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Yasutomo Murasawa

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Wikipedia or ReplicationWiki mentions

(Only mentions on Wikipedia that link back to a page on a RePEc service)
  1. Roberto S. Mariano & Yasutomo Murasawa, 2003. "A new coincident index of business cycles based on monthly and quarterly series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 427-443.

    Mentioned in:

    1. A new coincident index of businesscycles based on monthly and quarterly series (Journal of Applied Econometrics 2003) in ReplicationWiki ()
    2. A new coincident index of business cycles based on monthly and quarterly series (Journal of Applied Econometrics 2003) in ReplicationWiki ()

Working papers

  1. Murasawa, Yasutomo, 2015. "The multivariate Beveridge--Nelson decomposition with I(1) and I(2) series," MPRA Paper 66319, University Library of Munich, Germany.

    Cited by:

    1. Xu, Jia & Tan, Xiujie & He, Gang & Liu, Yu, 2019. "Disentangling the drivers of carbon prices in China's ETS pilots — An EEMD approach," Technological Forecasting and Social Change, Elsevier, vol. 139(C), pages 1-9.
    2. Murasawa, Yasutomo, 2019. "Bayesian multivariate Beveridge--Nelson decomposition of I(1) and I(2) series with cointegration," MPRA Paper 91979, University Library of Munich, Germany.

  2. MURASAWA Yasutomo, 2010. "Measuring Inflation Expectations Using Interval-Coded Data," ESRI Discussion paper series 236, Economic and Social Research Institute (ESRI).

    Cited by:

    1. Murasawa, Yasutomo, 2017. "Measuring the Distributions of Public Inflation Perceptions and Expectations in the UK," MPRA Paper 76244, University Library of Munich, Germany.
    2. Yasutomo Murasawa, 2020. "Measuring public inflation perceptions and expectations in the UK," Empirical Economics, Springer, vol. 59(1), pages 315-344, July.
    3. Péter Gábriel, 2010. "Household inflation expectations and inflation dynamics," MNB Working Papers 2010/12, Magyar Nemzeti Bank (Central Bank of Hungary).
    4. Nikola Mirkov & Andreas Steinhauer, 2014. "Are subjective distributions in inflation expectations symmetric?," ECON - Working Papers 173, Department of Economics - University of Zurich.
    5. Cacchiarelli, Luca & Carbone, Anna & Esti, Marco & Laureti, Tiziana & Sorrentino, Alessandro, 2015. "Assessing the Value of Quality in the Italian Wine Market," 2015 Conference, August 9-14, 2015, Milan, Italy 211379, International Association of Agricultural Economists.
    6. Cacchiarelli, Luca & Carbone, Anna & Esti, Marco & Laureti, Tiziana & Sorrentino, Alessandro, 2015. "Wine quality and prices: experts vs market," 143rd Joint EAAE/AAEA Seminar, March 25-27, 2015, Naples, Italy 202750, European Association of Agricultural Economists.

  3. Yasutomo Murasawa & Roberto S. Mariano, 2004. "Constructing a Coincident Index of Business Cycles Without Assuming a One-Factor Model," Econometric Society 2004 Far Eastern Meetings 710, Econometric Society.

    Cited by:

    1. Cecilia Frale & David Veredas, 2008. "A Monthly Volatility Index for the US Economy," Working Papers ECARES 2008-008, ULB -- Universite Libre de Bruxelles.
    2. Paul Viefers, 2011. "Bayesian Inference for the Mixed-Frequency VAR Model," Discussion Papers of DIW Berlin 1172, DIW Berlin, German Institute for Economic Research.
    3. Urasawa, Satoshi, 2014. "Real-time GDP forecasting for Japan: A dynamic factor model approach," Journal of the Japanese and International Economies, Elsevier, vol. 34(C), pages 116-134.

Articles

  1. Yasutomo Murasawa, 2020. "Measuring public inflation perceptions and expectations in the UK," Empirical Economics, Springer, vol. 59(1), pages 315-344, July.

    Cited by:

    1. Marcin Błażejowski, 2024. "Which C compiler and BLAS/LAPACK library should I use: gretl’s numerical efficiency in different configurations," Computational Statistics, Springer, vol. 39(7), pages 3497-3522, December.

  2. Yasutomo Murasawa, 2016. "The Beveridge–Nelson decomposition of mixed-frequency series," Empirical Economics, Springer, vol. 51(4), pages 1415-1441, December.

    Cited by:

    1. Murasawa, Yasutomo, 2019. "Bayesian multivariate Beveridge--Nelson decomposition of I(1) and I(2) series with cointegration," MPRA Paper 91979, University Library of Munich, Germany.

  3. Murasawa, Yasutomo, 2015. "The multivariate Beveridge–Nelson decomposition with I(1) and I(2) series," Economics Letters, Elsevier, vol. 137(C), pages 157-162.
    See citations under working paper version above.
  4. Yasutomo Murasawa, 2014. "Measuring the natural rates, gaps, and deviation cycles," Empirical Economics, Springer, vol. 47(2), pages 495-522, September.

    Cited by:

    1. Yasutomo Murasawa, 2016. "The Beveridge–Nelson decomposition of mixed-frequency series," Empirical Economics, Springer, vol. 51(4), pages 1415-1441, December.
    2. Marcin Błażejowski, 2024. "Which C compiler and BLAS/LAPACK library should I use: gretl’s numerical efficiency in different configurations," Computational Statistics, Springer, vol. 39(7), pages 3497-3522, December.
    3. Murasawa, Yasutomo, 2015. "The multivariate Beveridge--Nelson decomposition with I(1) and I(2) series," MPRA Paper 66319, University Library of Munich, Germany.
    4. Murasawa, Yasutomo, 2019. "Bayesian multivariate Beveridge--Nelson decomposition of I(1) and I(2) series with cointegration," MPRA Paper 91979, University Library of Munich, Germany.
    5. Chalmovianský, Jakub & Němec, Daniel, 2022. "Assessing uncertainty of output gap estimates: Evidence from Visegrad countries," Economic Modelling, Elsevier, vol. 116(C).

  5. Chengsi Zhang & Butan Zhang & Zhe Lu & Yasutomo Murasawa, 2013. "Output Gap Estimation and Monetary Policy in China," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 49(S4), pages 119-131, September.

    Cited by:

    1. Li, Xiao-Lin & Yan, Jing & Wei, Xiaohui, 2021. "Dynamic connectedness among monetary policy cycle, financial cycle and business cycle in China," Economic Analysis and Policy, Elsevier, vol. 69(C), pages 640-652.
    2. Wojciech Maliszewski & Ms. Longmei Zhang, 2015. "China’s Growth: Can Goldilocks Outgrow Bears?," IMF Working Papers 2015/113, International Monetary Fund.
    3. Mattesini, Fabrizio & Nosal, Ed, 2016. "Liquidity and asset prices in a monetary model with OTC asset markets," Journal of Economic Theory, Elsevier, vol. 164(C), pages 187-217.
    4. Paul G. Egan & Anthony J. Leddin, 2016. "Examining Monetary Policy Transmission in the People's Republic of China–Structural Change Models with a Monetary Policy Index," Asian Development Review, MIT Press, vol. 33(1), pages 74-110, March.

  6. Yasutomo Murasawa, 2013. "Measuring Inflation Expectations Using Interval-Coded Data," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(4), pages 602-623, August.
    See citations under working paper version above.
  7. Zhang, Chengsi & Murasawa, Yasutomo, 2012. "Multivariate model-based gap measures and a new Phillips curve for China," China Economic Review, Elsevier, vol. 23(1), pages 60-70.

    Cited by:

    1. Makram El-Shagi & Kiril Tochkov, 2023. "Regional Heterogeneity and the Provinicial Phillips Curve in China," CFDS Discussion Paper Series 2023/3, Center for Financial Development and Stability at Henan University, Kaifeng, Henan, China.
    2. Han, Yang & Liu, Zehao & Ma, Jun, 2020. "Growth cycles and business cycles of the Chinese economy through the lens of the unobserved components model," China Economic Review, Elsevier, vol. 63(C).
    3. Paul G. Egan & Anthony J. Leddin, 2016. "Examining Monetary Policy Transmission in the People's Republic of China–Structural Change Models with a Monetary Policy Index," Asian Development Review, MIT Press, vol. 33(1), pages 74-110, March.

  8. Zhang, Chengsi & Murasawa, Yasutomo, 2011. "Output gap measurement and the New Keynesian Phillips curve for China," Economic Modelling, Elsevier, vol. 28(6), pages 2462-2468.

    Cited by:

    1. Danny Hermawan & Denny Lie & Aryo Sasongko & Richard I. Yusan, 2023. "Money velocity, digital currency, and inflation dynamics," Working Papers 2023-01, University of Sydney, School of Economics.
    2. Makram El-Shagi & Kiril Tochkov, 2023. "Regional Heterogeneity and the Provinicial Phillips Curve in China," CFDS Discussion Paper Series 2023/3, Center for Financial Development and Stability at Henan University, Kaifeng, Henan, China.
    3. Clarida, Richard & Galí, Jordi & Gertler, Mark, 1999. "The Science of Monetary Policy: A New Keynesian Perspective," CEPR Discussion Papers 2139, C.E.P.R. Discussion Papers.
    4. Tiwari, Aviral Kumar & Oros, Cornel & Albulescu, Claudiu Tiberiu, 2014. "Revisiting the inflation–output gap relationship for France using a wavelet transform approach," Economic Modelling, Elsevier, vol. 37(C), pages 464-475.
    5. Saman, Corina & Pauna, Bianca, 2013. "New Keynesian Phillips Curve for Romania," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 159-171, June.
    6. Buncic, Daniel & Müller, Oliver, 2017. "Measuring the output gap in Switzerland with linear opinion pools," Economic Modelling, Elsevier, vol. 64(C), pages 153-171.
    7. Carsten A. Holz & Aaron Mehrotra, 2016. "Wage and Price Dynamics in China," The World Economy, Wiley Blackwell, vol. 39(8), pages 1109-1127, August.
    8. Nurudeen Abu, 2019. "Inflation and Unemployment Trade-off: A Re-examination of the Phillips Curve and its Stability in Nigeria," Contemporary Economics, University of Economics and Human Sciences in Warsaw., vol. 13(1), March.
    9. Jana Budova & Veronika Sulikova & Marianna Sinicakova, 2023. "When Inflation Again Matters: Do Domestic and Global Output Gaps Determine Inflation in the EU?," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 25(63), pages 575-575, April.
    10. Sardor Sadykov, 2018. "Modelling Of Inflationary Processes In Uzbekistan On The Basis Of The New Keynesian Philips Curve," Economics and Management, Faculty of Economics, SOUTH-WEST UNIVERSITY "NEOFIT RILSKI", BLAGOEVGRAD, vol. 14(2), pages 72-99.
    11. Xu, Qifa & Niu, Xufeng & Jiang, Cuixia & Huang, Xue, 2015. "The Phillips curve in the US: A nonlinear quantile regression approach," Economic Modelling, Elsevier, vol. 49(C), pages 186-197.
    12. Carsten A Holz & Aaron Mehrotra, 2013. "Wage and price dynamics in a large emerging economy: The case of China," BIS Working Papers 409, Bank for International Settlements.
    13. Bayari, Celal, 2020. "The Neoliberal Globalization Link to the Belt and Road Initiative: The State and State-Owned-Enterprises in China [alternative title: Bilateral and Multilateral Dualities of the Chinese State in the C," MPRA Paper 104471, University Library of Munich, Germany, revised 21 Jul 2020.
    14. Han, Yang & Liu, Zehao & Ma, Jun, 2020. "Growth cycles and business cycles of the Chinese economy through the lens of the unobserved components model," China Economic Review, Elsevier, vol. 63(C).
    15. Paul G. Egan & Anthony J. Leddin, 2016. "Examining Monetary Policy Transmission in the People's Republic of China–Structural Change Models with a Monetary Policy Index," Asian Development Review, MIT Press, vol. 33(1), pages 74-110, March.

  9. Roberto S. Mariano & Yasutomo Murasawa, 2010. "A Coincident Index, Common Factors, and Monthly Real GDP," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(1), pages 27-46, February.

    Cited by:

    1. Qian, Hang, 2012. "Essays on statistical inference with imperfectly observed data," ISU General Staff Papers 201201010800003618, Iowa State University, Department of Economics.
    2. Marcellino, Massimiliano & Sivec, Vasja, 2016. "Monetary, fiscal and oil shocks: Evidence based on mixed frequency structural FAVARs," Journal of Econometrics, Elsevier, vol. 193(2), pages 335-348.
    3. Scott Brave & R. Andrew Butters & Alejandro Justiniano, 2016. "Forecasting Economic Activity with Mixed Frequency Bayesian VARs," Working Paper Series WP-2016-5, Federal Reserve Bank of Chicago.
    4. Libero Monteforte & Valentina Raponi, 2019. "Short‐term forecasts of economic activity: Are fortnightly factors useful?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(3), pages 207-221, April.
    5. Paul Viefers & Ferdinand Fichtner & Simon Junker & Maximilian Podstawski, 2014. "Filtering German Economic Conditions from a Large Dataset: The New DIW Economic Barometer," Discussion Papers of DIW Berlin 1414, DIW Berlin, German Institute for Economic Research.
    6. Messner, Wolfgang, 2023. "The contingency impact of culture on health security capacities for pandemic preparedness: A moderated Bayesian inference analysis," Journal of International Management, Elsevier, vol. 29(5).
    7. Reichlin, Lucrezia & Giannone, Domenico & Modugno, Michele & Banbura, Marta, 2012. "Now-casting and the real-time data flow," CEPR Discussion Papers 9112, C.E.P.R. Discussion Papers.
    8. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
    9. Roberto S. Mariano & Suleyman Ozmucur, 2021. "Predictive Performance of Mixed-Frequency Nowcasting and Forecasting Models (with Application to Philippine Inflation and GDP Growth)," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 383-400, December.
    10. Robert Lehmann, 2024. "A real-time regional accounts database for Germany with applications to GDP revisions and nowcasting," Empirical Economics, Springer, vol. 67(2), pages 817-838, August.
    11. Jin, Sainan & Miao, Ke & Su, Liangjun, 2021. "On factor models with random missing: EM estimation, inference, and cross validation," Journal of Econometrics, Elsevier, vol. 222(1), pages 745-777.
    12. Gary Koop & Stuart McIntyre & James Mitchell & Aubrey Poon, 2022. "Reconciled Estimates of Monthly GDP in the US," Working Papers 22-01, Federal Reserve Bank of Cleveland.
    13. Ahiadorme, Johnson Worlanyo, 2020. "Monetary policy transmission and income inequality in Sub-Saharan Africa," MPRA Paper 104084, University Library of Munich, Germany.
    14. Lenza, Michele & Cimadomo, Jacopo & Giannone, Domenico & Monti, Francesca & Sokol, Andrej, 2021. "Nowcasting with Large Bayesian Vector Autoregressions," CEPR Discussion Papers 15854, C.E.P.R. Discussion Papers.
    15. 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, March.
    16. Lehmann, Robert & Wikman, Ida, 2022. "Quarterly GDP Estimates for the German States," MPRA Paper 112642, University Library of Munich, Germany.
    17. Gani Ramadani & Magdalena Petrovska & Vesna Bucevska, 2021. "Evaluation of mixed frequency approaches for tracking near-term economic developments in North Macedonia," Working Papers 2021-03, National Bank of the Republic of North Macedonia.
    18. Ankargren Sebastian & Unosson Måns & Yang Yukai, 2020. "A Flexible Mixed-Frequency Vector Autoregression with a Steady-State Prior," Journal of Time Series Econometrics, De Gruyter, vol. 12(2), pages 1-41, July.
    19. Gary Koop & Stuart McIntyre & James Mitchell & Aubrey Poon, 2018. "Regional Output Growth in the United Kingdom: More Timely and Higher Frequency Estimates, 1970-2017," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-14, Economic Statistics Centre of Excellence (ESCoE).
    20. Yasutomo Murasawa, 2014. "Measuring the natural rates, gaps, and deviation cycles," Empirical Economics, Springer, vol. 47(2), pages 495-522, September.
    21. Yasutomo Murasawa, 2016. "The Beveridge–Nelson decomposition of mixed-frequency series," Empirical Economics, Springer, vol. 51(4), pages 1415-1441, December.
    22. Knut Are Aastveit & Claudia Foroni & Francesco Ravazzolo, 2014. "Density forecasts with MIDAS models," Working Paper 2014/10, Norges Bank.
    23. Luca Barbaglia & Lorenzo Frattarolo & Niko Hauzenberger & Dominik Hirschbuehl & Florian Huber & Luca Onorante & Michael Pfarrhofer & Luca Tiozzo Pezzoli, 2024. "Nowcasting economic activity in European regions using a mixed-frequency dynamic factor model," Papers 2401.10054, arXiv.org.
    24. Michal Franta & David Havrlant & Marek Rusnak, 2014. "Forecasting Czech GDP Using Mixed-Frequency Data Models," Working Papers 2014/08, Czech National Bank.
    25. 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.
    26. Qian, Hang, 2016. "A computationally efficient method for vector autoregression with mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 433-437.
    27. Thomas B Götz & Klemens Hauzenberger, 2021. "Large mixed-frequency VARs with a parsimonious time-varying parameter structure," The Econometrics Journal, Royal Economic Society, vol. 24(3), pages 442-461.
    28. Gilles Dufrénot & Meryem Rhouzlane & Etienne Vaccaro-Grange, 2022. "Potential growth and natural yield curve in Japan," Post-Print hal-03680259, HAL.
    29. Grassi, Stefano & Proietti, Tommaso & Frale, Cecilia & Marcellino, Massimiliano & Mazzi, Gianluigi, 2015. "EuroMInd-C: A disaggregate monthly indicator of economic activity for the Euro area and member countries," International Journal of Forecasting, Elsevier, vol. 31(3), pages 712-738.
    30. Kenichiro McAlinn, 2021. "Mixed‐frequency Bayesian predictive synthesis for economic nowcasting," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1143-1163, November.
    31. Matteo Iacopini & Aubrey Poon & Luca Rossini & Dan Zhu, 2022. "Bayesian Mixed-Frequency Quantile Vector Autoregression: Eliciting tail risks of Monthly US GDP," Papers 2209.01910, arXiv.org.
    32. 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.
    33. Bhaghoe, Sailesh & Ooft, Gavin, 2021. "Nowcasting Quarterly GDP Growth in Suriname with Factor-MIDAS and Mixed-Frequency VAR Models," Studies in Applied Economics 176, The Johns Hopkins Institute for Applied Economics, Global Health, and the Study of Business Enterprise.
    34. Cleiton Guollo Taufemback, 2023. "Asymptotic Behavior of Temporal Aggregation in Mixed‐Frequency Datasets," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(4), pages 894-909, August.
    35. Sebastian Ankargren & Paulina Jon'eus, 2019. "Simulation smoothing for nowcasting with large mixed-frequency VARs," Papers 1907.01075, arXiv.org.
    36. Alexopoulos, Angelos & Varthalitis, Petros, 2023. "A machine learning approach to construct quarterly data on intangible investment for Eurozone," Economics Letters, Elsevier, vol. 231(C).
    37. Rueben Ellul, 2016. "A real-time measure of business conditions in Malta," CBM Working Papers WP/04/2016, Central Bank of Malta.
    38. Chan, Joshua C.C. & Poon, Aubrey & Zhu, Dan, 2023. "High-dimensional conditionally Gaussian state space models with missing data," Journal of Econometrics, Elsevier, vol. 236(1).
    39. Monica Defend & Aleksey Min & Lorenzo Portelli & Franz Ramsauer & Francesco Sandrini & Rudi Zagst, 2021. "Quantifying Drivers of Forecasted Returns Using Approximate Dynamic Factor Models for Mixed-Frequency Panel Data," Forecasting, MDPI, vol. 3(1), pages 1-35, February.
    40. Eraslan, Sercan & Schröder, Maximilian, 2019. "Nowcasting GDP with a large factor model space," Discussion Papers 41/2019, Deutsche Bundesbank.
    41. John Cotter & Mark Hallam & Kamil Yilmaz, 2017. "Mixed-frequency macro-financial spillovers," Working Papers 201704, Geary Institute, University College Dublin.
    42. Porshakov, A. & Ponomarenko, A. & Sinyakov, A., 2016. "Nowcasting and Short-Term Forecasting of Russian GDP with a Dynamic Factor Model," Journal of the New Economic Association, New Economic Association, vol. 30(2), pages 60-76.
    43. Eric Ghysels & J. Isaac Miller, 2013. "Testing for Cointegration with Temporally Aggregated and Mixed-frequency Time Series," Working Papers 1307, Department of Economics, University of Missouri, revised 07 May 2014.
    44. Hagher Ben Rhomdhane & Brahim Mehdi Benlallouna, 2022. "Nowcasting real GDP in Tunisia using large datasets and mixed-frequency models," IHEID Working Papers 02-2022, Economics Section, The Graduate Institute of International Studies.
    45. Alain Galli, 2018. "Which Indicators Matter? Analyzing the Swiss Business Cycle Using a Large-Scale Mixed-Frequency Dynamic Factor Model," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 14(2), pages 179-218, November.
    46. Claudia Foroni & Massimiliano Marcellino, 2014. "Mixed frequency structural VARs," Working Paper 2014/01, Norges Bank.
    47. Jansen, W. Jos & Jin, Xiaowen & de Winter, Jasper M., 2016. "Forecasting and nowcasting real GDP: Comparing statistical models and subjective forecasts," International Journal of Forecasting, Elsevier, vol. 32(2), pages 411-436.
    48. Ghysels, Eric & Hill, Jonathan B. & Motegi, Kaiji, 2016. "Testing for Granger causality with mixed frequency data," Journal of Econometrics, Elsevier, vol. 192(1), pages 207-230.
    49. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
    50. Sebastian Ankargren & Måns Unosson & Yukai Yang, 2018. "A mixed-frequency Bayesian vector autoregression with a steady-state prior," CREATES Research Papers 2018-32, Department of Economics and Business Economics, Aarhus University.
    51. Koki Kyo & Hideo Noda & Genshiro Kitagawa, 2022. "Co-movement of Cyclical Components Approach to Construct a Coincident Index of Business Cycles," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 18(1), pages 101-127, March.
    52. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2018. "Using low frequency information for predicting high frequency variables," International Journal of Forecasting, Elsevier, vol. 34(4), pages 774-787.
    53. Hager Ben Romdhane, 2021. "Nowcasting in Tunisia using large datasets and mixed frequency models," IHEID Working Papers 11-2021, Economics Section, The Graduate Institute of International Studies.
    54. Pablo Aguilar & Corinna Ghirelli & Matías Pacce & Alberto Urtasun, 2020. "Can news help measure economic sentiment? An application in COVID-19 times," Working Papers 2027, Banco de España.
    55. Markus Heinrich & Magnus Reif, 2018. "Forecasting using mixed-frequency VARs with time-varying parameters," ifo Working Paper Series 273, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    56. Fady Barsoum, 2015. "Point and Density Forecasts Using an Unrestricted Mixed-Frequency VAR Model," Working Paper Series of the Department of Economics, University of Konstanz 2015-19, Department of Economics, University of Konstanz.
    57. Brave, Scott A. & Butters, R. Andrew & Justiniano, Alejandro, 2019. "Forecasting economic activity with mixed frequency BVARs," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1692-1707.
    58. Tesi Aliaj & Milos Ciganovic & Massimiliano Tancioni, 2023. "Nowcasting inflation with Lasso‐regularized vector autoregressions and mixed frequency data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(3), pages 464-480, April.
    59. Michael Zhemkov, 2022. "Assessment of Monthly GDP Growth Using Temporal Disaggregation Methods," Russian Journal of Money and Finance, Bank of Russia, vol. 81(2), pages 79-104, June.
    60. George Kapetanios & Fotis Papailias, 2018. "Big Data & Macroeconomic Nowcasting: Methodological Review," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-12, Economic Statistics Centre of Excellence (ESCoE).
    61. Qian, Hang, 2012. "A Flexible State Space Model and its Applications," MPRA Paper 38455, University Library of Munich, Germany.
    62. Schumacher, Christian & Marcellino, Massimiliano & Kuzin, Vladimir, 2009. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the Euro Area," CEPR Discussion Papers 7445, C.E.P.R. Discussion Papers.
    63. Löchel, H. & Packham, N. & Walisch, F., 2016. "Determinants of the onshore and offshore Chinese government yield curves," Pacific-Basin Finance Journal, Elsevier, vol. 36(C), pages 77-93.
    64. Qiu, Yue, 2020. "Forecasting the Consumer Confidence Index with tree-based MIDAS regressions," Economic Modelling, Elsevier, vol. 91(C), pages 247-256.
    65. Heinisch Katja & Scheufele Rolf, 2019. "Should Forecasters Use Real-Time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence," German Economic Review, De Gruyter, vol. 20(4), pages 170-200, December.
    66. 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.
    67. Foroni, Claudia & Marcellino, Massimiliano & Stevanović, Dalibor, 2018. "Mixed frequency models with MA components," Discussion Papers 02/2018, Deutsche Bundesbank.
    68. Gary Koop & Stuart McIntyre & James Mitchell & Aubrey Poon, 2020. "Regional output growth in the United Kingdom: More timely and higher frequency estimates from 1970," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(2), pages 176-197, March.
    69. Ramadani Gani & Petrovska Magdalena & Bucevska Vesna, 2021. "Evaluation of Mixed Frequency Approaches for Tracking Near-Term Economic Developments in North Macedonia," South East European Journal of Economics and Business, Sciendo, vol. 16(2), pages 43-52, December.
    70. Barhoumi, K. & Darn , O. & Ferrara, L., 2013. "Dynamic Factor Models: A review of the Literature ," Working papers 430, Banque de France.
    71. Markus Heinrich & Magnus Reif, 2020. "Real-Time Forecasting Using Mixed-Frequency VARS with Time-Varying Parameters," CESifo Working Paper Series 8054, CESifo.
    72. Nuttanan Wichitaksorn, 2020. "Analyzing and Forecasting Thai Macroeconomic Data using Mixed-Frequency Approach," PIER Discussion Papers 146, Puey Ungphakorn Institute for Economic Research.
    73. Davtyan, Karen, 2023. "Unconventional monetary policy and economic inequality," Economic Modelling, Elsevier, vol. 126(C).
    74. Claudia Foroni & Massimiliano Marcellino, 2013. "Mixed frequency structural models: estimation, and policy analysis," Working Paper 2013/15, Norges Bank.
    75. Bjørn Eraker & Ching Wai (Jeremy) Chiu & Andrew T. Foerster & Tae Bong Kim & Hernán D. Seoane, 2015. "Bayesian Mixed Frequency VARs," Journal of Financial Econometrics, Oxford University Press, vol. 13(3), pages 698-721.
    76. Franz Ramsauer & Aleksey Min & Michael Lingauer, 2019. "Estimation of FAVAR Models for Incomplete Data with a Kalman Filter for Factors with Observable Components," Econometrics, MDPI, vol. 7(3), pages 1-43, July.
    77. Qian, Hang, 2013. "Vector Autoregression with Mixed Frequency Data," MPRA Paper 47856, University Library of Munich, Germany.
    78. Heinisch, Katja, 2016. "A real-time analysis on the importance of hard and soft data for nowcasting German GDP," VfS Annual Conference 2016 (Augsburg): Demographic Change 145864, Verein für Socialpolitik / German Economic Association.
    79. Zheng, Tingguo & Fan, Xinyue & Jin, Wei & Fang, Kuangnan, 2024. "Words or numbers? Macroeconomic nowcasting with textual and macroeconomic data," International Journal of Forecasting, Elsevier, vol. 40(2), pages 746-761.
    80. Sebastian Ankargren & Paulina Jon'eus, 2019. "Estimating Large Mixed-Frequency Bayesian VAR Models," Papers 1912.02231, arXiv.org.
    81. Xu, Qifa & Zhuo, Xingxuan & Jiang, Cuixia & Liu, Xi & Liu, Yezheng, 2018. "Group penalized unrestricted mixed data sampling model with application to forecasting US GDP growth," Economic Modelling, Elsevier, vol. 75(C), pages 221-236.
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  11. Murasawa, Yasutomo & Morimune, Kimio, 2004. "Distribution-free statistical inference for generalized Lorenz dominance based on grouped data," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 64(1), pages 133-142.

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    1. Kuan Xu & Gordon Fisher, 2006. "Myopic loss aversion and margin of safety: the risk of value investing," Quantitative Finance, Taylor & Francis Journals, vol. 6(6), pages 481-494.
    2. Dentcheva Darinka & Stock Gregory J. & Rekeda Ludmyla, 2011. "Mean-risk tests of stochastic dominance," Statistics & Risk Modeling, De Gruyter, vol. 28(2), pages 97-118, May.

  12. Roberto S. Mariano & Yasutomo Murasawa, 2003. "A new coincident index of business cycles based on monthly and quarterly series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 427-443.

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