primer," Economic Review, Federal Reserve Bank of Atlanta, vol. 82(Q 2), pages 26-43.

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1. Portuguese Economists

NEP Fields

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1. NEP-ECM: Econometrics (2) 2010-07-31 2020-06-15
2. NEP-EEC: European Economics (1) 2012-06-25
3. NEP-MAC: Macroeconomics (1) 2020-06-15
4. NEP-ORE: Operations Research (1) 2020-06-15

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