IDEAS home Printed from https://ideas.repec.org/f/c/ppo612.html
   My authors  Follow this author

Pilar Poncela

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. 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.

    Cited by:

    1. Fresoli, Diego & Poncela, Pilar & Ruiz, Esther, 2023. "Ignoring cross-correlated idiosyncratic components when extracting factors in dynamic factor models," Economics Letters, Elsevier, vol. 230(C).
    2. Karen Miranda & Pilar Poncela & Esther Ruiz, 2022. "Dynamic factor models: Does the specification matter?," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 13(1), pages 397-428, May.
    3. Juan, Aranzazu de & Poncela, Maria Pilar, 2023. "Economic activity and C02 emissions in Spain," DES - Working Papers. Statistics and Econometrics. WS 37975, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. Lippi, Marco & Deistler, Manfred & Anderson, Brian, 2023. "High-Dimensional Dynamic Factor Models: A Selective Survey and Lines of Future Research," Econometrics and Statistics, Elsevier, vol. 26(C), pages 3-16.
    5. Juho Koistinen & Bernd Funovits, 2022. "Estimation of Impulse-Response Functions with Dynamic Factor Models: A New Parametrization," Papers 2202.00310, arXiv.org, revised Feb 2022.
    6. Matteo Barigozzi & Marc Hallin, 2023. "Dynamic Factor Models: a Genealogy," Papers 2310.17278, arXiv.org, revised Jan 2024.
    7. 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).
    8. Escribano, Alvaro & Peña, Daniel & Ruiz, Esther, 2021. "30 years of cointegration and dynamic factor models forecasting and its future with big data: Editorial," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1333-1337.
    9. In Choi, 2023. "Does climate change affect economic data?," Empirical Economics, Springer, vol. 64(6), pages 2939-2956, June.
    10. Luke Mosley & Tak-Shing Chan & Alex Gibberd, 2023. "sparseDFM: An R Package to Estimate Dynamic Factor Models with Sparse Loadings," Papers 2303.14125, arXiv.org.
    11. Fatemeh Bakhshi Ostadkalayeh & Saba Moradi & Ali Asadi & Alireza Moghaddam Nia & Somayeh Taheri, 2023. "Performance Improvement of LSTM-based Deep Learning Model for Streamflow Forecasting Using Kalman Filtering," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(8), pages 3111-3127, June.
    12. Shu‐Lien Chang & Hsiu‐Chuan Lee & Donald Lien, 2022. "The global latent factor and international index futures returns predictability," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 514-538, April.

  2. Poncela, Pilar & Ruiz, Esther, 2020. "A comment on the dynamic factor model with dynamic factors," Economics Discussion Papers 2020-7, Kiel Institute for the World Economy (IfW Kiel).

    Cited by:

    1. 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.
    2. Lucchetti, Riccardo & Venetis, Ioannis A., 2020. "A replication of "A quasi-maximum likelihood approach for large, approximate dynamic factor models" (Review of Economics and Statistics, 2012)," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 14, pages 1-14.

  3. Asdrubali, Pierfederico & Kim, Soyoung & Pericoli, Filippo & Poncela, Pilar, 2018. "New Risk Sharing Channels in OECD Countries: a Heterogeneous Panel VAR," Working Papers 2018-13, Joint Research Centre, European Commission.

    Cited by:

    1. Clancy, Daragh & Ricci, Lorenzo, 2022. "Economic sentiments and international risk sharing," International Economics, Elsevier, vol. 169(C), pages 208-229.
    2. Zouri, Stéphane, 2021. "New evidence on international risk-sharing in the Economic Community of West African States (ECOWAS)," International Economics, Elsevier, vol. 165(C), pages 121-139.
    3. Giovannini, Alessandro & Ioannou, Demosthenes & Stracca, Livio, 2022. "Public and private risk sharing: friends or foes? The interplay between different forms of risk sharing," Occasional Paper Series 295, European Central Bank.
    4. Marius Clemens & Stefan Gebauer & Tobias König, 2020. "The Macroeconomic Effects of a European Deposit (Re-) Insurance Scheme," Discussion Papers of DIW Berlin 1873, DIW Berlin, German Institute for Economic Research.
    5. Joongsan Ko, 2020. "Intranational Consumption Risk Sharing in South Korea: 2000–2016," Asian Economic Journal, East Asian Economic Association, vol. 34(1), pages 29-49, March.
    6. Pasquale Foresti & Oreste Napolitano, 2022. "Risk Sharing in the EMU: A Time‐Varying Perspective," Journal of Common Market Studies, Wiley Blackwell, vol. 60(2), pages 319-336, March.
    7. Daragh Clancy & Lorenzo Ricci, 2019. "Loss aversion, economic sentiments and international consumption smoothing," Working Papers 35, European Stability Mechanism.
    8. Du, Julan & He, Qing & Zhang, Ce, 2022. "Risk sharing and industrial specialization in China," Journal of Comparative Economics, Elsevier, vol. 50(2), pages 599-626.
    9. Martín Fuentes, Natalia & Born, Alexandra & Bremus, Franziska & Kastelein, Wieger & Lambert, Claudia, 2023. "A deep dive into the capital channel of risk sharing in the euro area," Working Paper Series 2864, European Central Bank.

  4. 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.

    Cited by:

    1. Chiara Casoli & Riccardo (Jack) Lucchetti, 2022. "Permanent-Transitory decomposition of cointegrated time series via dynamic factor models, with an application to commodity prices [Commodity-price comovement and global economic activity]," The Econometrics Journal, Royal Economic Society, vol. 25(2), pages 494-514.
    2. Gonzalo, Jesús & Pitarakis, Jean-Yves, 2021. "Spurious relationships in high-dimensional systems with strong or mild persistence," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1480-1497.
    3. Francisco Corona & Graciela González-Farías & Pedro Orraca, 2017. "A dynamic factor model for the Mexican economy: are common trends useful when predicting economic activity?," Latin American Economic Review, Springer;Centro de Investigaciòn y Docencia Económica (CIDE), vol. 26(1), pages 1-35, December.
    4. Philip Hans Franses & Thomas Wiemann, 2020. "Intertemporal Similarity of Economic Time Series: An Application of Dynamic Time Warping," Computational Economics, Springer;Society for Computational Economics, vol. 56(1), pages 59-75, June.
    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.
    6. Miljkovic, Dragan & Vatsa, Puneet, 2023. "On the linkages between energy and agricultural commodity prices: A dynamic time warping analysis," International Review of Financial Analysis, Elsevier, vol. 90(C).
    7. Proietti, Tommaso & Giovannelli, Alessandro & Ricchi, Ottavio & Citton, Ambra & Tegami, Christían & Tinti, Cristina, 2021. "Nowcasting GDP and its components in a data-rich environment: The merits of the indirect approach," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1376-1398.
    8. 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.

  5. 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.

    Cited by:

    1. Alessi, Lucia & Kerssenfischer, Mark, 2016. "The response of asset prices to monetary policy shocks: stronger than thought," Working Paper Series 1967, European Central Bank.
    2. 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.
    3. Ergemen, Yunus Emre & Rodríguez-Caballero, C. Vladimir, 2023. "Estimation of a dynamic multi-level factor model with possible long-range dependence," International Journal of Forecasting, Elsevier, vol. 39(1), pages 405-430.
    4. Francisco Corona & Pilar Poncela & Esther Ruiz, 2020. "Estimating Non-stationary Common Factors: Implications for Risk Sharing," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 37-60, January.
    5. Marcos Bujosa & Antonio García‐Ferrer & Aránzazu de Juan & Antonio Martín‐Arroyo, 2020. "Evaluating early warning and coincident indicators of business cycles using smooth trends," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 1-17, January.
    6. 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.

  6. Pilar Poncela & Filippo Pericoli & Anna Manca & Filippo Michela Nardo, 2016. "Risk Sharing in Europe," JRC Research Reports JRC104621, Joint Research Centre.

    Cited by:

    1. Bofinger, Peter & Feld, Lars P. & Schmidt, Christoph M. & Schnabel, Isabel & Wieland, Volker, 2018. "Vor wichtigen wirtschaftspolitischen Weichenstellungen. Jahresgutachten 2018/19 [Setting the Right Course for Economic Policy. Annual Report 2018/19]," Annual Economic Reports / Jahresgutachten, German Council of Economic Experts / Sachverständigenrat zur Begutachtung der gesamtwirtschaftlichen Entwicklung, volume 127, number 201819.
    2. Ferrari, Alessandro & Rogantini Picco, Anna, 2023. "Risk sharing and the adoption of the Euro," Journal of International Economics, Elsevier, vol. 141(C).
    3. Gabrisch, Hubert, 2018. "A fire department for the Euro area: reflections on a fiscal risk-sharing capacity," MPRA Paper 83965, University Library of Munich, Germany.
    4. Esther Gordo & Ivan Kataryniuk, 2019. "Towards a more resilient euro area," Economics and Business Letters, Oviedo University Press, vol. 8(2), pages 106-114.

  7. 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.

    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.
    3. Karen Miranda & Pilar Poncela & Esther Ruiz, 2022. "Dynamic factor models: Does the specification matter?," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 13(1), pages 397-428, May.
    4. Poncela, Pilar & Ruiz, Esther, 2020. "A comment on the dynamic factor model with dynamic factors," Economics Discussion Papers 2020-7, Kiel Institute for the World Economy (IfW Kiel).
    5. Francisco Corona & Pilar Poncela & Esther Ruiz, 2020. "Estimating Non-stationary Common Factors: Implications for Risk Sharing," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 37-60, January.
    6. Daniel Kaufmann & Rolf Scheufele, 2015. "Business tendency surveys and macroeconomic fluctuations," KOF Working papers 15-378, KOF Swiss Economic Institute, ETH Zurich.
    7. Alonso, Andrés M. & Galeano, Pedro & Peña, Daniel, 2020. "A robust procedure to build dynamic factor models with cluster structure," Journal of Econometrics, Elsevier, vol. 216(1), pages 35-52.
    8. Corona, Francisco & Orraca, Pedro, 2016. "Remittances in Mexico and their unobserved components," DES - Working Papers. Statistics and Econometrics. WS 22674, Universidad Carlos III de Madrid. Departamento de Estadística.
    9. 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.

  8. Fuentes, Julieta & Poncela, Pilar & Rodríguez, Julio, 2014. "Selecting and combining experts from survey forecasts," DES - Working Papers. Statistics and Econometrics. WS ws140905, Universidad Carlos III de Madrid. Departamento de Estadística.

    Cited by:

    1. Francis X. Diebold & Minchul Shin, 2017. "Beating the Simple Average: Egalitarian LASSO for Combining Economic Forecasts," PIER Working Paper Archive 17-017, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 20 Aug 2017.

  9. Maximo Camacho & Gabriel Perez-Quiros & Pilar Poncela, 2013. "Short-term forecasting for empirical economists. A survey of the recently proposed algorithms," Working Papers 1318, Banco de España.

    Cited by:

    1. Oxana Babecka Kucharcukova & Jan Bruha, 2016. "Nowcasting the Czech Trade Balance," Working Papers 2016/11, Czech National Bank.
    2. Carl Bonham & Peter Fuleky & James Jones & Ashley Hirashima, 2015. "Nowcasting Tourism Industry Performance Using High Frequency Covariates," Working Papers 2015-13R, University of Hawaii Economic Research Organization, University of Hawaii at Manoa, revised Jul 2016.
    3. Götz, Thomas B. & Knetsch, Thomas A., 2017. "Google data in bridge equation models for German GDP," Discussion Papers 18/2017, Deutsche Bundesbank.
    4. Mikosch, Heiner & Solanko, Laura, 2017. "Should one follow movements in the oil price or in money supply? Forecasting quarterly GDP growth in Russia with higher-frequency indicators," BOFIT Discussion Papers 19/2017, Bank of Finland Institute for Emerging Economies (BOFIT).
    5. Pavel Vidal Alejandro & Lya Paola Sierra Suárez & Johana Sanabria Dominguez & Jaime Andres Collazos Rodríguez, 2015. "Indicador mensual de actividad económica (IMAE) para el Valle del Cauca," Borradores de Economia 900, Banco de la Republica de Colombia.
    6. Fornaro, Paolo, 2016. "Predicting Finnish economic activity using firm-level data," International Journal of Forecasting, Elsevier, vol. 32(1), pages 10-19.
    7. Tóth, Peter, 2014. "Malý dynamický faktorový model na krátkodobé prognózovanie slovenského HDP [A Small Dynamic Factor Model for the Short-Term Forecasting of Slovak GDP]," MPRA Paper 63713, University Library of Munich, Germany.
    8. Allan, Grant & Koop, Gary & McIntyre, Stuart & Smith, Paul, 2014. "Nowcasting Scottish GDP Growth," SIRE Discussion Papers 2015-08, Scottish Institute for Research in Economics (SIRE).
    9. 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.
    10. Marcos Bujosa & Antonio García‐Ferrer & Aránzazu de Juan & Antonio Martín‐Arroyo, 2020. "Evaluating early warning and coincident indicators of business cycles using smooth trends," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 1-17, January.
    11. Tony Chernis & Rodrigo Sekkel, 2018. "Nowcasting Canadian Economic Activity in an Uncertain Environment," Discussion Papers 18-9, Bank of Canada.
    12. Hirashima, Ashley & Jones, James & Bonham, Carl S. & Fuleky, Peter, 2017. "Forecasting in a Mixed Up World: Nowcasting Hawaii Tourism," Annals of Tourism Research, Elsevier, vol. 63(C), pages 191-202.
    13. Grant Allan & Gary Koop & Stuart McIntyre & Paul Smith, 2019. "Nowcasting Using Mixed Frequency Methods: An Application to the Scottish Economy," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 12-45, September.
    14. Мекенбаева Камила // Mekenbayeva Kamila & Karel Musil, 2017. "Система прогнозирования в Национальном Банке Казахстана: наукаст на основа опросов // Forecasting system at the National Bank of Kazakhstan: survey-based nowcasting," Working Papers #2017-1, National Bank of Kazakhstan.
    15. Mikel Bedayo & Ángel Estrada & Jesús Saurina, 2018. "Bank capital, lending booms, and busts. Evidence from Spain in the last 150 years," Working Papers 1847, Banco de España.
    16. Kitlinski, Tobias, 2015. "With or without you: Do financial data help to forecast industrial production?," Ruhr Economic Papers 558, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    17. Smith Paul, 2016. "Nowcasting UK GDP during the depression," Working Papers 1606, University of Strathclyde Business School, Department of Economics.

  10. Poncela, Pilar, 2012. "More is not always better : back to the Kalman filter in dynamic factor models," DES - Working Papers. Statistics and Econometrics. WS ws122317, Universidad Carlos III de Madrid. Departamento de Estadística.

    Cited by:

    1. Tóth, Peter, 2014. "Malý dynamický faktorový model na krátkodobé prognózovanie slovenského HDP [A Small Dynamic Factor Model for the Short-Term Forecasting of Slovak GDP]," MPRA Paper 63713, University Library of Munich, Germany.
    2. Lya Paola Sierra Suárez & Jaime Andrés Collazos-Rodríguez & Johana Sanabria-Domínguez & Pavel Vidal-Alejandro, 2017. "La construcción de indicadores de la actividad económica: una revisión bibliográfica," Apuntes del Cenes, Universidad Pedagógica y Tecnológica de Colombia, vol. 36(64), pages 79-107, October.
    3. Deicy J. Cristiano-Botia & Manuel Dario Hernandez-Bejarano & Mario A. Ramos-Veloza, 2021. "Labor Market Indicator for Colombia (LMI)," Borradores de Economia 1152, Banco de la Republica de Colombia.
    4. Camacho Maximo & Lovcha Yuliya & Quiros Gabriel Perez, 2015. "Can we use seasonally adjusted variables in dynamic factor models?," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(3), pages 377-391, June.
    5. Lauren Stagnol, 2019. "Extracting global factors from local yield curves," Journal of Asset Management, Palgrave Macmillan, vol. 20(5), pages 341-350, September.

  11. Fuentes, Julieta & Poncela, Pilar & Rodríguez, Julio, 2012. "Sparse partial least squares in time series for macroeconomic forecasting," DES - Working Papers. Statistics and Econometrics. WS ws122216, Universidad Carlos III de Madrid. Departamento de Estadística.

    Cited by:

    1. Marine Carrasco & Barbara Rossi, 2016. "In-sample inference and forecasting in misspecified factor models," Economics Working Papers 1530, Department of Economics and Business, Universitat Pompeu Fabra.
    2. Cepni, Oguzhan & Clements, Michael P., 2024. "How local is the local inflation factor? Evidence from emerging European countries," International Journal of Forecasting, Elsevier, vol. 40(1), pages 160-183.
    3. Alessandro Giovannelli & Tommaso Proietti, 2014. "On the Selection of Common Factors for Macroeconomic Forecasting," CREATES Research Papers 2014-46, Department of Economics and Business Economics, Aarhus University.
    4. Francisco Corona & Graciela González-Farías & Pedro Orraca, 2017. "A dynamic factor model for the Mexican economy: are common trends useful when predicting economic activity?," Latin American Economic Review, Springer;Centro de Investigaciòn y Docencia Económica (CIDE), vol. 26(1), pages 1-35, December.
    5. Constantin ANGHELACHE & Madalina-Gabriela ANGHEL & Tudor SAMSON & Radu STOICA, 2017. "Methods And Techniques For Preparing Forecasts," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 65(4), pages 26-36, April.
    6. Karen Miranda & Pilar Poncela & Esther Ruiz, 2022. "Dynamic factor models: Does the specification matter?," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 13(1), pages 397-428, May.
    7. Juan, Aranzazu de & Poncela, Maria Pilar, 2023. "Economic activity and C02 emissions in Spain," DES - Working Papers. Statistics and Econometrics. WS 37975, Universidad Carlos III de Madrid. Departamento de Estadística.
    8. Stamer, Vincent, 2022. "Thinking Outside the Container: A Sparse Partial Least Squares Approach to Forecasting Trade Flows," VfS Annual Conference 2022 (Basel): Big Data in Economics 264096, Verein für Socialpolitik / German Economic Association.
    9. Fuentes, Julieta & Poncela, Pilar & Rodríguez, Julio, 2014. "Selecting and combining experts from survey forecasts," DES - Working Papers. Statistics and Econometrics. WS ws140905, Universidad Carlos III de Madrid. Departamento de Estadística.
    10. Yin-Wong Cheung & Wenhao Wang, 2020. "Uncovered Interest Rate Parity Redux: Non- Uniform Effects," GRU Working Paper Series GRU_2020_004, City University of Hong Kong, Department of Economics and Finance, Global Research Unit.
    11. Hwee Kwan Chow & Yijie Fei & Daniel Han, 2023. "Forecasting GDP with many predictors in a small open economy: forecast or information pooling?," Empirical Economics, Springer, vol. 65(2), pages 805-829, August.
    12. Cheng, Mingmian & Swanson, Norman R. & Yang, Xiye, 2021. "Forecasting volatility using double shrinkage methods," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 46-61.

  12. Pérez-Quirós, Gabriel & Poncela, Pilar & Camacho, Máximo, 2012. "Green Shoots and Double Dips in the Euro Area. A Real Time Measure," CEPR Discussion Papers 8896, C.E.P.R. Discussion Papers.

    Cited by:

    1. Carstensen, Kai & Heinrich, Markus & Reif, Magnus & Wolters, Maik H., 2020. "Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model," International Journal of Forecasting, Elsevier, vol. 36(3), pages 829-850.
    2. Ferrara, Laurent & Marcellino, Massimiliano & Mogliani, Matteo, 2015. "Macroeconomic forecasting during the Great Recession: The return of non-linearity?," International Journal of Forecasting, Elsevier, vol. 31(3), pages 664-679.
    3. 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.
    4. John O’Trakoun, 2022. "Business forecasting during the pandemic," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 57(3), pages 95-110, July.
    5. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers halshs-02262202, HAL.
    6. Ben L. Kyer & Gary E. Maggs, 2019. "Some International Evidence on Double-Dip Recession," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 25(3), pages 347-362, August.
    7. Kihwan Kim & Hyun Hak Kim & Norman R. Swanson, 2023. "Mixing mixed frequency and diffusion indices in good times and in bad: an assessment based on historical data around the great recession of 2008," Empirical Economics, Springer, vol. 64(3), pages 1421-1469, March.
    8. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87.
    9. Agnieszka Gehringer & Thomas Mayer, 2021. "Measuring the Business Cycle Chronology with a Novel Business Cycle Indicator for Germany," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 17(1), pages 71-89, April.
    10. Marcellino, Massimiliano & Foroni, Claudia, 2014. "Markov-Switching Mixed-Frequency VAR Models," CEPR Discussion Papers 9815, C.E.P.R. Discussion Papers.
    11. James D. Hamilton, 2016. "Macroeconomic Regimes and Regime Shifts," NBER Working Papers 21863, National Bureau of Economic Research, Inc.
    12. Danilo Leiva-Leon & Gabriel Pérez-Quirós & Eyno Rots, 2020. "Real-Time Weakness of the Global Economy: A First Assessment of the Coronavirus Crisis," MNB Working Papers 2020/4, Magyar Nemzeti Bank (Central Bank of Hungary).
    13. Pirschel, Inske, 2016. "Forecasting euro area recessions in real-time," Kiel Working Papers 2020, Kiel Institute for the World Economy (IfW Kiel).
    14. Carstensen, Kai & Heinrich, Markus & Reif, Magnus & Wolters, Maik H., 2020. "Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model An application to the German business cycle," Munich Reprints in Economics 84736, University of Munich, Department of Economics.
    15. Pirschel, Inske, 2015. "Forecasting Euro Area Recessions in real-time with a mixed-frequency Bayesian VAR," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113031, Verein für Socialpolitik / German Economic Association.
    16. Yoshihiro Ohtsuka, 2018. "Large Shocks and the Business Cycle: The Effect of Outlier Adjustments," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 14(1), pages 143-178, April.
    17. Romain Aumond & Julien Royer, 2024. "Improving the robustness of Markov-switching dynamic factor models with time-varying volatility," Working Papers 2024-04, Center for Research in Economics and Statistics.

  13. Pérez-Quirós, Gabriel & Poncela, Pilar & Camacho, Máximo, 2012. "Markov-switching dynamic factor models in real time," CEPR Discussion Papers 8866, C.E.P.R. Discussion Papers.

    Cited by:

    1. Carstensen, Kai & Heinrich, Markus & Reif, Magnus & Wolters, Maik H., 2020. "Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model," International Journal of Forecasting, Elsevier, vol. 36(3), pages 829-850.
    2. Longo, Luigi & Riccaboni, Massimo & Rungi, Armando, 2022. "A neural network ensemble approach for GDP forecasting," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
    3. Marcelle Chauvet & Rafael R. S. Guimaraes, 2021. "Transfer Learning for Business Cycle Identification," Working Papers Series 545, Central Bank of Brazil, Research Department.
    4. Poncela, Pilar, 2012. "More is not always better : back to the Kalman filter in dynamic factor models," DES - Working Papers. Statistics and Econometrics. WS ws122317, Universidad Carlos III de Madrid. Departamento de Estadística.
    5. Paolo Andreini & Cosimo Izzo & Giovanni Ricco, 2020. "Deep Dynamic Factor Models," Papers 2007.11887, arXiv.org, revised May 2023.
    6. Christian Glocker & Philipp Wegmueller, 2020. "Business cycle dating and forecasting with real-time Swiss GDP data," Empirical Economics, Springer, vol. 58(1), pages 73-105, January.
    7. Guérin, Pierre & Leiva-Leon, Danilo, 2017. "Model averaging in Markov-switching models: Predicting national recessions with regional data," Economics Letters, Elsevier, vol. 157(C), pages 45-49.
    8. A. Girardi & R. Golinelli & C. Pappalardo, 2014. "The Role of Indicator Selection in Nowcasting Euro Area GDP in Pseudo Real Time," Working Papers wp919, Dipartimento Scienze Economiche, Universita' di Bologna.
    9. van Os, Bram & van Dijk, Dick, 2024. "Accelerating peak dating in a dynamic factor Markov-switching model," International Journal of Forecasting, Elsevier, vol. 40(1), pages 313-323.
    10. Li, Haixi & Sheng, Xuguang Simon & Yang, Jingyun, 2021. "Monitoring recessions: A Bayesian sequential quickest detection method," International Journal of Forecasting, Elsevier, vol. 37(2), pages 500-510.
    11. Maximo Camacho & Jaime Martinez-Martin, 2015. "Monitoring the world business cycle," Working Papers 1509, Banco de España.
    12. Foroni, Claudia & Marcellino, Massimiliano, 2014. "A comparison of mixed frequency approaches for nowcasting Euro area macroeconomic aggregates," International Journal of Forecasting, Elsevier, vol. 30(3), pages 554-568.
    13. Lopez-Buenache, German, 2019. "The evolution of monetary policy effectiveness under macroeconomic instability," Economic Modelling, Elsevier, vol. 83(C), pages 221-233.
    14. Buss, Ginters, 2010. "A note on GDP now-/forecasting with dynamic versus static factor models along a business cycle," MPRA Paper 22147, University Library of Munich, Germany.
    15. Andrea Giusto & Jeremy Piger, 2013. "Nowcasting U.S. Business Cycle Turning Points with Vector Quantization," Working Papers daleconwp2013-02, Dalhousie University, Department of Economics.
    16. 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.
    17. Pérez Quirós, Gabriel, 2020. "Global Weakness Index – reading the economy’s vital signs during the COVID-19 crisis," Research Bulletin, European Central Bank, vol. 72.
    18. Ergemen, Yunus Emre & Rodríguez-Caballero, C. Vladimir, 2023. "Estimation of a dynamic multi-level factor model with possible long-range dependence," International Journal of Forecasting, Elsevier, vol. 39(1), pages 405-430.
    19. 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.
    20. Pérez-Quirós, Gabriel & Poncela, Pilar & Camacho, Máximo, 2012. "Green Shoots and Double Dips in the Euro Area. A Real Time Measure," CEPR Discussion Papers 8896, C.E.P.R. Discussion Papers.
    21. Azqueta-Gavaldon, Andres & Hirschbühl, Dominik & Onorante, Luca & Saiz, Lorena, 2020. "Nowcasting business cycle turning points with stock networks and machine learning," Working Paper Series 2494, European Central Bank.
    22. Maximo Camacho & Gabriel Perez-Quiros & Pilar Poncela, 2010. "Green shoots in the euro area. A real time measure," Working Papers 1026, Banco de España.
    23. Pierzak, Agnieszka, 2013. "Forecasting inflation in Poland using dynamic factor model," MF Working Papers 17, Ministry of Finance in Poland, revised 01 Aug 2013.
    24. Chauvet, Marcelle & Potter, Simon, 2013. "Forecasting Output," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 141-194, Elsevier.
    25. Kihwan Kim & Hyun Hak Kim & Norman R. Swanson, 2023. "Mixing mixed frequency and diffusion indices in good times and in bad: an assessment based on historical data around the great recession of 2008," Empirical Economics, Springer, vol. 64(3), pages 1421-1469, March.
    26. Rafael R. S. Guimaraes, 2022. "Deep Learning Macroeconomics," Papers 2201.13380, arXiv.org.
    27. Fumio Hayashi & Yuta Tachi, 2023. "Nowcasting Japan’s GDP," Empirical Economics, Springer, vol. 64(4), pages 1699-1735, April.
    28. Cem Çakmakli & Hamza Dem I˙rcani & Sumru Altug, 2021. "Modelling of Economic and Financial Conditions for Real‐Time Prediction of Recessions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(3), pages 663-685, June.
    29. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87.
    30. Gabriel Pérez-Quiros & Maximo Camacho & Pilar Poncela, 2010. "Green Shoots? Where, when and how?," Working Papers 2010-04, FEDEA.
    31. Giusto, Andrea & Piger, Jeremy, 2017. "Identifying business cycle turning points in real time with vector quantization," International Journal of Forecasting, Elsevier, vol. 33(1), pages 174-184.
    32. Pami Dua, 2023. "Macroeconomic Modelling and Bayesian Methods," Springer Books, in: Pami Dua (ed.), Macroeconometric Methods, chapter 0, pages 19-37, Springer.
    33. Zhang, Wei & He, Jie & Ge, Chanyuan & Xue, Rui, 2022. "Real-time macroeconomic monitoring using mixed frequency data: Evidence from China," Economic Modelling, Elsevier, vol. 117(C).
    34. Pérez-Quirós, Gabriel & Camacho, Máximo & Lovcha, Yuliya, 2012. "Can we use seasonally adjusted indicators in dynamic factor models?," CEPR Discussion Papers 9191, C.E.P.R. Discussion Papers.
    35. Danilo Leiva-Leon & Gabriel Pérez-Quirós & Eyno Rots, 2020. "Real-Time Weakness of the Global Economy: A First Assessment of the Coronavirus Crisis," MNB Working Papers 2020/4, Magyar Nemzeti Bank (Central Bank of Hungary).
    36. Juan Laborda & Sonia Ruano & Ignacio Zamanillo, 2023. "Multi-Country and Multi-Horizon GDP Forecasting Using Temporal Fusion Transformers," Mathematics, MDPI, vol. 11(12), pages 1-26, June.
    37. Eraslan, Sercan & Nöller, Marvin, 2020. "Recession probabilities falling from the STARs," Discussion Papers 08/2020, Deutsche Bundesbank.
    38. Marcus Scheiblecker & Christian Glocker & Serguei Kaniovski & Atanas Pekanov, 2018. "Der Beitrag der Finanzmarktinterventionen des Bundes über die HETA Abwicklungsgesellschaft zur Stabilisierung des österreichischen Finanzmarktes," WIFO Studies, WIFO, number 60979, April.
    39. Kihwan Kim & Norman Swanson, 2013. "Diffusion Index Model Specification and Estimation Using Mixed Frequency Datasets," Departmental Working Papers 201315, Rutgers University, Department of Economics.
    40. Carstensen, Kai & Heinrich, Markus & Reif, Magnus & Wolters, Maik H., 2020. "Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model An application to the German business cycle," Munich Reprints in Economics 84736, University of Munich, Department of Economics.
    41. Romain Aumond & Julien Royer, 2024. "Improving the robustness of Markov-switching dynamic factor models with time-varying volatility," Working Papers 2024-04, Center for Research in Economics and Statistics.
    42. Wegmüller, Philipp & Glocker, Christian & Guggia, Valentino, 2023. "Weekly economic activity: Measurement and informational content," International Journal of Forecasting, Elsevier, vol. 39(1), pages 228-243.
    43. Máximo Camacho & Gonzalo Palmieri, 2021. "Evaluating the OECD’s main economic indicators at anticipating recessions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(1), pages 80-93, January.
    44. Degras, David & Ting, Chee-Ming & Ombao, Hernando, 2022. "Markov-switching state-space models with applications to neuroimaging," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).

  14. Pérez-Quirós, Gabriel & Poncela, Pilar & Camacho, Máximo, 2012. "Extracting nonlinear signals from several economic indicators," CEPR Discussion Papers 8865, C.E.P.R. Discussion Papers.

    Cited by:

    1. Michael T. Owyang & Jeremy Piger & Daniel Soques, 2022. "Contagious switching," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(2), pages 415-432, March.
    2. Carstensen, Kai & Heinrich, Markus & Reif, Magnus & Wolters, Maik H., 2020. "Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model," International Journal of Forecasting, Elsevier, vol. 36(3), pages 829-850.
    3. Jaime Martínez-Martín & Elena Rusticelli, 2020. "Keeping track of global trade in real time," Working Papers 2019, Banco de España.
    4. Guérin, Pierre & Leiva-Leon, Danilo, 2017. "Model averaging in Markov-switching models: Predicting national recessions with regional data," Economics Letters, Elsevier, vol. 157(C), pages 45-49.
    5. Maximo Camacho & Jaime Martinez-Martin, 2015. "Monitoring the world business cycle," Working Papers 1509, Banco de España.
    6. Sebastian Fossati, 2015. "Forecasting US recessions with macro factors," Applied Economics, Taylor & Francis Journals, vol. 47(53), pages 5726-5738, November.
    7. Joan Paredes & Javier J. Pérez & Gabriel Perez Quiros, 2023. "Fiscal targets. A guide to forecasters?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 472-492, June.
    8. Christiane Baumeister & Danilo Leiva-León & Eric R. Sims, 2021. "Tracking Weekly State-Level Economic Conditions," CESifo Working Paper Series 9165, CESifo.
    9. Hartigan, Luke & Morley, James, 2019. "A Factor Model Analysis of the Australian Economy and the Effects of Inflation Targeting," Working Papers 2019-10, University of Sydney, School of Economics, revised Nov 2019.
    10. 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.
    11. Pérez-Quirós, Gabriel & Poncela, Pilar & Camacho, Máximo, 2012. "Green Shoots and Double Dips in the Euro Area. A Real Time Measure," CEPR Discussion Papers 8896, C.E.P.R. Discussion Papers.
    12. Luke Hartigan & James Morley, 2018. "A Factor Model Analysis of the Effects on Inflation Targeting on the Australian Economy," RBA Annual Conference Volume (Discontinued), in: John Simon & Maxwell Sutton (ed.),Central Bank Frameworks: Evolution or Revolution?, Reserve Bank of Australia.
    13. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers halshs-02262202, HAL.
    14. Luke Hartigan, 2015. "Changes in the Factor Structure of the U.S. Economy: Permanent Breaks or Business Cycle Regimes?," Discussion Papers 2015-17, School of Economics, The University of New South Wales.
    15. Catherine Doz & Anna Petronevich, 2016. "Dating Business Cycle Turning Points for the French Economy: An MS-DFM approach," Advances in Econometrics, in: Dynamic Factor Models, volume 35, pages 481-538, Emerald Group Publishing Limited.
    16. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87.
    17. Fiorentini, Gabriele & Planas, Christophe & Rossi, Alessandro, 2016. "Skewness and kurtosis of multivariate Markov-switching processes," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 153-159.
    18. Máximo Camacho & Jaime Martínez-Martín, 2014. "Real-time forecasting us GDP from small-scale factor models," Working Papers 1425, Banco de España.
    19. Maximo Camacho & Gabriel Perez-Quiros & Pilar Poncela, 2012. "Markov-switching dynamic factor models in real time," Working Papers 1205, Banco de España.
    20. Gabriele Fiorentini & Enrique Sentana, 2013. "Dynamic Specification Tests for Dynamic Factor Models," Working Papers wp2013_1306, CEMFI.
    21. Juan Laborda & Sonia Ruano & Ignacio Zamanillo, 2023. "Multi-Country and Multi-Horizon GDP Forecasting Using Temporal Fusion Transformers," Mathematics, MDPI, vol. 11(12), pages 1-26, June.
    22. Eraslan, Sercan & Nöller, Marvin, 2020. "Recession probabilities falling from the STARs," Discussion Papers 08/2020, Deutsche Bundesbank.
    23. Carstensen, Kai & Heinrich, Markus & Reif, Magnus & Wolters, Maik H., 2020. "Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model An application to the German business cycle," Munich Reprints in Economics 84736, University of Munich, Department of Economics.
    24. Fossati, Sebastian, 2017. "Testing for State-Dependent Predictive Ability," Working Papers 2017-9, University of Alberta, Department of Economics.
    25. James Morley, 2018. "The Econometric Analysis of Recurrent Events in Macroeconomics and Finance," The Economic Record, The Economic Society of Australia, vol. 94(306), pages 338-340, September.

  15. Maximo Camacho & Gabriel Perez-Quiros & Pilar Poncela, 2010. "Green shoots in the euro area. A real time measure," Working Papers 1026, Banco de España.

    Cited by:

    1. Maria Gadea & Ana Gómez-Loscos & Antonio Montañés, 2012. "Cycles inside cycles: Spanish regional aggregation," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 3(4), pages 423-456, December.
    2. Leiva-Leon Danilo, 2014. "Real vs. nominal cycles: a multistate Markov-switching bi-factor approach," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 18(5), pages 557-580, December.
    3. Pami Dua, 2023. "Macroeconomic Modelling and Bayesian Methods," Springer Books, in: Pami Dua (ed.), Macroeconometric Methods, chapter 0, pages 19-37, Springer.
    4. Mendoza, Liu & Morales, Daniel, 2013. "Construyendo un índice coincidente de recesión: Una aplicación para la economía peruana," Revista Estudios Económicos, Banco Central de Reserva del Perú, issue 26, pages 81-100.
    5. Mendoza, Liu & Morales, Daniel, 2012. "Constructing a real-time coincident recession index: an application to the Peruvian economy," Working Papers 2012-020, Banco Central de Reserva del Perú.
    6. James D. Hamilton, 2010. "Calling Recessions in Real Time," NBER Working Papers 16162, National Bureau of Economic Research, Inc.
    7. Ángel Cuevas & Enrique Quilis, 2012. "A factor analysis for the Spanish economy," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 3(3), pages 311-338, September.

  16. Poncela, Pilar & Senra, Eva, 2002. "Forecasting monthly us consumer price indexes through a disaggregated I(2) analysis," DES - Working Papers. Statistics and Econometrics. WS ws020301, Universidad Carlos III de Madrid. Departamento de Estadística.

    Cited by:

    1. Igor Živko & Mile Bošnjak, 2017. "Time Series Modeling of Inflation and its Volatility in Croatia," Notitia - journal for economic, business and social issues, Notitia Ltd., vol. 1(3), pages 1-10, December.
    2. Janine Aron & John Muellbauer, 2008. "New methods for forecasting inflation and its sub-components: application to the USA," Economics Series Working Papers 406, University of Oxford, Department of Economics.
    3. Ellis W. Tallman & Saeed Zaman, 2015. "Forecasting Inflation: Phillips Curve Effects on Services Price Measures," Working Papers (Old Series) 1519, Federal Reserve Bank of Cleveland.
    4. Aron, Janine & Muellbauer, John, 2012. "Improving forecasting in an emerging economy, South Africa: Changing trends, long run restrictions and disaggregation," International Journal of Forecasting, Elsevier, vol. 28(2), pages 456-476.
    5. Janine Aron & John Muellbauer & Coen Pretorius, 2004. "A Framework for Forecasting the Components of the Consumer Price," Development and Comp Systems 0409054, University Library of Munich, Germany.
    6. Muellbauer, John & Aron, Janine, 2010. "Does aggregating forecasts by CPI component improve inflation forecast accuracy in South Africa?," CEPR Discussion Papers 7895, C.E.P.R. Discussion Papers.

  17. Poncela, Pilar, 2000. "Forecasting with nostationary dynamic factor models," DES - Working Papers. Statistics and Econometrics. WS 9959, Universidad Carlos III de Madrid. Departamento de Estadística.

    Cited by:

    1. Jennifer Castle & David Hendry & Michael P. Clements, 2014. "Robust Approaches to Forecasting," Economics Series Working Papers 697, University of Oxford, Department of Economics.
    2. Poncela, Pilar, 2012. "More is not always better : back to the Kalman filter in dynamic factor models," DES - Working Papers. Statistics and Econometrics. WS ws122317, Universidad Carlos III de Madrid. Departamento de Estadística.
    3. Francisco Corona & Graciela González-Farías & Pedro Orraca, 2017. "A dynamic factor model for the Mexican economy: are common trends useful when predicting economic activity?," Latin American Economic Review, Springer;Centro de Investigaciòn y Docencia Económica (CIDE), vol. 26(1), pages 1-35, December.
    4. García-Martos, Carolina & Rodríguez, Julio & Sánchez, María Jesús, 2008. "Seasonal dynamic factor analysis and bootstrap inference : application to electricity market forecasting," DES - Working Papers. Statistics and Econometrics. WS ws081406, Universidad Carlos III de Madrid. Departamento de Estadística.
    5. Bastos, Guadalupe & García-Martos, Carolina, 2017. "Electricity prices forecasting by averaging dynamic factor models," DES - Working Papers. Statistics and Econometrics. WS 24028, Universidad Carlos III de Madrid. Departamento de Estadística.
    6. Ortega, Jose Antonio & Poncela, Pilar, 2005. "Joint forecasts of Southern European fertility rates with non-stationary dynamic factor models," International Journal of Forecasting, Elsevier, vol. 21(3), pages 539-550.
    7. Forni, Mario & Giovannelli, Alessandro & Lippi, Marco & Soccorsi, Stefano, 2016. "Dynamic Factor model with infinite dimensional factor space: forecasting," CEPR Discussion Papers 11161, C.E.P.R. Discussion Papers.
    8. Andrés M. Alonso & Guadalupe Bastos & Carolina García-Martos, 2016. "Electricity Price Forecasting by Averaging Dynamic Factor Models," Energies, MDPI, vol. 9(8), pages 1-21, July.
    9. Morten {O}rregaard Nielsen & Won-Ki Seo & Dakyung Seong, 2023. "Inference on common trends in functional time series," Papers 2312.00590, arXiv.org, revised May 2024.
    10. Wei-Chun Hsu & Lin Lin & Chen-Yu Li, 2014. "Forecasting automobile sales: the Peña-Box approach," Transportation Planning and Technology, Taylor & Francis Journals, vol. 37(6), pages 568-580, August.
    11. Tu, Yundong & Yao, Qiwei & Zhang, Rongmao, 2020. "Error-correction factor models for high-dimensional cointegrated time series," LSE Research Online Documents on Economics 106994, London School of Economics and Political Science, LSE Library.
    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. Stavros Degiannakis, 2023. "The D-model for GDP nowcasting," Working Papers 317, Bank of Greece.
    14. Jennifer Castle & David Hendry, 2012. "Forecasting by factors, by variables, or both?," Economics Series Working Papers 600, University of Oxford, Department of Economics.
    15. Pedro Galeano & Daniel Peña, 2019. "Data science, big data and statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 289-329, June.
    16. Castle, Jennifer L. & Clements, Michael P. & Hendry, David F., 2013. "Forecasting by factors, by variables, by both or neither?," Journal of Econometrics, Elsevier, vol. 177(2), pages 305-319.
    17. Hiroaki Chigira & Taku Yamamoto, 2006. "Forcasting in large cointegrated processes," Hi-Stat Discussion Paper Series d06-169, Institute of Economic Research, Hitotsubashi University.
    18. Korobilis, Dimitris, 2018. "Machine Learning Macroeconometrics A Primer," Essex Finance Centre Working Papers 22666, University of Essex, Essex Business School.
    19. Poncela, Pilar & Rodríguez, Julio & Sánchez-Mangas, Rocío & Senra, Eva, 2011. "Forecast combination through dimension reduction techniques," International Journal of Forecasting, Elsevier, vol. 27(2), pages 224-237, April.
    20. Rodríguez, Julio, 2008. "A methodology for population projections: an application to Spain," DES - Working Papers. Statistics and Econometrics. WS ws084512, Universidad Carlos III de Madrid. Departamento de Estadística.
    21. Gianna Figá-Talamanca & Sergio Focardi & Marco Patacca, 2021. "Common dynamic factors for cryptocurrencies and multiple pair-trading statistical arbitrages," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(2), pages 863-882, December.
    22. 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.
    23. Corona, Francisco & Orraca, Pedro, 2016. "Remittances in Mexico and their unobserved components," DES - Working Papers. Statistics and Econometrics. WS 22674, Universidad Carlos III de Madrid. Departamento de Estadística.
    24. García-Martos, Carolina & Rodríguez, Julio & Sánchez, María Jesús, 2011. "Forecasting electricity prices and their volatilities using Unobserved Components," Energy Economics, Elsevier, vol. 33(6), pages 1227-1239.
    25. Ángel Cuevas & Enrique Quilis, 2012. "A factor analysis for the Spanish economy," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 3(3), pages 311-338, September.
    26. M. Pilar Muñoz & Cristina Corchero & F.-Javier Heredia, 2013. "Improving Electricity Market Price Forecasting with Factor Models for the Optimal Generation Bid," International Statistical Review, International Statistical Institute, vol. 81(2), pages 289-306, August.
    27. Clements, Michael P., 2016. "Real-time factor model forecasting and the effects of instability," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 661-675.
    28. Schanne, Norbert, 2015. "A Global Vector Autoregression (GVAR) model for regional labour markets and its forecasting performance with leading indicators in Germany," IAB-Discussion Paper 201513, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    29. John Galbraith & Greg Tkacz, 2007. "How Far Can Forecasting Models Forecast? Forecast Content Horizons for Some Important Macroeconomic Variables," Staff Working Papers 07-1, Bank of Canada.
    30. Kosei Fukuda, 2011. "Cointegration rank switching model: an application to forecasting interest rates," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(5), pages 509-522, August.

  18. Poncela, Pilar, 1997. "Eigenstructure of nonstationary factor models," DES - Working Papers. Statistics and Econometrics. WS 6224, Universidad Carlos III de Madrid. Departamento de Estadística.

    Cited by:

    1. Matteo Barigozzi & Matteo Luciani, 2017. "Common Factors, Trends, and Cycles in Large Datasets," Finance and Economics Discussion Series 2017-111, Board of Governors of the Federal Reserve System (U.S.).

  19. Guerrero, Victor M. & Juárez, Rodrigo & Poncela, Pilar, 1997. "Data graduation based on statistical time series methods," DES - Working Papers. Statistics and Econometrics. WS 6213, Universidad Carlos III de Madrid. Departamento de Estadística.

    Cited by:

    1. Victor M. Guerrero, 2008. "Estimating Trends with Percentage of Smoothness Chosen by the User," International Statistical Review, International Statistical Institute, vol. 76(2), pages 187-202, August.
    2. Guerrero, Victor M., 2007. "Time series smoothing by penalized least squares," Statistics & Probability Letters, Elsevier, vol. 77(12), pages 1225-1234, July.
    3. Cortez, Willy Walter & Islas-Camargo, Alejandro, 2009. "How Correlated are Mexico’s Salaries and Us Output? an Inquiry on Some Us Border Cities," Panorama Económico, Escuela Superior de Economía, Instituto Politécnico Nacional, vol. 0(08), pages 35-62, primer se.

Articles

  1. Bógalo, Juan & Llada, Martín & Poncela, Pilar & Senra, Eva, 2022. "Seasonality in COVID-19 times," Economics Letters, Elsevier, vol. 211(C).

    Cited by:

    1. Lahiri, Kajal & Yin, Yimeng, 2024. "Seasonality in U.S. disability applications, labor market, and the pandemic echoes," Labour Economics, Elsevier, vol. 87(C).
    2. William M. Doerner & Wenzhen Lin, 2022. "Applying Seasonal Adjustments to Housing Markets," FHFA Staff Working Papers 22-03, Federal Housing Finance Agency.

  2. 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.
    See citations under working paper version above.
  3. Marta Poncela-Blanco & Pilar Poncela, 2021. "Improving Wind Power Forecasts: Combination through Multivariate Dimension Reduction Techniques," Energies, MDPI, vol. 14(5), pages 1-16, March.

    Cited by:

    1. Imed Khabbouchi & Dhaou Said & Aziz Oukaira & Idir Mellal & Lyes Khoukhi, 2023. "Machine Learning and Game-Theoretic Model for Advanced Wind Energy Management Protocol (AWEMP)," Energies, MDPI, vol. 16(5), pages 1-15, February.
    2. Chia-Hung Wang & Qigen Zhao & Rong Tian, 2023. "Short-Term Wind Power Prediction Based on a Hybrid Markov-Based PSO-BP Neural Network," Energies, MDPI, vol. 16(11), pages 1-24, May.
    3. Nikodinoska, Dragana & Käso, Mathias & Müsgens, Felix, 2022. "Solar and wind power generation forecasts using elastic net in time-varying forecast combinations," Applied Energy, Elsevier, vol. 306(PA).
    4. Carneiro, Tatiane C. & Rocha, Paulo A.C. & Carvalho, Paulo C.M. & Fernández-Ramírez, Luis M., 2022. "Ridge regression ensemble of machine learning models applied to solar and wind forecasting in Brazil and Spain," Applied Energy, Elsevier, vol. 314(C).

  4. Jorge Caiado & Nuno Crato & Pilar Poncela, 2020. "A fragmented-periodogram approach for clustering big data time series," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(1), pages 117-146, March.

    Cited by:

    1. Raffaele Mattera & Philipp Otto, 2023. "Network log-ARCH models for forecasting stock market volatility," Papers 2303.11064, arXiv.org.
    2. Lúcio, Francisco & Caiado, Jorge, 2022. "COVID-19 and Stock Market Volatility: A Clustering Approach for S&P 500 Industry Indices," Finance Research Letters, Elsevier, vol. 49(C).
    3. João A. Bastos & Jorge Caiado, 2021. "On the classification of financial data with domain agnostic features," Working Papers REM 2021/0185, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.

  5. Pilar Poncela & Eva Senra & Lya Paola Sierra, 2020. "Global vs Sectoral Factors and the Impact of the Financialization in Commodity Price Changes," Open Economies Review, Springer, vol. 31(4), pages 859-879, September.

    Cited by:

    1. 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.

  6. Francisco Corona & Pilar Poncela & Esther Ruiz, 2020. "Estimating Non-stationary Common Factors: Implications for Risk Sharing," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 37-60, January.
    See citations under working paper version above.
  7. Poncela, Pilar & Nardo, Michela & Pericoli, Filippo M., 2019. "A Review of International Risk Sharing for Policy Analysis," East Asian Economic Review, Korea Institute for International Economic Policy, vol. 23(3), pages 227-260, September.

    Cited by:

    1. Nardo, M. & Ossola, E. & Papanagiotou, E., 2022. "Financial integration in the EU28 equity markets: Measures and drivers," Journal of Financial Markets, Elsevier, vol. 57(C).
    2. Alcidi, Cinzia & D’Imperio, Paolo & Thirion, Gilles, 2023. "Risk-sharing and consumption-smoothing patterns in the US and the Euro Area: A comprehensive comparison," Structural Change and Economic Dynamics, Elsevier, vol. 64(C), pages 58-69.
    3. Ojea-Ferreiro, Javier & Reboredo, Juan C., 2022. "Exchange rates and the global transmission of equity market shocks," Economic Modelling, Elsevier, vol. 114(C).
    4. Pasquale Foresti & Oreste Napolitano, 2022. "Risk Sharing in the EMU: A Time‐Varying Perspective," Journal of Common Market Studies, Wiley Blackwell, vol. 60(2), pages 319-336, March.
    5. Martín Fuentes, Natalia & Born, Alexandra & Bremus, Franziska & Kastelein, Wieger & Lambert, Claudia, 2023. "A deep dive into the capital channel of risk sharing in the euro area," Working Paper Series 2864, European Central Bank.

  8. Camacho, Maximo & Perez-Quiros, Gabriel & Poncela, Pilar, 2018. "Markov-switching dynamic factor models in real time," International Journal of Forecasting, Elsevier, vol. 34(4), pages 598-611.
    See citations under working paper version above.
  9. Pilar Poncela & Eva Senra, 2017. "Measuring uncertainty and assessing its predictive power in the euro area," Empirical Economics, Springer, vol. 53(1), pages 165-182, August.

    Cited by:

    1. Oscar Claveria, 2021. "Uncertainty indicators based on expectations of business and consumer surveys," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 48(2), pages 483-505, May.
    2. Oscar Claveria, 2021. "On the Aggregation of Survey-Based Economic Uncertainty Indicators Between Different Agents and Across Variables," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 17(1), pages 1-26, April.
    3. Glas, Alexander, 2020. "Five dimensions of the uncertainty–disagreement linkage," International Journal of Forecasting, Elsevier, vol. 36(2), pages 607-627.
    4. Michael Clements & Robert W. Rich & Joseph Tracy, 2024. "An Investigation into the Uncertainty Revision Process of Professional Forecasters," Working Papers 24-19, Federal Reserve Bank of Cleveland.
    5. Frederik Kunze, 2020. "Predicting exchange rates in Asia: New insights on the accuracy of survey forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 313-333, March.
    6. Antoni Espasa & Eva Senra, 2017. "Twenty-Two Years of Inflation Assessment and Forecasting Experience at the Bulletin of EU & US Inflation and Macroeconomic Analysis," Econometrics, MDPI, vol. 5(4), pages 1-28, October.
    7. Suardi, Sandy & Rasel, Atiqur Rahman & Liu, Bin, 2022. "On the predictive power of tweet sentiments and attention on bitcoin," International Review of Economics & Finance, Elsevier, vol. 79(C), pages 289-301.

  10. César Castro & Rebeca Jiménez-Rodríguez & Pilar Poncela & Eva Senra, 2017. "A new look at oil price pass-through into inflation: evidence from disaggregated European data," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 34(1), pages 55-82, April.

    Cited by:

    1. Tersoo Shimonkabir Shitile & Nuruddeen Usman, 2020. "Disaggregated Inflation and Asymmetric Oil Price Pass-Through in Nigeria," International Journal of Energy Economics and Policy, Econjournals, vol. 10(1), pages 255-264.
    2. Jassim Aladwani, 2024. "Oil Volatility Uncertainty: Impact on Fundamental Macroeconomics and the Stock Index," Economies, MDPI, vol. 12(6), pages 1-24, June.
    3. Ligia Topan & César Castro & Miguel Jerez & Andrés Barge-Gil, 2020. "Oil price pass-through into inflation in Spain at national and regional level," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 11(4), pages 561-583, December.
    4. İbrahim Özmen & Şerife Özşahin, 2023. "Effects of global energy and price fluctuations on Turkey's inflation: new evidence," Economic Change and Restructuring, Springer, vol. 56(4), pages 2695-2728, August.
    5. Abdurrahman Nazif Çatik & Mehmet Karaçuka & A. Özlem Önder, 2022. "The Time-Varying Impact of External Shocks on the Consumer Price Components: Evidence from an Emerging Market," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 20(4), pages 781-807, December.
    6. Siok Kun Sek & KivanÇ Halil AriÇ & Jenq Fei Chu, 2019. "Oil Price Pass-through on Domestic Inflation: Oil Importing Versus Oil Exporting Countries," Journal of Reviews on Global Economics, Lifescience Global, vol. 8, pages 604-610.
    7. Rebeca Jiménez-Rodríguez & Amalia Morales-Zumaquero, 2022. "Commodity price pass-through along the pricing chain," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 158(1), pages 109-125, February.
    8. Pradeep, Siddhartha, 2022. "Impact of diesel price reforms on asymmetricity of oil price pass-through to inflation: Indian perspective," The Journal of Economic Asymmetries, Elsevier, vol. 26(C).
    9. Dipesh Karki & Hari Gopal Risal, 2019. "Asymmetric Impact of Oil Price on Inflation: Evidence from Nepal," NRB Economic Review, Nepal Rastra Bank, Economic Research Department, vol. 31(1), pages 21-46, April.
    10. Jesus Cuauhtemoc Tellez Gaytan & Aqila Rafiuddin & Gyanendra Singh Sisodia & Gouher Ahmed & CH Paramaiah, 2023. "Pass-through Effects of Oil Prices on LATAM Emerging Stocks before and during COVID-19: An Evidence from a Wavelet -VAR Analysis," International Journal of Energy Economics and Policy, Econjournals, vol. 13(1), pages 529-543, January.

  11. 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.
    See citations under working paper version above.
  12. Pilar Poncela & Eva Senra & Lya Paola Sierra, 2017. "Long-term links between raw materials prices, real exchange rate and relative de-industrialization in a commodity-dependent economy: empirical evidence of “Dutch disease” in Colombia," Empirical Economics, Springer, vol. 52(2), pages 777-798, March.

    Cited by:

    1. Famil Majidli, 2022. "The Effects of Oil Prices and Oil Production on Non-Oil Exports in an Oil-Rich Country: The Case of Dutch Disease Symptom in Azerbaijan," International Journal of Energy Economics and Policy, Econjournals, vol. 12(3), pages 32-40, May.
    2. José Tomás Peláez S. & Lya Paola Sierra S., 2016. "Does Industrial Employment React to Movements in the Real Exchange Rate? An Empirical Analysis for Colombia, 2000-2010," Latin American Journal of Economics-formerly Cuadernos de Economía, Instituto de Economía. Pontificia Universidad Católica de Chile., vol. 53(1), pages 39-60, December.
    3. Oviedo Gómez, Andrés Felipe & Sierra, Lya Paola, 2019. "The importance of terms of trade in the Colombian economy," Revista CEPAL, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL), August.
    4. Benedictow, Andreas & Hammersland, Roger, 2023. "Transition risk of a petroleum currency," Economic Modelling, Elsevier, vol. 128(C).
    5. Juan Manuel Candelo-Viafara & Andrés Oviedo-Gómez, 2021. "La tasa de cambio y sus impactos en los agregados económicos colombianos: una aproximación FAVAR," Revista Facultad de Ciencias Económicas, Universidad Militar Nueva Granada, vol. 29(2), pages 121-142, October.
    6. Oviedo Gómez, Andrés Felipe & Sierra, Lya Paola, 2019. "Importancia de los términos de intercambio en la economía colombiana," Revista CEPAL, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL), August.
    7. Alejandro Torres García & Thomas Goda & Santiago Sanchez Gonzalez & Adriana Romero Villanueva, 2017. "Efectos diferenciales de la tasa de cambio real sobre el comercio internacional en Colombia," Documentos de Trabajo de Valor Público 15662, Universidad EAFIT.
    8. Rashesh Shrestha & Ian Coxhead, 2018. "Can Indonesia Secure a Development Dividend from Its Resource Export Boom?," Bulletin of Indonesian Economic Studies, Taylor & Francis Journals, vol. 54(1), pages 1-24, January.
    9. Juan Manuel Julio-Román & Fredy Gamboa-Estrada, 2019. "The Exchange Rate and Oil Prices in Colombia: A High Frequency Analysis," Borradores de Economia 1091, Banco de la Republica de Colombia.
    10. Chang, Kuei-Feng & Lin, Jin-Xu & Lin, Shih-Mo, 2021. "Revisiting the Dutch disease thesis from the perspective of value-added trade," Resources Policy, Elsevier, vol. 72(C).

  13. Martínez, Wilmer & Nieto, Fabio H. & Poncela, Pilar, 2016. "Choosing a dynamic common factor as a coincident index," Statistics & Probability Letters, Elsevier, vol. 109(C), pages 89-98.

    Cited by:

    1. 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.
    2. Rahman, Abdul & Khan, Muhammad Arshad & Charfeddine, Lanouar, 2021. "Regime-specific impact of financial reforms on economic growth in Pakistan," Journal of Policy Modeling, Elsevier, vol. 43(1), pages 161-182.

  14. Julieta Fuentes & Pilar Poncela & Julio Rodríguez, 2015. "Sparse Partial Least Squares in Time Series for Macroeconomic Forecasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(4), pages 576-595, June.
    See citations under working paper version above.
  15. Maximo Camacho & Gabriel Perez‐Quiros & Pilar Poncela, 2015. "Extracting Nonlinear Signals from Several Economic Indicators," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(7), pages 1073-1089, November.
    See citations under working paper version above.
  16. Pilar Poncela & Antonio García‐Ferrer, 2014. "The Effects of Disaggregation on Forecasting Nonstationary Time Series," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(4), pages 300-314, July.

    Cited by:

    1. Katja Drechsel & Dr. Rolf Scheufele, 2012. "Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment," Working Papers 2012-16, Swiss National Bank.
    2. 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.
    3. Proietti, Tommaso & Giovannelli, Alessandro & Ricchi, Ottavio & Citton, Ambra & Tegami, Christían & Tinti, Cristina, 2021. "Nowcasting GDP and its components in a data-rich environment: The merits of the indirect approach," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1376-1398.

  17. Pilar Poncela & Eva Senra & Lya Paola Sierra, 2014. "Common dynamics of nonenergy commodity prices and their relation to uncertainty," Applied Economics, Taylor & Francis Journals, vol. 46(30), pages 3724-3735, October.

    Cited by:

    1. Chiara Casoli & Riccardo (Jack) Lucchetti, 2022. "Permanent-Transitory decomposition of cointegrated time series via dynamic factor models, with an application to commodity prices [Commodity-price comovement and global economic activity]," The Econometrics Journal, Royal Economic Society, vol. 25(2), pages 494-514.
    2. Sipan Aslan & Ceylan Yozgatligil & Cem Iyigun, 2018. "Temporal clustering of time series via threshold autoregressive models: application to commodity prices," Annals of Operations Research, Springer, vol. 260(1), pages 51-77, January.
    3. Hedi Ben Haddad & Imed Mezghani & Abdessalem Gouider, 2021. "The Dynamic Spillover Effects of Macroeconomic and Financial Uncertainty on Commodity Markets Uncertainties," Economies, MDPI, vol. 9(2), pages 1-22, June.
    4. Muhammad Abubakr Naeem & Saqib Farid & Safwan Mohd Nor & Syed Jawad Hussain Shahzad, 2021. "Spillover and Drivers of Uncertainty among Oil and Commodity Markets," Mathematics, MDPI, vol. 9(4), pages 1-26, February.
    5. 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.
    6. Pavel Kotyza & Katarzyna Czech & Michał Wielechowski & Luboš Smutka & Petr Procházka, 2021. "Sugar Prices vs. Financial Market Uncertainty in the Time of Crisis: Does COVID-19 Induce Structural Changes in the Relationship?," Agriculture, MDPI, vol. 11(2), pages 1-16, January.
    7. Liao, Wenting & Ma, Jun & Zhang, Chengsi, 2024. "Commodity returns co-movement, uncertainty shocks, and the US dollar exchange rate," Journal of International Money and Finance, Elsevier, vol. 143(C).
    8. Allayioti, Anastasia & Venditti, Fabrizio, 2024. "The role of comovement and time-varying dynamics in forecasting commodity prices," Working Paper Series 2901, European Central Bank.
    9. Pavel Vidal Alejandro & Lya Paola Sierra Suárez & Johana Sanabria Dominguez & Jaime Andres Collazos Rodríguez, 2015. "Indicador mensual de actividad económica (IMAE) para el Valle del Cauca," Borradores de Economia 900, Banco de la Republica de Colombia.
    10. Raza, Syed Ali & Masood, Amna & Benkraiem, Ramzi & Urom, Christian, 2023. "Forecasting the volatility of precious metals prices with global economic policy uncertainty in pre and during the COVID-19 period: Novel evidence from the GARCH-MIDAS approach," Energy Economics, Elsevier, vol. 120(C).
    11. Mokni, Khaled & Al-Shboul, Mohammed & Assaf, Ata, 2021. "Economic policy uncertainty and dynamic spillover among precious metals under market conditions: Does COVID-19 have any effects?," Resources Policy, Elsevier, vol. 74(C).
    12. Joseph P Byrne & Ryuta Sakemoto & Bing Xu, 2020. "Commodity price co-movement: heterogeneity and the time-varying impact of fundamentals [Oil price shocks and the stock market: evidence from Japan]," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 47(2), pages 499-528.
    13. Fabian Lutzenberger & Benedikt Gleich & Herbert G. Mayer & Christian Stepanek & Andreas W. Rathgeber, 2017. "Metals: resources or financial assets? A multivariate cross-sectional analysis," Empirical Economics, Springer, vol. 53(3), pages 927-958, November.
    14. Kakade, Kshitij Abhay & Mishra, Aswini Kumar, 2021. "The impact of macroeconomic and oil shocks on India’s non-ferrous metal prices: A structural-VAR approach," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 63, pages 30-50.
    15. Bernardina Algieri, 2021. "Fast & furious: Do psychological and legal factors affect commodity price volatility?," The World Economy, Wiley Blackwell, vol. 44(4), pages 980-1017, April.
    16. Lya Paola Sierra Suárez & Jaime Andrés Collazos-Rodríguez & Johana Sanabria-Domínguez & Pavel Vidal-Alejandro, 2017. "La construcción de indicadores de la actividad económica: una revisión bibliográfica," Apuntes del Cenes, Universidad Pedagógica y Tecnológica de Colombia, vol. 36(64), pages 79-107, October.
    17. Mensi, Walid & Naeem, Muhammad Abubakr & Vo, Xuan Vinh & Kang, Sang Hoon, 2022. "Dynamic and frequency spillovers between green bonds, oil and G7 stock markets: Implications for risk management," Economic Analysis and Policy, Elsevier, vol. 73(C), pages 331-344.
    18. Md Rafayet Alam & Scott Gilbert, 2017. "Monetary policy shocks and the dynamics of agricultural commodity prices: evidence from structural and factor†augmented VAR analyses," Agricultural Economics, International Association of Agricultural Economists, vol. 48(1), pages 15-27, January.
    19. Sierra Lya Paola & Girón Luis Eduardo & Girón Victor & Girón Andrés, 2018. "What is the Spillover Effect of the U.S. Equity and Money Market on the Key Latin American Agricultural Exports?," Global Economy Journal, De Gruyter, vol. 18(4), pages 1-9, December.
    20. Srivastava, Mrinalini & Rao, Amar & Parihar, Jaya Singh & Chavriya, Shubham & Singh, Surendar, 2023. "What do the AI methods tell us about predicting price volatility of key natural resources: Evidence from hyperparameter tuning," Resources Policy, Elsevier, vol. 80(C).
    21. Lya Paola Sierra & Luis Eduardo Gir n & Carolina Osorio, 2017. "Has Financialization in Commodity Markets Affected the Predictability in Metal Markets? The Efficient Markets Hypotheses for Metal Returns," International Journal of Economics and Financial Issues, Econjournals, vol. 7(4), pages 15-22.

  18. Camacho, Maximo & Perez Quiros, Gabriel & Poncela, Pilar, 2014. "Green shoots and double dips in the euro area: A real time measure," International Journal of Forecasting, Elsevier, vol. 30(3), pages 520-535.
    See citations under working paper version above.
  19. Pilar Poncela & Eva Senra & Daniel Sotelsek & Guido Zack, 2014. "Some New Results on the Estimation of Structural Budget Balance for Spain," Hacienda Pública Española / Review of Public Economics, IEF, vol. 210(3), pages 11-31, September.

    Cited by:

    1. Francisco Martí & Javier J. Pérez, 2015. "Spanish Public Finances through the Financial Crisis," Fiscal Studies, Institute for Fiscal Studies, vol. 36, pages 527-554, December.
    2. Achim TRUGER & Michael NAGEL, 2016. "Austerity, Cyclical Adjustment and How to use the Remaining Leeway for Expansionary Fiscal Policies Within the Current EU Fiscal Framework," Turkish Economic Review, KSP Journals, vol. 3(2), pages 235-255, June.
    3. Jose Francisco Bellod Redondo, 2015. "Plan E: la estrategia keynesiana frente a la crisis en España," Revista de Economía Crítica, Asociación de Economía Crítica, vol. 20, pages 4-22.
    4. Truger, Achim, 2015. "Austerity, cyclical adjustment and the remaining leeway for expansionary fiscal policies within the current EU fiscal framework," IPE Working Papers 50/2015, Berlin School of Economics and Law, Institute for International Political Economy (IPE).

  20. Camacho, Maximo & Perez-Quiros, Gabriel & Poncela, Pilar, 2013. "Short-term Forecasting for Empirical Economists: A Survey of the Recently Proposed Algorithms," Foundations and Trends(R) in Econometrics, now publishers, vol. 6(2), pages 101-161, November.
    See citations under working paper version above.
  21. Poncela, Marta & Poncela, Pilar & Perán, José Ramón, 2013. "Automatic tuning of Kalman filters by maximum likelihood methods for wind energy forecasting," Applied Energy, Elsevier, vol. 108(C), pages 349-362.

    Cited by:

    1. Nantian Huang & Enkai Xing & Guowei Cai & Zhiyong Yu & Bin Qi & Lin Lin, 2018. "Short-Term Wind Speed Forecasting Based on Low Redundancy Feature Selection," Energies, MDPI, vol. 11(7), pages 1-19, June.
    2. Yonggang Li & Yue Wang & Binyuan Wu, 2020. "Short-Term Direct Probability Prediction Model of Wind Power Based on Improved Natural Gradient Boosting," Energies, MDPI, vol. 13(18), pages 1-15, September.
    3. Heo, SungKu & Byun, Jaewon & Ifaei, Pouya & Ko, Jaerak & Ha, Byeongmin & Hwangbo, Soonho & Yoo, ChangKyoo, 2024. "Towards mega-scale decarbonized industrial park (Mega-DIP): Generative AI-driven techno-economic and environmental assessment of renewable and sustainable energy utilization in petrochemical industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    4. Nikodinoska, Dragana & Käso, Mathias & Müsgens, Felix, 2022. "Solar and wind power generation forecasts using elastic net in time-varying forecast combinations," Applied Energy, Elsevier, vol. 306(PA).
    5. Mostafa Farrokhabadi, 2019. "Data-Driven Mitigation of Energy Scheduling Inaccuracy in Renewable-Penetrated Grids: Summerside Electric Use Case," Energies, MDPI, vol. 12(12), pages 1-23, June.
    6. Cheng, William Y.Y. & Liu, Yubao & Bourgeois, Alfred J. & Wu, Yonghui & Haupt, Sue Ellen, 2017. "Short-term wind forecast of a data assimilation/weather forecasting system with wind turbine anemometer measurement assimilation," Renewable Energy, Elsevier, vol. 107(C), pages 340-351.
    7. Chinmoy, Lakshmi & Iniyan, S. & Goic, Ranko, 2019. "Modeling wind power investments, policies and social benefits for deregulated electricity market – A review," Applied Energy, Elsevier, vol. 242(C), pages 364-377.
    8. Zhao, Yongning & Ye, Lin & Li, Zhi & Song, Xuri & Lang, Yansheng & Su, Jian, 2016. "A novel bidirectional mechanism based on time series model for wind power forecasting," Applied Energy, Elsevier, vol. 177(C), pages 793-803.
    9. Marta Poncela-Blanco & Pilar Poncela, 2021. "Improving Wind Power Forecasts: Combination through Multivariate Dimension Reduction Techniques," Energies, MDPI, vol. 14(5), pages 1-16, March.
    10. Duan, Jikai & Chang, Mingheng & Chen, Xiangyue & Wang, Wenpeng & Zuo, Hongchao & Bai, Yulong & Chen, Bolong, 2022. "A combined short-term wind speed forecasting model based on CNN–RNN and linear regression optimization considering error," Renewable Energy, Elsevier, vol. 200(C), pages 788-808.
    11. Vincenzo Loia & Stefania Tomasiello & Alfredo Vaccaro & Jinwu Gao, 2020. "Using local learning with fuzzy transform: application to short term forecasting problems," Fuzzy Optimization and Decision Making, Springer, vol. 19(1), pages 13-32, March.
    12. Wang, Qin & Wu, Hongyu & Florita, Anthony R. & Brancucci Martinez-Anido, Carlo & Hodge, Bri-Mathias, 2016. "The value of improved wind power forecasting: Grid flexibility quantification, ramp capability analysis, and impacts of electricity market operation timescales," Applied Energy, Elsevier, vol. 184(C), pages 696-713.
    13. Wang, Yun & Xu, Houhua & Zou, Runmin & Zhang, Lingjun & Zhang, Fan, 2022. "A deep asymmetric Laplace neural network for deterministic and probabilistic wind power forecasting," Renewable Energy, Elsevier, vol. 196(C), pages 497-517.
    14. Feijóo, Andrés & Villanueva, Daniel, 2016. "Assessing wind speed simulation methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 473-483.
    15. Lydia, M. & Suresh Kumar, S. & Immanuel Selvakumar, A. & Edwin Prem Kumar, G., 2015. "Wind resource estimation using wind speed and power curve models," Renewable Energy, Elsevier, vol. 83(C), pages 425-434.
    16. Carvalho, D. & Rocha, A. & Gómez-Gesteira, M. & Silva Santos, C., 2014. "Sensitivity of the WRF model wind simulation and wind energy production estimates to planetary boundary layer parameterizations for onshore and offshore areas in the Iberian Peninsula," Applied Energy, Elsevier, vol. 135(C), pages 234-246.
    17. Feng, Cong & Cui, Mingjian & Hodge, Bri-Mathias & Zhang, Jie, 2017. "A data-driven multi-model methodology with deep feature selection for short-term wind forecasting," Applied Energy, Elsevier, vol. 190(C), pages 1245-1257.
    18. Zuluaga, Carlos D. & Álvarez, Mauricio A. & Giraldo, Eduardo, 2015. "Short-term wind speed prediction based on robust Kalman filtering: An experimental comparison," Applied Energy, Elsevier, vol. 156(C), pages 321-330.

  22. Poncela, Pilar & Rodríguez, Julio & Sánchez-Mangas, Rocío & Senra, Eva, 2011. "Forecast combination through dimension reduction techniques," International Journal of Forecasting, Elsevier, vol. 27(2), pages 224-237.

    Cited by:

    1. Constantin Bürgi, 2023. "How to Deal With Missing Observations in Surveys of Professional Forecasters," CESifo Working Paper Series 10203, CESifo.
    2. Pablo Pincheira, 2012. "Are Forecast Combinations Efficient?," Working Papers Central Bank of Chile 661, Central Bank of Chile.
    3. Cristina Conflitti & Christine De Mol & Domenico Giannone, 2012. "Optimal Combination of Survey Forecasts," Working Papers ECARES ECARES 2012-023, ULB -- Universite Libre de Bruxelles.
    4. Bastos, Guadalupe & García-Martos, Carolina, 2017. "Electricity prices forecasting by averaging dynamic factor models," DES - Working Papers. Statistics and Econometrics. WS 24028, Universidad Carlos III de Madrid. Departamento de Estadística.
    5. Zhemkov, Michael, 2021. "Nowcasting Russian GDP using forecast combination approach," International Economics, Elsevier, vol. 168(C), pages 10-24.
    6. Bartosz Uniejewski & Katarzyna Maciejowska, 2022. "LASSO Principal Component Averaging -- a fully automated approach for point forecast pooling," Papers 2207.04794, arXiv.org.
    7. Qian, Yilin & Thompson, Ryan & Vasnev, Andrey L, 2022. "Global combinations of expert forecasts," Working Papers BAWP-2022-02, University of Sydney Business School, Discipline of Business Analytics.
    8. Andrés M. Alonso & Guadalupe Bastos & Carolina García-Martos, 2016. "Electricity Price Forecasting by Averaging Dynamic Factor Models," Energies, MDPI, vol. 9(8), pages 1-21, July.
    9. Ryan Thompson & Yilin Qian & Andrey L. Vasnev, 2022. "Flexible global forecast combinations," Papers 2207.07318, arXiv.org, revised Mar 2024.
    10. Katarzyna Maciejowska & Bartosz Uniejewski & Tomasz Serafin, 2020. "PCA Forecast Averaging—Predicting Day-Ahead and Intraday Electricity Prices," Energies, MDPI, vol. 13(14), pages 1-19, July.
    11. A.S.M. Arroyo & A. de Juan Fern¨¢ndez, 2014. "Split-then-Combine Method for out-of-sample Combinations of Forecasts," Journal of Business Administration Research, Journal of Business Administration Research, Sciedu Press, vol. 3(1), pages 19-37, April.
    12. Antonio Martin Arroyo & Aranzazu de Juan Fernandez, 2020. "Split-then-Combine simplex combination and selection of forecasters," Papers 2012.11935, arXiv.org.
    13. Constantin Bürgi & Tara M. Sinclair, 2015. "A Nonparametric Approach to Identifying a Subset of Forecasters that Outperforms the Simple Average," Working Papers 2015-006, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    14. Fuentes, Julieta & Poncela, Pilar & Rodríguez, Julio, 2014. "Selecting and combining experts from survey forecasts," DES - Working Papers. Statistics and Econometrics. WS ws140905, Universidad Carlos III de Madrid. Departamento de Estadística.
    15. Marta Poncela-Blanco & Pilar Poncela, 2021. "Improving Wind Power Forecasts: Combination through Multivariate Dimension Reduction Techniques," Energies, MDPI, vol. 14(5), pages 1-16, March.
    16. Anastasiia Pankratova, 2024. "Forecasting Key Macroeconomic Indicators Using DMA and DMS Methods," Russian Journal of Money and Finance, Bank of Russia, vol. 83(1), pages 32-52, March.
    17. MeiChi Huang, 2019. "A Nationwide or Localized Housing Crisis? Evidence from Structural Instability in US Housing Price and Volume Cycles," Computational Economics, Springer;Society for Computational Economics, vol. 53(4), pages 1547-1563, April.
    18. Esteban Fernández-Vázquez & Blanca Moreno, 2017. "Entropy Econometrics for combining regional economic forecasts: A Data-Weighted Prior Estimator," Journal of Geographical Systems, Springer, vol. 19(4), pages 349-370, October.
    19. Pablo Pincheira & Andrés Gatty, 2014. "Forecasting Chilean Inflation with International Factors," Working Papers Central Bank of Chile 723, Central Bank of Chile.
    20. Pilar Poncela & Eva Senra, 2017. "Measuring uncertainty and assessing its predictive power in the euro area," Empirical Economics, Springer, vol. 53(1), pages 165-182, August.
    21. Maciejowska, Katarzyna & Nowotarski, Jakub & Weron, Rafał, 2016. "Probabilistic forecasting of electricity spot prices using Factor Quantile Regression Averaging," International Journal of Forecasting, Elsevier, vol. 32(3), pages 957-965.
    22. Hutchinson, Mark C. & Kyziropoulos, Panagiotis E. & O'Brien, John & O'Reilly, Philip & Sharma, Tripti, 2022. "Are carry, momentum and value still there in currencies?," International Review of Financial Analysis, Elsevier, vol. 83(C).
    23. Pablo Pincheira-Brown & Andrea Bentancor & Nicolás Hardy, 2023. "An Inconvenient Truth about Forecast Combinations," Mathematics, MDPI, vol. 11(18), pages 1-24, September.
    24. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.
    25. Víctor López-Pérez, 2017. "Do professional forecasters behave as if they believed in the New Keynesian Phillips Curve for the euro area?," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 44(1), pages 147-174, February.

  23. Antonio García‐ferrer & Aránzazu De Juan & Pilar Poncela, 2007. "The relationship between road traffic accidents and real economic activity in spain: common cycles and health issues," Health Economics, John Wiley & Sons, Ltd., vol. 16(6), pages 603-626, June.

    Cited by:

    1. Castillo-Manzano, José I. & Castro-Nuño, Mercedes & Fageda, Xavier, 2015. "Are traffic violators criminals? Searching for answers in the experiences of European countries," Transport Policy, Elsevier, vol. 38(C), pages 86-94.
    2. Martínez-Gabaldón, Eduardo & Méndez Martínez, Ildefonso & Martínez-Pérez, Jorge Eduardo, 2020. "Estimating the impact of the Penalty Point System on road fatalities in Spain," Transport Policy, Elsevier, vol. 86(C), pages 1-8.
    3. Yoshitsugu Kitazawa, 2010. "Size of economic activity and occurrence of fatal traffic accidents: a count panel data analysis on Fukuoka prefecture in Japan," Discussion Papers 41, Kyushu Sangyo University, Faculty of Economics.
    4. Dadashova, Bahar & Ramírez Arenas, Blanca & McWilliams Mira, José & Izquierdo Aparicio, Francisco, 2014. "Explanatory and prediction power of two macro models. An application to van-involved accidents in Spain," Transport Policy, Elsevier, vol. 32(C), pages 203-217.
    5. Castro-Nuño, Mercedes & Arévalo-Quijada, M. Teresa, 2018. "Assessing urban road safety through multidimensional indexes: Application of multicriteria decision making analysis to rank the Spanish provinces," Transport Policy, Elsevier, vol. 68(C), pages 118-129.
    6. Harizi Riadh, 2021. "Land artificialization, economic growth, and road insecurity: Theoretical improvements and empirical validation for the case of Algeria," Technium Social Sciences Journal, Technium Science, vol. 18(1), pages 241-255, April.
    7. Mercedes Castro-Nuno & José I. Castillo-Manzano & Diego J. Pedregal-Tercero, 2013. "The Speed Limits Debate: Is Effective A Temporary Change? The Case Of Spain," ERSA conference papers ersa13p160, European Regional Science Association.
    8. José Castillo-Manzano & Mercedes Castro-Nuño & Xavier Fageda, 2014. "Can health public expenditure reduce the tragic consequences of road traffic accidents? The EU-27 experience," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 15(6), pages 645-652, July.
    9. Francisco Calvo-Poyo & José Navarro-Moreno & Juan de Oña, 2020. "Road Investment and Traffic Safety: An International Study," Sustainability, MDPI, vol. 12(16), pages 1-15, August.
    10. Castillo-Manzano, José I. & Castro-Nuño, Mercedes & Pedregal-Tercero, Diego J., 2014. "Temporary speed limit changes: An econometric estimation of the effects of the Spanish Energy Efficiency and Saving Plan," Economic Modelling, Elsevier, vol. 44(S1), pages 68-76.
    11. Castillo-Manzano, José I. & Castro-Nuño, Mercedes & López-Valpuesta, Lourdes & Pedregal, Diego J., 2019. "From legislation to compliance: The power of traffic law enforcement for the case study of Spain," Transport Policy, Elsevier, vol. 75(C), pages 1-9.
    12. Yueh-Tzu Lu & Mototsugu Fukushige, 2017. "Smeed fs Law and the Role of Hospitals in Modeling Fatalities and Traffic Accidents," Discussion Papers in Economics and Business 17-22, Osaka University, Graduate School of Economics.
    13. Yueh-Tzu Lu & Mototsugu Fukushige, 2019. "Smeed’s law and the role of hospitals in modeling traffic accidents and fatalities in Japan," Asia-Pacific Journal of Regional Science, Springer, vol. 3(2), pages 319-332, June.
    14. Daniel Albalate & Germa Bel, 2008. "Motorways, tolls and road safety.Evidence from European Panel Data," IREA Working Papers 200802, University of Barcelona, Research Institute of Applied Economics, revised Feb 2008.
    15. Biondi, Beatrice & Mazzocchi, Mario, 2024. "An empirical analysis of the effect of economic activity and COVID-19 restrictions on road traffic accidents in Italy," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).

  24. Garcia-Ferrer, A. & de Juan, A. & Poncela, P., 2006. "Forecasting traffic accidents using disaggregated data," International Journal of Forecasting, Elsevier, vol. 22(2), pages 203-222.

    Cited by:

    1. Bichen Wang & Peng Jing & Chengxi Jiang, 2023. "Combining SEM, fsQCA and BNs to Explore E-Bike Riders’ Helmet Wearing Intentions under the Impact of Mandatory Policies: An Empirical Study in Zhenjiang," Sustainability, MDPI, vol. 15(24), pages 1-25, December.
    2. Dadashova, Bahar & Ramírez Arenas, Blanca & McWilliams Mira, José & Izquierdo Aparicio, Francisco, 2014. "Explanatory and prediction power of two macro models. An application to van-involved accidents in Spain," Transport Policy, Elsevier, vol. 32(C), pages 203-217.
    3. A.S.M. Arroyo & A. de Juan Fern¨¢ndez, 2014. "Split-then-Combine Method for out-of-sample Combinations of Forecasts," Journal of Business Administration Research, Journal of Business Administration Research, Sciedu Press, vol. 3(1), pages 19-37, April.
    4. Green, Kesten C. & Armstrong, J. Scott, 2015. "Simple versus complex forecasting: The evidence," Journal of Business Research, Elsevier, vol. 68(8), pages 1678-1685.
    5. Trinh, Cong Tam & Nguyen, Xuan & Sgro, Pasquale & Pham, Cong S., 2020. "Culture, financial crisis and the demand for property, accident and health insurance in the OECD countries," Economic Modelling, Elsevier, vol. 93(C), pages 480-498.
    6. Trinh, Cong Tam & Chao, Chi-Chur & Ho, Nhut Quang, 2023. "Private health insurance consumption and public health-care provision in OECD countries: Impact of culture, finance, and the pandemic," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
    7. Antonio García‐ferrer & Aránzazu De Juan & Pilar Poncela, 2007. "The relationship between road traffic accidents and real economic activity in spain: common cycles and health issues," Health Economics, John Wiley & Sons, Ltd., vol. 16(6), pages 603-626, June.
    8. Sajjakaj Jomnonkwao & Savalee Uttra & Vatanavongs Ratanavaraha, 2020. "Forecasting Road Traffic Deaths in Thailand: Applications of Time-Series, Curve Estimation, Multiple Linear Regression, and Path Analysis Models," Sustainability, MDPI, vol. 12(1), pages 1-17, January.
    9. Jaume Rosselló Nadal & Óscar Saenz-de-Miera, 2009. "Road accidents and tourism: the case of the Balearic Islands (Spain)," CRE Working Papers (Documents de treball del CRE) 2009/4, Centre de Recerca Econòmica (UIB ·"Sa Nostra").
    10. Aparicio Izquierdo, Francisco & Arenas Ramírez, Blanca & Bernardos Rodríguez, Eva, 2013. "The interurban DRAG-Spain model: The main factors of influence on road accidents in Spain," Research in Transportation Economics, Elsevier, vol. 37(1), pages 57-65.

  25. Pilar Poncela & Eva Senra, 2006. "A two factor model to combine US inflation forecasts," Applied Economics, Taylor & Francis Journals, vol. 38(18), pages 2191-2197.

    Cited by:

    1. Christina Anderl & Guglielmo Maria Caporale, 2022. "Forecasting Inflation with a Zero Lower Bound or Negative Interest Rates: Evidence from Point and Density Forecasts," CESifo Working Paper Series 9687, CESifo.
    2. Poncela, Pilar & Rodríguez, Julio & Sánchez-Mangas, Rocío & Senra, Eva, 2011. "Forecast combination through dimension reduction techniques," International Journal of Forecasting, Elsevier, vol. 27(2), pages 224-237, April.
    3. Pilar Poncela & Eva Senra, 2017. "Measuring uncertainty and assessing its predictive power in the euro area," Empirical Economics, Springer, vol. 53(1), pages 165-182, August.
    4. Guerrero, Víctor & Islas C., Alejandro & Poncela, Pilar & Rodríguez, Julio & Sánchez-Mangas, Rocío, 2014. "Mexico: Combining monthly inflation predictions from surveys," Revista CEPAL, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL), August.

  26. Antonio García-Ferrer & Marcos Bujosa & Aránzazu de Juan & Pilar Poncela, 2006. "Demand Forecast and Elasticities Estimation of Public Transport," Journal of Transport Economics and Policy, University of Bath, vol. 40(1), pages 45-67, January.

    Cited by:

    1. Souche, Stéphanie, 2010. "Measuring the structural determinants of urban travel demand," Transport Policy, Elsevier, vol. 17(3), pages 127-134, May.
    2. Burguillo, Mercedes & Romero-Jordán, Desiderio & Sanz-Sanz, José Félix, 2017. "The new public transport pricing in Madrid Metropolitan Area: A welfare analysis," Research in Transportation Economics, Elsevier, vol. 62(C), pages 25-36.
    3. João M. Pinto & Mário Coutinho dos Santos & Pedro Verga Matos, 2021. "Contracting Out Public Transit Services: An Incentive Performance-Based Approach," Working Papers de Economia (Economics Working Papers) 02, Católica Porto Business School, Universidade Católica Portuguesa.
    4. Hörcher, Daniel & Tirachini, Alejandro, 2021. "A review of public transport economics," Economics of Transportation, Elsevier, vol. 25(C).
    5. Anna Matas, 2003. "Demand and revenue implications of an integrated public transport policy. The case of," Working Papers wpdea0304, Department of Applied Economics at Universitat Autonoma of Barcelona.
    6. A.S.M. Arroyo & A. de Juan Fern¨¢ndez, 2014. "Split-then-Combine Method for out-of-sample Combinations of Forecasts," Journal of Business Administration Research, Journal of Business Administration Research, Sciedu Press, vol. 3(1), pages 19-37, April.
    7. Milioti, Christina P. & Karlaftis, Matthew G., 2014. "Estimating multimodal public transport mode shares in Athens, Greece," Journal of Transport Geography, Elsevier, vol. 34(C), pages 88-95.
    8. Gkritza, Konstantina & Karlaftis, Matthew G. & Mannering, Fred L., 2011. "Estimating multimodal transit ridership with a varying fare structure," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(2), pages 148-160, February.
    9. Melo, Patricia C. & Sobreira, Nuno & Goulart, Pedro, 2019. "Estimating the long-run metro demand elasticities for Lisbon: A time-varying approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 126(C), pages 360-376.
    10. Hernández-Díaz, Alfredo G. & García Cobián, Emilio Carlos, 2014. "Elasticidad precio de la demanda y perfil de los usuarios de la parada “Pablo de Olavide" de Metro de Sevilla || Price Elasticity of Demand and Profile of “Pablo de Olavide" Metro Stop's Use," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 17(1), pages 80-100, June.
    11. Anna Matas, 2003. "Demand and Revenue Implications of an Integrated Public Transport Policy: The Case of Madrid," Transport Reviews, Taylor & Francis Journals, vol. 24(2), pages 195-217, May.
    12. Germa Bel & Daniel Albalate, 2009. "What shapes local public transportation in Europe? Economics, Mobility, Institutions, and Geography," RSCAS Working Papers 2009/34, European University Institute.
    13. Wang, Jiangbo & Yamamoto, Toshiyuki & Liu, Kai, 2021. "Spatial dependence and spillover effects in customized bus demand: Empirical evidence using spatial dynamic panel models," Transport Policy, Elsevier, vol. 105(C), pages 166-180.
    14. Youzhi Zeng & Bin Ran & Ning Zhang & Xiaobao Yang, 2021. "Estimating the Price Elasticity of Train Travel Demand and Its Variation Rules and Application in Energy Used and CO 2 Emissions," Sustainability, MDPI, vol. 13(2), pages 1-19, January.
    15. Michaelides, Panayotis G. & Konstantakis, Konstantinos N. & Milioti, Christina & Karlaftis, Matthew G., 2015. "Modelling spillover effects of public transportation means: An intra-modal GVAR approach for Athens," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 82(C), pages 1-18.
    16. ur Rehman, Naveed & Hijazi, Mohamad & Uzair, Muhammad, 2020. "Solar potential assessment of public bus routes for solar buses," Renewable Energy, Elsevier, vol. 156(C), pages 193-200.

  27. Ortega, Jose Antonio & Poncela, Pilar, 2005. "Joint forecasts of Southern European fertility rates with non-stationary dynamic factor models," International Journal of Forecasting, Elsevier, vol. 21(3), pages 539-550.

    Cited by:

    1. Booth, Heather, 2006. "Demographic forecasting: 1980 to 2005 in review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 547-581.
    2. Rob Hyndman & Heather Booth & Farah Yasmeen, 2013. "Coherent Mortality Forecasting: The Product-Ratio Method With Functional Time Series Models," Demography, Springer;Population Association of America (PAA), vol. 50(1), pages 261-283, February.
    3. García-Martos, Carolina & Rodríguez, Julio & Sánchez, María Jesús, 2008. "Seasonal dynamic factor analysis and bootstrap inference : application to electricity market forecasting," DES - Working Papers. Statistics and Econometrics. WS ws081406, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. 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.
    5. Rob J Hyndman & Heather Booth, 2006. "Stochastic population forecasts using functional data models for mortality, fertility and migration," Monash Econometrics and Business Statistics Working Papers 14/06, Monash University, Department of Econometrics and Business Statistics.
    6. Dordonnat, Virginie & Koopman, Siem Jan & Ooms, Marius, 2012. "Dynamic factors in periodic time-varying regressions with an application to hourly electricity load modelling," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3134-3152.
    7. Rodríguez, Julio, 2008. "A methodology for population projections: an application to Spain," DES - Working Papers. Statistics and Econometrics. WS ws084512, Universidad Carlos III de Madrid. Departamento de Estadística.
    8. García-Martos, Carolina & Rodríguez, Julio & Sánchez, María Jesús, 2011. "Forecasting electricity prices and their volatilities using Unobserved Components," Energy Economics, Elsevier, vol. 33(6), pages 1227-1239.

  28. Garcia-Ferrer, Antonio & De Gooijer, Jan G. & Poncela, Pilar & Ruiz, Esther, 2005. "Introduction to nonlinearities, business cycles, and forecasting," International Journal of Forecasting, Elsevier, vol. 21(4), pages 623-625.

    Cited by:

    1. Stekler, H.O., 2007. "The future of macroeconomic forecasting: Understanding the forecasting process," International Journal of Forecasting, Elsevier, vol. 23(2), pages 237-248.

  29. Pena, Daniel & Poncela, Pilar, 2004. "Forecasting with nonstationary dynamic factor models," Journal of Econometrics, Elsevier, vol. 119(2), pages 291-321, April.
    See citations under working paper version above.
  30. Garcia-Ferrer, Antonio & Poncela, Pilar, 2002. "Forecasting European GNP Data through Common Factor Models and Other Procedures," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(4), pages 225-244, July.

    Cited by:

    1. Stefan Gerlach & Matthew S. Yiu, 2004. "A Dynamic Factor Model for Current-Quarter Estimates of Economic Activity in Hong Kong," Working Papers 162004, Hong Kong Institute for Monetary Research.
    2. Ortega, Jose Antonio & Poncela, Pilar, 2005. "Joint forecasts of Southern European fertility rates with non-stationary dynamic factor models," International Journal of Forecasting, Elsevier, vol. 21(3), pages 539-550.
    3. Wei-Chun Hsu & Lin Lin & Chen-Yu Li, 2014. "Forecasting automobile sales: the Peña-Box approach," Transportation Planning and Technology, Taylor & Francis Journals, vol. 37(6), pages 568-580, August.
    4. 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.
    5. Hwee Kwan Chow & Keen Meng Choy, 2009. "Analyzing and Forecasting Business Cycles in a Small Open Economy: A Dynamic Factor Model for Singapore," Working Papers 05-2009, Singapore Management University, School of Economics.
    6. Poncela, Pilar, 2000. "Forecasting with nostationary dynamic factor models," DES - Working Papers. Statistics and Econometrics. WS 9959, Universidad Carlos III de Madrid. Departamento de Estadística.
    7. Marcos Bujosa & Antonio García‐Ferrer & Aránzazu de Juan & Antonio Martín‐Arroyo, 2020. "Evaluating early warning and coincident indicators of business cycles using smooth trends," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 1-17, January.
    8. John Galbraith & Greg Tkacz, 2007. "How Far Can Forecasting Models Forecast? Forecast Content Horizons for Some Important Macroeconomic Variables," Staff Working Papers 07-1, Bank of Canada.

  31. Guerrero, Víctor M. & Juárez, Rodrigo & Poncela, Pilar, 2001. "Data graduation based on statistical time series methods," Statistics & Probability Letters, Elsevier, vol. 52(2), pages 169-175, April.
    See citations under working paper version above.

Chapters

  1. 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.
    See citations under working paper version above.Sorry, no citations of chapters recorded.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.