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George Monokroussos

Personal Details

First Name:George
Middle Name:
Last Name:Monokroussos
Suffix:
RePEc Short-ID:pmo480
https://sites.google.com/site/georgemonokroussos/
Terminal Degree:2005 Department of Economics; University of California-San Diego (UCSD) (from RePEc Genealogy)

Affiliation

Joint Research Centre
European Commission

Ispra, Italy
https://ec.europa.eu/jrc/en/about/jrc-site/ispra

:


RePEc:edi:eejrcit (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Langedijk, Sven & Monokroussos, George & Papanagiotou, Evangelia, 2015. "Benchmarking Liquidity Proxies: Accounting for Dynamics and Frequency Issues," MPRA Paper 61865, University Library of Munich, Germany.
  2. Monokroussos, George, 2015. "Nowcasting in Real Time Using Popularity Priors," MPRA Paper 68594, University Library of Munich, Germany.
  3. Kajal Lahiri & George Monokroussos & Yongchen Zhao, 2015. "Forecasting Consumption: The Role of Consumer Confidence in Real Time with many Predictors," Working Papers 2015-02, Towson University, Department of Economics, revised Jul 2015.
  4. Kajal Lahiri & George Monokroussos & Yongchen Zhao, 2012. "Forecasting Consumption in Real Time: The Role of Consumer Confidence Surveys," Discussion Papers 12-02, University at Albany, SUNY, Department of Economics.
  5. Kajal Lahiri & George Monokroussos & Yongchen Zhao, 2012. "The Yield Spread Puzzle and the Information Content of SPF Forecasts," CESifo Working Paper Series 3949, CESifo Group Munich.
  6. Kajal Lahiri & George Monokroussos & Yongchen Zhao, 2012. "The yield spread puzzle and the information content of SPF forecasts," Discussion Papers 12-04, University at Albany, SUNY, Department of Economics.
  7. Kajal Lahiri & George Monokroussos, 2011. "Nowcasting US GDP: The role of ISM Business Surveys," Discussion Papers 11-01, University at Albany, SUNY, Department of Economics.
  8. George Monokroussos, 2009. "A Classical MCMC Approach to the Estimation of Limited Dependent Variable Models of Time Series," Discussion Papers 09-07, University at Albany, SUNY, Department of Economics.
  9. George Monokroussos, 2006. "A Dynamic Tobit Model for the Open Market Desk's Daily Reaction Function," Computing in Economics and Finance 2006 390, Society for Computational Economics.
  10. George Monokroussos, 2005. "Dynamic Limited Dependent Variable Modeling and US Monetary Policy," Computing in Economics and Finance 2005 460, Society for Computational Economics.
  11. Kraay, Aart & Monokroussos, George, 1999. "Growth forecasts using time series and growth models," Policy Research Working Paper Series 2224, The World Bank.

Articles

  1. Langedijk, Sven & Monokroussos, George & Papanagiotou, Evangelia, 2018. "Benchmarking liquidity proxies: The case of EU sovereign bonds," International Review of Economics & Finance, Elsevier, vol. 56(C), pages 321-329.
  2. Kajal Lahiri & George Monokroussos & Yongchen Zhao, 2016. "Forecasting Consumption: the Role of Consumer Confidence in Real Time with many Predictors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1254-1275, November.
  3. Lahiri, Kajal & Monokroussos, George & Zhao, Yongchen, 2013. "The yield spread puzzle and the information content of SPF forecasts," Economics Letters, Elsevier, vol. 118(1), pages 219-221.
  4. George Monokroussos, 2013. "A Classical MCMC Approach to the Estimation of Limited Dependent Variable Models of Time Series," Computational Economics, Springer;Society for Computational Economics, vol. 42(1), pages 71-105, June.
  5. Lahiri, Kajal & Monokroussos, George, 2013. "Nowcasting US GDP: The role of ISM business surveys," International Journal of Forecasting, Elsevier, vol. 29(4), pages 644-658.
  6. George Monokroussos, 2011. "Dynamic Limited Dependent Variable Modeling and U.S. Monetary Policy," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 43, pages 519-534, March.

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.

RePEc Biblio mentions

As found on the RePEc Biblio, the curated bibliography of Economics:
  1. Lahiri, Kajal & Monokroussos, George, 2013. "Nowcasting US GDP: The role of ISM business surveys," International Journal of Forecasting, Elsevier, vol. 29(4), pages 644-658.

    Mentioned in:

    1. > Econometrics > Forecasting > Nowcasting

Working papers

  1. Langedijk, Sven & Monokroussos, George & Papanagiotou, Evangelia, 2015. "Benchmarking Liquidity Proxies: Accounting for Dynamics and Frequency Issues," MPRA Paper 61865, University Library of Munich, Germany.

    Cited by:

    1. Langedijk, Sven & Monokroussos, George & Papanagiotou, Evangelia, 2018. "Benchmarking liquidity proxies: The case of EU sovereign bonds," International Review of Economics & Finance, Elsevier, vol. 56(C), pages 321-329.
    2. Csóka, Péter & Havran, Dániel & Váradi, Kata, 2016. "Konferencia a pénzügyi piacok likviditásáról. Sixth Annual Financial Market Liquidity Conference, 2015
      [Conference on the liquidity of financial markets. Sixth Annual Financial Market Liquidity Con
      ," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(4), pages 461-469.

  2. Kajal Lahiri & George Monokroussos & Yongchen Zhao, 2015. "Forecasting Consumption: The Role of Consumer Confidence in Real Time with many Predictors," Working Papers 2015-02, Towson University, Department of Economics, revised Jul 2015.

    Cited by:

    1. Aneta Maria Kłopocka, 2017. "Does Consumer Confidence Forecast Household Saving and Borrowing Behavior? Evidence for Poland," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 133(2), pages 693-717, September.
    2. Hashmat Khan & Santosh Upadhayaya, 2017. "Does Business Confidence Matter for Investment?," Carleton Economic Papers 17-13, Carleton University, Department of Economics, revised 20 Mar 2019.
    3. 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.
    4. Dimitra Kontana & Fotios Siokis, 2019. "Revisiting the Relationship between Financial Wealth, Housing Wealth, and Consumption: A Panel Analysis for the U.S," Discussion Paper Series 2019_03, Department of Economics, University of Macedonia, revised May 2019.
    5. Gabe Jacob de Bondt & Arne Gieseck & Zivile Zekaite, 2020. "Thick modelling income and wealth effects: a forecast application to euro area private consumption," Empirical Economics, Springer, vol. 58(1), pages 257-286, January.
    6. van Giesen, Roxanne I. & Pieters, Rik, 2019. "Climbing out of an economic crisis: A cycle of consumer sentiment and personal stress," Journal of Economic Psychology, Elsevier, vol. 70(C), pages 109-124.
    7. Tony Chernis & Rodrigo Sekkel, 2017. "A Dynamic Factor Model for Nowcasting Canadian GDP Growth," Staff Working Papers 17-2, Bank of Canada.
    8. Gustavo Adolfo HERNANDEZ DIAZ & Margarita MARÍN JARAMILLO, 2016. "Pronóstico del Consumo Privado: Usando datos de alta frecuencia para el pronóstico de variables de baja frecuencia," Archivos de Economía 014828, Departamento Nacional de Planeación.
    9. Rusnák, Marek, 2016. "Nowcasting Czech GDP in real time," Economic Modelling, Elsevier, vol. 54(C), pages 26-39.
    10. 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.
    11. 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.
    12. Kevin Moran & Simplice Aimé Nono & Imad Rherrad, 2018. "Forecasting with Many Predictors: How Useful are National and International Confidence Data?," Cahiers de recherche 1814, Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques.
    13. de Bondt, Gabe & Gieseck, Arne & Herrero, Pablo & Zekaite, Zivile, 2019. "Disaggregate income and wealth effects in the largest euro area countries," Research Technical Papers 15/RT/19, Central Bank of Ireland.
    14. Lorenzo Bencivelli & Massimiliano Marcellino & Gianluca Moretti, 2017. "Forecasting economic activity by Bayesian bridge model averaging," Empirical Economics, Springer, vol. 53(1), pages 21-40, August.
    15. Christian Gayer & Alessandro Girardi & Andreas Reuter, 2016. "Replacing Judgment by Statistics: Constructing Consumer Confidence Indicators on the basis of Data-driven Techniques. The Case of the Euro Area," Working Papers LuissLab 16125, Dipartimento di Economia e Finanza, LUISS Guido Carli.
    16. Kajal Lahiri & Yongchen Zhao, 2016. "Determinants of Consumer Sentiment over Business Cycles: Evidence from the U.S. Surveys of Consumers," Working Papers 2016-14, Towson University, Department of Economics, revised Jul 2016.
    17. Acuña, Guillermo, 2017. "Evaluación de la capacidad predictiva del índice de percepción del consumidor
      [Assessing the predictive power of the consumer perception index]
      ," MPRA Paper 83154, University Library of Munich, Germany.
    18. Marina Matosec & Zdenka Obuljen Zoricic, 2019. "Identifying the Interdependence between Consumer Confidence and Macroeconomic Developments in Croatia," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 17(2-B), pages 345-354.
    19. Willem Vanlaer & Samantha Bielen & Wim Marneffe, 2020. "Consumer Confidence and Household Saving Behaviors: A Cross-Country Empirical Analysis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 147(2), pages 677-721, January.
    20. Oscar Claveria & Enric Monte & Salvador Torra, 2019. "Empirical modelling of survey-based expectations for the design of economic indicators in five European regions," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 46(2), pages 205-227, May.
    21. Hamid Baghestani, 2017. "Do US consumer survey data help beat the random walk in forecasting mortgage rates?," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1343017-134, January.
    22. Hashmat Khan & Jean-François Rouillard & Santosh Upadhayaya, 2019. "Consumer Confidence and Household Investment," Carleton Economic Papers 19-06, Carleton University, Department of Economics.

  3. Kajal Lahiri & George Monokroussos & Yongchen Zhao, 2012. "Forecasting Consumption in Real Time: The Role of Consumer Confidence Surveys," Discussion Papers 12-02, University at Albany, SUNY, Department of Economics.

    Cited by:

    1. John Khumalo, 2014. "Consumer Spending and Consumer Confidence in South Africa: Cointegration Analysis," Journal of Economics and Behavioral Studies, AMH International, vol. 6(2), pages 95-104.
    2. Hatice Gökçe Karasoy Can & Çağlar Yüncüler, 2018. "The Explanatory Power and the Forecast Performance of Consumer Confidence Indices for Private Consumption Growth in Turkey," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 54(9), pages 2136-2152, July.

  4. Kajal Lahiri & George Monokroussos & Yongchen Zhao, 2012. "The Yield Spread Puzzle and the Information Content of SPF Forecasts," CESifo Working Paper Series 3949, CESifo Group Munich.

    Cited by:

    1. Soojin Jo & Rodrigo Sekkel, 2019. "Macroeconomic Uncertainty Through the Lens of Professional Forecasters," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(3), pages 436-446, July.
    2. El-Shagi, Makram, 2019. "Rationality tests in the presence of instabilities in finite samples," Economic Modelling, Elsevier, vol. 79(C), pages 242-246.
    3. Weiling Liu & Emanuel Moench, . "What predicts U.S. recessions?," Staff Reports, Federal Reserve Bank of New York.
    4. Kajal Lahiri & Liu Yang, 2015. "Asymptotic Variance of Brier (Skill) Score in the Presence of Serial Correlation," CESifo Working Paper Series 5290, CESifo Group Munich.
    5. Liu, Weiling & Moench, Emanuel, 2016. "What predicts US recessions?," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1138-1150.
    6. Chatterjee, Ujjal K., 2018. "Bank liquidity creation and recessions," Journal of Banking & Finance, Elsevier, vol. 90(C), pages 64-75.
    7. Lahiri, Kajal & Yang, Liu, 2016. "Asymptotic variance of Brier (skill) score in the presence of serial correlation," Economics Letters, Elsevier, vol. 141(C), pages 125-129.
    8. Kajal Lahiri & Huaming Peng & Yongchen Zhao, 2013. "Testing the Value of Probability Forecasts for Calibrated Combining," Discussion Papers 13-02, University at Albany, SUNY, Department of Economics.
    9. Herman O. Stekler & Tianyu Ye, 2017. "Evaluating a leading indicator: an application—the term spread," Empirical Economics, Springer, vol. 53(1), pages 183-194, August.
    10. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    11. Pablo Aguilar & Jesús Vázquez, 2018. "Term structure and real-time learning," Working Papers 1803, Banco de España;Working Papers Homepage.

  5. Kajal Lahiri & George Monokroussos & Yongchen Zhao, 2012. "The yield spread puzzle and the information content of SPF forecasts," Discussion Papers 12-04, University at Albany, SUNY, Department of Economics.

    Cited by:

    1. Soojin Jo & Rodrigo Sekkel, 2019. "Macroeconomic Uncertainty Through the Lens of Professional Forecasters," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(3), pages 436-446, July.
    2. El-Shagi, Makram, 2019. "Rationality tests in the presence of instabilities in finite samples," Economic Modelling, Elsevier, vol. 79(C), pages 242-246.
    3. Weiling Liu & Emanuel Moench, . "What predicts U.S. recessions?," Staff Reports, Federal Reserve Bank of New York.
    4. Kajal Lahiri & Liu Yang, 2015. "Asymptotic Variance of Brier (Skill) Score in the Presence of Serial Correlation," CESifo Working Paper Series 5290, CESifo Group Munich.
    5. Liu, Weiling & Moench, Emanuel, 2016. "What predicts US recessions?," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1138-1150.
    6. Chatterjee, Ujjal K., 2018. "Bank liquidity creation and recessions," Journal of Banking & Finance, Elsevier, vol. 90(C), pages 64-75.
    7. Lahiri, Kajal & Yang, Liu, 2016. "Asymptotic variance of Brier (skill) score in the presence of serial correlation," Economics Letters, Elsevier, vol. 141(C), pages 125-129.
    8. Kajal Lahiri & Huaming Peng & Yongchen Zhao, 2013. "Testing the Value of Probability Forecasts for Calibrated Combining," Discussion Papers 13-02, University at Albany, SUNY, Department of Economics.
    9. Herman O. Stekler & Tianyu Ye, 2017. "Evaluating a leading indicator: an application—the term spread," Empirical Economics, Springer, vol. 53(1), pages 183-194, August.
    10. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    11. Pablo Aguilar & Jesús Vázquez, 2018. "Term structure and real-time learning," Working Papers 1803, Banco de España;Working Papers Homepage.

  6. Kajal Lahiri & George Monokroussos, 2011. "Nowcasting US GDP: The role of ISM Business Surveys," Discussion Papers 11-01, University at Albany, SUNY, Department of Economics.

    Cited by:

    1. Lahiri, Kajal & Monokroussos, George & Zhao, Yongchen, 2013. "The yield spread puzzle and the information content of SPF forecasts," Economics Letters, Elsevier, vol. 118(1), pages 219-221.
    2. Lamprou, Dimitra, 2016. "Nowcasting GDP in Greece: The impact of data revisions and forecast origin on model selection and performance," The Journal of Economic Asymmetries, Elsevier, vol. 14(PA), pages 93-102.
    3. Caruso, Alberto, 2018. "Nowcasting with the help of foreign indicators: The case of Mexico," Economic Modelling, Elsevier, vol. 69(C), pages 160-168.
    4. Michele Modugno & Lucrezia Reichlin & Domenico Giannone & Marta Banbura, 2012. "Nowcasting with Daily Data," 2012 Meeting Papers 555, Society for Economic Dynamics.
    5. Antonello D'Agostino & Domenico Giannone & Michele Lenza & Michele Modugno, 2015. "Nowcasting Business Cycles: a Bayesian Approach to Dynamic Heterogeneous Factor Models," Finance and Economics Discussion Series 2015-66, Board of Governors of the Federal Reserve System (U.S.).
    6. Bragoli, Daniela & Modugno, Michele, 2017. "A now-casting model for Canada: Do U.S. variables matter?," International Journal of Forecasting, Elsevier, vol. 33(4), pages 786-800.
    7. Kaufmann, Daniel & Scheufele, Rolf, 2017. "Business tendency surveys and macroeconomic fluctuations," International Journal of Forecasting, Elsevier, vol. 33(4), pages 878-893.
    8. Alain Galli & Christian Hepenstrick & Rolf Scheufele, 2017. "Mixed-frequency models for tracking short-term economic developments in Switzerland," Working Papers 2017-02, Swiss National Bank.
    9. Jos Jansen & Jasper de Winter, 2016. "Improving model-based near-term GDP forecasts by subjective forecasts: A real-time exercise for the G7 countries," DNB Working Papers 507, Netherlands Central Bank, Research Department.
    10. Wang, Mei-Chih & Tsangyao Chang, 2019. "Revisiting Oil Prices, Producer Price Index (PPI), and the Purchasing Managers Index (PMI) Nexus: China and the USA," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 9(8), pages 913-925, August.
    11. Ergun Ermisoglu & Yasin Akcelik & Arif Oduncu, 2013. "GDP Growth and Credit Data," Working Papers 1327, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    12. Tony Chernis & Rodrigo Sekkel, 2017. "A Dynamic Factor Model for Nowcasting Canadian GDP Growth," Staff Working Papers 17-2, Bank of Canada.
    13. Hindrayanto, Irma & Koopman, Siem Jan & de Winter, Jasper, 2016. "Forecasting and nowcasting economic growth in the euro area using factor models," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1284-1305.
    14. Alberto Caruso, 2018. "Macroeconomic News and Market Reaction: Surprise Indexes meet Nowcasting," Working Papers ECARES 2018-06, ULB -- Universite Libre de Bruxelles.
    15. Mogliani, Matteo & Darné, Olivier & Pluyaud, Bertrand, 2017. "The new MIBA model: Real-time nowcasting of French GDP using the Banque de France's monthly business survey," Economic Modelling, Elsevier, vol. 64(C), pages 26-39.
    16. Deicy J. Cristiano & Manuel D. Hernández & José David Pulido, 2012. "Pronósticos de corto plazo en tiempo real para la actividad económica colombiana," BORRADORES DE ECONOMIA 009827, BANCO DE LA REPÚBLICA.
    17. Sandra Hanslin & Rolf Scheufele, 2016. "Foreign PMIs: A reliable indicator for exports?," Working Papers 2016-01, Swiss National Bank.
    18. Rusnák, Marek, 2016. "Nowcasting Czech GDP in real time," Economic Modelling, Elsevier, vol. 54(C), pages 26-39.
    19. Bragoli, Daniela, 2017. "Now-casting the Japanese economy," International Journal of Forecasting, Elsevier, vol. 33(2), pages 390-402.
    20. Bańbura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2013. "Now-Casting and the Real-Time Data Flow," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 195-237, Elsevier.
    21. Gabe de Bondt, 2012. "Nowcasting: Trust the Purchasing Managers’ Index or wait for the flash GDP estimate?," EcoMod2012 3896, EcoMod.
    22. 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.
    23. John B. Broughton & Bento J. Lobo, 2018. "Herding and anchoring in macroeconomic forecasts: the case of the PMI," Empirical Economics, Springer, vol. 55(3), pages 1337-1355, November.
    24. Kevin Moran & Simplice Aimé Nono & Imad Rherrad, 2018. "Forecasting with Many Predictors: How Useful are National and International Confidence Data?," Cahiers de recherche 1814, Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques.
    25. Sangeeta Das & Dipankor Coondoo, 2018. "Is PMI Useful in Quarterly GDP Growth Forecasts for India? An Exploratory Note," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 16(1), pages 199-207, December.
    26. Das, Abhiman & Lahiri, Kajal & Zhao, Yongchen, 2019. "Inflation expectations in India: Learning from household tendency surveys," International Journal of Forecasting, Elsevier, vol. 35(3), pages 980-993.
    27. Kilinc, Zubeyir & Yucel, Eray, 2016. "PMI Thresholds for GDP Growth," MPRA Paper 70929, University Library of Munich, Germany.
    28. Lorenzo Bencivelli & Massimiliano Marcellino & Gianluca Moretti, 2017. "Forecasting economic activity by Bayesian bridge model averaging," Empirical Economics, Springer, vol. 53(1), pages 21-40, August.
    29. Kajal Lahiri & George Monokroussos & Yongchen Zhao, 2016. "Forecasting Consumption: the Role of Consumer Confidence in Real Time with many Predictors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1254-1275, November.
    30. Rolando F. Peláez, 2018. "Improving the usefulness of the Purchasing Managers’ Index," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 53(4), pages 195-201, October.
    31. Gabe J. Bondt, 2019. "A PMI-Based Real GDP Tracker for the Euro Area," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 15(2), pages 147-170, December.
    32. Kajal Lahiri & Yongchen Zhao, 2016. "Determinants of Consumer Sentiment over Business Cycles: Evidence from the U.S. Surveys of Consumers," Working Papers 2016-14, Towson University, Department of Economics, revised Jul 2016.
    33. Kajal Lahiri & George Monokroussos & Yongchen Zhao, 2012. "The Yield Spread Puzzle and the Information Content of SPF Forecasts," CESifo Working Paper Series 3949, CESifo Group Munich.
    34. Schnatz, Bernd & D'Agostino, Antonello, 2012. "Survey-based nowcasting of US growth: a real-time forecast comparison over more than 40 years," Working Paper Series 1455, European Central Bank.
    35. Brandyn Bok & Daniele Caratelli & Domenico Giannone & Argia M. Sbordone & Andrea Tambalotti, . "Macroeconomic nowcasting and forecasting with big data," Staff Reports, Federal Reserve Bank of New York.
    36. Tiziana Cesaroni & Stefano Iezzi, 2015. "The Predictive Content of Business Survey Indicators: evidence from SIGE," Working Papers LuissLab 15118, Dipartimento di Economia e Finanza, LUISS Guido Carli.
    37. Khundrakpam, Jeevan Kumar & George, Asish Thomas, 2012. "An Empirical Analysis of the Relationship between WPI and PMI-Manufacturing Price Indices in India," MPRA Paper 50929, University Library of Munich, Germany.
    38. Alexander James & Yaser S. Abu-Mostafa & Xiao Qiao, 2019. "Nowcasting Recessions using the SVM Machine Learning Algorithm," Papers 1903.03202, arXiv.org, revised Jun 2019.
    39. Liu, Ping & James Hueng, C., 2017. "Measuring real business condition in China," China Economic Review, Elsevier, vol. 46(C), pages 261-274.
    40. Alexander Chudik & Valerie Grossman & M. Hashem Pesaran, 2014. "A multi-country approach to forecasting output growth using PMIs," Globalization Institute Working Papers 213, Federal Reserve Bank of Dallas, revised 01 Nov 2014.
    41. 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).
    42. Huseyin Cagri Akkoyun & Mahmut Gunay, 2013. "Milli Gelir Buyume Tahmini : IYA ve PMI Gostergelerinin Rolu," CBT Research Notes in Economics 1331, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    43. Tsuchiya, Yoichi, 2014. "Purchasing and supply managers provide early clues on the direction of the US economy: An application of a new market-timing test," International Review of Economics & Finance, Elsevier, vol. 29(C), pages 599-618.
    44. Alexander Chudik & Valerie Grossman & M. Hashem Pesaran, 2014. "A Multi-Country Approach to Forecasting Output Growth Using PMIs," CESifo Working Paper Series 5100, CESifo Group Munich.
    45. Kajal Lahiri & George Monokroussos & Yongchen Zhao, 2012. "Forecasting Consumption in Real Time: The Role of Consumer Confidence Surveys," Discussion Papers 12-02, University at Albany, SUNY, Department of Economics.
    46. Hanslin Grossmann, Sandra & Scheufele, Rolf, 2015. "Foreign PMIs: A reliable indicator for Swiss exports," Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112830, Verein für Socialpolitik / German Economic Association.
    47. 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.
    48. Gabe J. Bondt & Stefano Schiaffi, 2015. "Confidence Matters for Current Economic Growth: Empirical Evidence for the Euro Area and the United States," Social Science Quarterly, Southwestern Social Science Association, vol. 96(4), pages 1027-1040, December.

  7. George Monokroussos, 2009. "A Classical MCMC Approach to the Estimation of Limited Dependent Variable Models of Time Series," Discussion Papers 09-07, University at Albany, SUNY, Department of Economics.

    Cited by:

    1. George Monokroussos, 2006. "A Dynamic Tobit Model for the Open Market Desk's Daily Reaction Function," Computing in Economics and Finance 2006 390, Society for Computational Economics.
    2. George Monokroussos, 2006. "Dynamic Limited Dependent Variable Modeling and U.S. Monetary Policy," Discussion Papers 06-02, University at Albany, SUNY, Department of Economics.

  8. George Monokroussos, 2005. "Dynamic Limited Dependent Variable Modeling and US Monetary Policy," Computing in Economics and Finance 2005 460, Society for Computational Economics.

    Cited by:

    1. Andrei Sirchenko, 2019. "A regime-switching model for the federal funds rate target," UvA-Econometrics Working Papers 19-01, Universiteit van Amsterdam, Dept. of Econometrics.
    2. Hyeongwoo Kim & Wen Shi, 2017. "The Determinants of the Benchmark Interest Rates in China: A Discrete Choice Model Approach," Auburn Economics Working Paper Series auwp2017-04, Department of Economics, Auburn University.
    3. Sjoerd van den Hauwe & Dick van Dijk & Richard Paap, 2011. "Bayesian Forecasting of Federal Funds Target Rate Decisions," Tinbergen Institute Discussion Papers 11-093/4, Tinbergen Institute.
    4. Gurnain Kaur Pasricha, 2017. "Policy Rules for Capital Controls," BIS Working Papers 670, Bank for International Settlements.
    5. George Monokroussos, 2009. "A Classical MCMC Approach to the Estimation of Limited Dependent Variable Models of Time Series," Discussion Papers 09-07, University at Albany, SUNY, Department of Economics.
    6. George Monokroussos, 2006. "A Dynamic Tobit Model for the Open Market Desk's Daily Reaction Function," Computing in Economics and Finance 2006 390, Society for Computational Economics.
    7. Zhang, Xinyu & Lu, Zudi & Zou, Guohua, 2013. "Adaptively combined forecasting for discrete response time series," Journal of Econometrics, Elsevier, vol. 176(1), pages 80-91.
    8. Pauwels, Laurent L. & Vasnev, Andrey L., 2016. "A note on the estimation of optimal weights for density forecast combinations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 391-397.
    9. Laurent L. Pauwels & Andrey L. Vasnev, 2017. "Forecast combination for discrete choice models: predicting FOMC monetary policy decisions," Empirical Economics, Springer, vol. 52(1), pages 229-254, February.
    10. Kim, Hyeongwoo & Shi, Wen, 2018. "The determinants of the benchmark interest rates in China," Journal of Policy Modeling, Elsevier, vol. 40(2), pages 395-417.
    11. Eric Girardin & Sandrine Lunven & Guonan Ma, 2014. "Inflation and China's monetary policy reaction function: 2002-2013," BIS Papers chapters, in: Bank for International Settlements (ed.), Globalisation, inflation and monetary policy in Asia and the Pacific, volume 77, pages 159-170, Bank for International Settlements.
    12. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    13. Eric Girardin & Sandrine Lunven & Guonan Ma, 2017. "China's evolving monetary policy rule: from inflation-accommodating to anti-inflation policy," BIS Working Papers 641, Bank for International Settlements.
    14. Armin Seibert & Andrei Sirchenko & Gernot Muller, 2018. "A Model for Policy Interest Rates," HSE Working papers WP BRP 192/EC/2018, National Research University Higher School of Economics.
    15. Dick van Dijk & Robin L. Lumsdaine & Michel van der Wel, 2014. "Market Set-Up in Advance of Federal Reserve Policy Decisions," NBER Working Papers 19814, National Bureau of Economic Research, Inc.

  9. Kraay, Aart & Monokroussos, George, 1999. "Growth forecasts using time series and growth models," Policy Research Working Paper Series 2224, The World Bank.

    Cited by:

    1. David E. Bloom & David Canning & Günther Fink & Jocelyn E. Finlay, 2007. "Does Age Structure Forecast Economic Growth?," NBER Working Papers 13221, National Bureau of Economic Research, Inc.
    2. Qin, Duo & Cagas, Marie Anne & Ducanes, Geoffrey & Magtibay-Ramos, Nedelyn & Quising, Pilipinas, 2008. "Automatic leading indicators versus macroeconometric structural models: A comparison of inflation and GDP growth forecasting," International Journal of Forecasting, Elsevier, vol. 24(3), pages 399-413.
    3. Ianchovichina, Elena & Kacker, Pooja, 2005. "Growth trends in the developing world : country forecasts and determinants," Policy Research Working Paper Series 3775, The World Bank.
    4. Ahlburg, Dennis & Lindh, Thomas, 2007. "Long-run income forecasting," International Journal of Forecasting, Elsevier, vol. 23(4), pages 533-538.

Articles

  1. Langedijk, Sven & Monokroussos, George & Papanagiotou, Evangelia, 2018. "Benchmarking liquidity proxies: The case of EU sovereign bonds," International Review of Economics & Finance, Elsevier, vol. 56(C), pages 321-329.

    Cited by:

    1. Kang-Soek Lee, 2020. "Macroprudential stress testing: A proposal for the Luxembourg investment fund sector," BCL working papers 141, Central Bank of Luxembourg.

  2. Kajal Lahiri & George Monokroussos & Yongchen Zhao, 2016. "Forecasting Consumption: the Role of Consumer Confidence in Real Time with many Predictors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1254-1275, November.
    See citations under working paper version above.
  3. Lahiri, Kajal & Monokroussos, George & Zhao, Yongchen, 2013. "The yield spread puzzle and the information content of SPF forecasts," Economics Letters, Elsevier, vol. 118(1), pages 219-221.
    See citations under working paper version above.
  4. George Monokroussos, 2013. "A Classical MCMC Approach to the Estimation of Limited Dependent Variable Models of Time Series," Computational Economics, Springer;Society for Computational Economics, vol. 42(1), pages 71-105, June.
    See citations under working paper version above.
  5. Lahiri, Kajal & Monokroussos, George, 2013. "Nowcasting US GDP: The role of ISM business surveys," International Journal of Forecasting, Elsevier, vol. 29(4), pages 644-658.
    See citations under working paper version above.
  6. George Monokroussos, 2011. "Dynamic Limited Dependent Variable Modeling and U.S. Monetary Policy," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 43, pages 519-534, March.
    See citations under working paper version above.

More information

Research fields, statistics, top rankings, if available.

Statistics

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Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 11 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-MAC: Macroeconomics (6) 2005-11-19 2006-07-15 2011-11-28 2015-07-25 2016-01-03 2020-02-17. Author is listed
  2. NEP-FOR: Forecasting (4) 2012-06-25 2012-10-06 2015-07-25 2016-01-03
  3. NEP-ECM: Econometrics (3) 2006-07-15 2009-11-07 2016-01-03
  4. NEP-ETS: Econometric Time Series (2) 2009-11-07 2016-01-03
  5. NEP-MST: Market Microstructure (2) 2015-02-11 2017-08-27
  6. NEP-BEC: Business Economics (1) 2011-11-28
  7. NEP-BIG: Big Data (1) 2020-02-17
  8. NEP-CBA: Central Banking (1) 2005-11-19
  9. NEP-FMK: Financial Markets (1) 2006-07-15
  10. NEP-HIS: Business, Economic & Financial History (1) 2005-11-19
  11. NEP-MON: Monetary Economics (1) 2005-11-19
  12. NEP-ORE: Operations Research (1) 2020-02-17

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