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Frédéric Karamé
(Frederic Karame)

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. Adjemian, Stéphane & Bastani, Houtan & Juillard, Michel & Karamé, Fréderic & Mihoubi, Ferhat & Mutschler, Willi & Pfeifer, Johannes & Ratto, Marco & Rion, Normann & Villemot, Sébastien, 2022. "Dynare: Reference Manual Version 5," Dynare Working Papers 72, CEPREMAP, revised Mar 2023.
    • Stéphane Adjemian & Houtan Bastani & Michel Juillard & Frédéric Karamé & Ferhat Mihoubi & Willi Mutschler & Johannes Pfeifer & Marco Ratto & Sébastien Villemot & Normann Rion, 2023. "Dynare: Reference Manual Version 5," PSE Working Papers hal-04219920, HAL.
    • Stéphane Adjemian & Houtan Bastani & Michel Juillard & Frédéric Karamé & Ferhat Mihoubi & Willi Mutschler & Johannes Pfeifer & Marco Ratto & Sébastien Villemot & Normann Rion, 2023. "Dynare: Reference Manual Version 5," Working Papers hal-04219920, HAL.

    Cited by:

    1. Gantert, Konstantin, 2022. "The Impact of Active Aggregate Demand on Utilization-Adjusted TFP," VfS Annual Conference 2022 (Basel): Big Data in Economics 264103, Verein für Socialpolitik / German Economic Association.
    2. Ferri, Javier & Herranz-Baez, Francisca, 2024. "Government expenditure and the housing puzzle: Unpacking mechanisms," Economic Modelling, Elsevier, vol. 140(C).
    3. Gantert, Konstantin, 2022. "The impact of active aggregate demand on utilisation-adjusted TFP," IWH Discussion Papers 9/2022, Halle Institute for Economic Research (IWH).
    4. William Gatt, 2022. "MEDSEA-FIN: an estimated DSGE model with housing and financial frictions for Malta," CBM Working Papers WP/05/2022, Central Bank of Malta.
    5. Guljanov, Gaygysyz & Mutschler, Willi & Trede, Mark, 2022. "Pruned Skewed Kalman Filter and Smoother: With Application to the Yield Curve," Dynare Working Papers 78, CEPREMAP.
    6. del Río, Fernando & Lores, Francisco-Xavier, 2023. "Accounting for the role of investment frictions in recessions," MPRA Paper 116024, University Library of Munich, Germany.

  2. Adjemian, Stéphane & Karamé, Frédéric & Langot, François, 2021. "Nonlinearities and Workers’ Heterogeneity in Unemployment Dynamics," Dynare Working Papers 71, CEPREMAP.

    Cited by:

    1. Kandoussi, Malak & Langot, François, 2020. "Uncertainty Shocks and Unemployment Dynamics," IZA Discussion Papers 13438, Institute of Labor Economics (IZA).

  3. Frédéric Karamé, 2018. "A new particle filtering approach to estimate stochastic volatility models with Markov-switching," Post-Print hal-02296093, HAL.

    Cited by:

    1. Feng, Jingxue & Wang, Liangliang, 2024. "A switching state-space transmission model for tracking epidemics and assessing interventions," Computational Statistics & Data Analysis, Elsevier, vol. 197(C).
    2. Bermudez, P. de Zea & Marín, J. Miguel & Rue, Håvard & Veiga, Helena, 2024. "Integrated nested Laplace approximations for threshold stochastic volatility models," Econometrics and Statistics, Elsevier, vol. 30(C), pages 15-35.

  4. Frédéric Karamé, 2015. "Asymmetries and Markov-switching structural VAR," Post-Print hal-02296101, HAL.

    Cited by:

    1. Cavicchioli, Maddalena, 2024. "A matrix unified framework for deriving various impulse responses in Markov switching VAR: Evidence from oil and gas markets," The Journal of Economic Asymmetries, Elsevier, vol. 29(C).
    2. Erik Kole & Dick van Dijk, 2022. "Moments, Shocks and Spillovers in Markov-switching VAR Models," Tinbergen Institute Discussion Papers 21-080/III, Tinbergen Institute, revised 11 Jan 2022.
    3. Markku Lanne & Henri Nyberg, 2016. "Generalized Forecast Error Variance Decomposition for Linear and Nonlinear Multivariate Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(4), pages 595-603, August.
    4. Maddalena Cavicchioli, 2021. "OLS Estimation of Markov switching VAR models: asymptotics and application to energy use," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(3), pages 431-449, September.
    5. Koursaros, Demetris & Michail, Nektarios & Savva, Christos, 2024. "Examining the behaviour of inflation to supply and demand shocks using an MS-VAR model," Economic Modelling, Elsevier, vol. 141(C).

  5. Frédéric Karamé, 2012. "An Algorithm for Generalized Impulse-Response Functions in Markov-Switching Structural VAR," Documents de recherche 12-04, Centre d'Études des Politiques Économiques (EPEE), Université d'Evry Val d'Essonne.

    Cited by:

    1. Cavicchioli, Maddalena, 2024. "A matrix unified framework for deriving various impulse responses in Markov switching VAR: Evidence from oil and gas markets," The Journal of Economic Asymmetries, Elsevier, vol. 29(C).
    2. Frédéric Karamé, 2015. "Asymmetries and Markov-switching structural VAR," Post-Print hal-02296101, HAL.
    3. Erik Kole & Dick van Dijk, 2022. "Moments, Shocks and Spillovers in Markov-switching VAR Models," Tinbergen Institute Discussion Papers 21-080/III, Tinbergen Institute, revised 11 Jan 2022.
    4. Huang, Shupei & An, Haizhong & Wen, Shaobo & An, Feng, 2017. "Revisiting driving factors of oil price shocks across time scales," Energy, Elsevier, vol. 139(C), pages 617-629.
    5. Cavicchioli, Maddalena, 2023. "Impulse response function analysis for Markov switching var models," Economics Letters, Elsevier, vol. 232(C).
    6. Thomas St rdal Gundersen & Even Soltvedt Hvinden, 2021. "OPEC's crude game: Strategic Competition and Regime-switching in Global Oil Markets," Working Papers No 01/2021, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    7. Markku Lanne & Henri Nyberg, 2016. "Generalized Forecast Error Variance Decomposition for Linear and Nonlinear Multivariate Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(4), pages 595-603, August.
    8. Markku Lanne & Henri Nyberg, 2015. "Nonlinear dynamic interrelationships between real activity and stock returns," CREATES Research Papers 2015-36, Department of Economics and Business Economics, Aarhus University.

  6. Frédéric Karamé & Yannick Fondeur, 2012. "Can Google Data Help Predict French Youth Unemployment?," Documents de recherche 12-03, Centre d'Études des Politiques Économiques (EPEE), Université d'Evry Val d'Essonne.

    Cited by:

    1. Gutiérrez, Antonio, 2023. "La brecha de género en el emprendimiento y la cultura emprendedora: Evidencia con Google Trends [Entrepreneurship gender gap and entrepreneurial culture: Evidence from Google Trends]," MPRA Paper 115876, University Library of Munich, Germany.
    2. Mihaela Simionescu & Javier Cifuentes-Faura, 2022. "Forecasting National and Regional Youth Unemployment in Spain Using Google Trends," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 164(3), pages 1187-1216, December.
    3. Zhongchen Song & Tom Coupé, 2022. "Predicting Chinese consumption series with Baidu," Working Papers in Economics 22/19, University of Canterbury, Department of Economics and Finance.
    4. Palma Lampreia Dos Santos, Maria José, 2018. "Nowcasting and forecasting aquaponics by Google Trends in European countries," Technological Forecasting and Social Change, Elsevier, vol. 134(C), pages 178-185.
    5. Tuhkuri, Joonas, 2016. "Forecasting Unemployment with Google Searches," ETLA Working Papers 35, The Research Institute of the Finnish Economy.
    6. Agnese Carella & Federica Ciocchetta & Valentina Michelangeli & Federico Maria Signoretti, 2020. "What can we learn about mortgage supply from online data?," Questioni di Economia e Finanza (Occasional Papers) 583, Bank of Italy, Economic Research and International Relations Area.
    7. Costa, Eduardo André & Silva, Maria Eduarda & Galvão, Ana Beatriz, 2024. "Real-time nowcasting the monthly unemployment rates with daily Google Trends data," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
    8. Park, Sungjun & Kim, Jinsoo, 2018. "The effect of interest in renewable energy on US household electricity consumption: An analysis using Google Trends data," Renewable Energy, Elsevier, vol. 127(C), pages 1004-1010.
    9. Böhme, Marcus H. & Gröger, André & Stöhr, Tobias, 2020. "Searching for a better life: Predicting international migration with online search keywords," Journal of Development Economics, Elsevier, vol. 142(C).
    10. Georgios Bampinas & Theodore Panagiotidis & Christina Rouska, 2018. "Volatility persistence and asymmetry under the microscope: The role of information demand for gold and oil," Working Paper series 18-13, Rimini Centre for Economic Analysis.
    11. Grechyna, Daryna, 2025. "Raising awareness of climate change: Nature, activists, politicians?," Ecological Economics, Elsevier, vol. 227(C).
    12. Pietro Giorgio Lovaglio & Mario Mezzanzanica & Emilio Colombo, 2020. "Comparing time series characteristics of official and web job vacancy data," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(1), pages 85-98, February.
    13. Clément Bortoli & Stéphanie Combes & Thomas Renault, 2018. "Nowcasting GDP Growth by Reading Newspapers," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-03205161, HAL.
    14. Monge, Manuel & Claudio-Quiroga, Gloria & Poza, Carlos, 2024. "Chinese economic behavior in times of covid-19. A new leading economic indicator based on Google trends," International Economics, Elsevier, vol. 177(C).
    15. Mihaela Simionescu & Dalia Streimikiene & Wadim Strielkowski, 2020. "What Does Google Trends Tell Us about the Impact of Brexit on the Unemployment Rate in the UK?," Sustainability, MDPI, vol. 12(3), pages 1-10, January.
    16. Liwen Ling & Dabin Zhang & Shanying Chen & Amin W. Mugera, 2020. "Can online search data improve the forecast accuracy of pork price in China?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(4), pages 671-686, July.
    17. Maas, Benedikt, 2019. "Short-term forecasting of the US unemployment rate," MPRA Paper 94066, University Library of Munich, Germany.
    18. Nikolaos Askitas & Klaus F. Zimmermann, 2015. "The internet as a data source for advancement in social sciences," International Journal of Manpower, Emerald Group Publishing Limited, vol. 36(1), pages 2-12, April.
    19. Perroni, Carlo & Scharf, Kimberley & Talavera, Oleksandr & Vi, Linh, 2021. "Online Salience and Charitable Giving : Evidence from SMS Donations," The Warwick Economics Research Paper Series (TWERPS) 1325, University of Warwick, Department of Economics.
    20. Dimitrios Anastasiou & Zacharias Bragoudakis & Stelios Giannoulakis, 2020. "Perceived vs actual financial crisis and bank credit standards: is there any indication of self-fulfilling prophecy?," Working Papers 277, Bank of Greece.
    21. Nakamura, Nobuyuki & Suzuki, Aya, 2021. "COVID-19 and the intentions to migrate from developing countries: Evidence from online search activities in Southeast Asia," Journal of Asian Economics, Elsevier, vol. 76(C).
    22. Nuno Barreira & Pedro Godinho & Paulo Melo, 2013. "Nowcasting unemployment rate and new car sales in south-western Europe with Google Trends," Netnomics, Springer, vol. 14(3), pages 129-165, November.
    23. Erik Christian Montes Schütte, 2018. "In Search of a Job: Forecasting Employment Growth in the US using Google Trends," CREATES Research Papers 2018-25, Department of Economics and Business Economics, Aarhus University.
    24. Perroni, Carlo & Scharf, Kimberley & Talavera, Oleksandr & Vi, Linh, 2022. "Does Online Salience Predict Charitable Giving? Evidence from SMS Text Donations," CEPR Discussion Papers 17030, C.E.P.R. Discussion Papers.
    25. Havranek, Tomas & Zeynalov, Ayaz, 2018. "Forecasting Tourist Arrivals with Google Trends and Mixed Frequency Data," EconStor Preprints 187420, ZBW - Leibniz Information Centre for Economics.
    26. Andreea Avramescu & Arkadiusz Wiśniowski, 2021. "Now-casting Romanian migration into the United Kingdom by using Google Search engine data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 45(40), pages 1219-1254.
    27. Necmettin Alpay Koçak, 2020. "The Role of Ecb Speeches in Nowcasting German Gdp," European Financial and Accounting Journal, Prague University of Economics and Business, vol. 2020(2), pages 05-20.
    28. González-Fernández, Marcos & González-Velasco, Carmen, 2018. "Can Google econometrics predict unemployment? Evidence from Spain," Economics Letters, Elsevier, vol. 170(C), pages 42-45.
    29. Daniel Borup & Erik Christian Montes Schütte, 2019. "In search of a job: Forecasting employment growth using Google Trends," CREATES Research Papers 2019-13, Department of Economics and Business Economics, Aarhus University.
    30. Havranek, Tomas & Zeynalov, Ayaz, 2018. "Forecasting Tourist Arrivals: Google Trends Meets Mixed Frequency Data," MPRA Paper 90205, University Library of Munich, Germany.
    31. Siliverstovs, Boriss & Wochner, Daniel S., 2018. "Google Trends and reality: Do the proportions match?," Journal of Economic Behavior & Organization, Elsevier, vol. 145(C), pages 1-23.
    32. Jianchun Fang & Wanshan Wu & Zhou Lu & Eunho Cho, 2019. "Using Baidu Index To Nowcast Mobile Phone Sales In China," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 64(01), pages 83-96, March.
    33. Alina Stundziene & Vaida Pilinkiene & Jurgita Bruneckiene & Andrius Grybauskas & Mantas Lukauskas & Irena Pekarskiene, 2024. "Future directions in nowcasting economic activity: A systematic literature review," Journal of Economic Surveys, Wiley Blackwell, vol. 38(4), pages 1199-1233, September.
    34. Francesco D'Amuri & Juri Marcucci, 2012. "The predictive power of Google searches in forecasting unemployment," Temi di discussione (Economic working papers) 891, Bank of Italy, Economic Research and International Relations Area.
    35. David Coble & Pablo Pincheira, 2021. "Forecasting building permits with Google Trends," Empirical Economics, Springer, vol. 61(6), pages 3315-3345, December.
    36. Caperna, Giulio & Colagrossi, Marco & Geraci, Andrea & Mazzarella, Gianluca, 2020. "Googling Unemployment During the Pandemic: Inference and Nowcast Using Search Data," JRC Working Papers in Economics and Finance 2020-04, Joint Research Centre, European Commission.
    37. González-Fernández, Marcos & González-Velasco, Carmen, 2020. "An alternative approach to predicting bank credit risk in Europe with Google data," Finance Research Letters, Elsevier, vol. 35(C).
    38. Zeynalov, Ayaz, 2014. "Nowcasting Tourist Arrivals to Prague: Google Econometrics," MPRA Paper 60945, University Library of Munich, Germany.
    39. Kohns, David & Bhattacharjee, Arnab, 2023. "Nowcasting growth using Google Trends data: A Bayesian Structural Time Series model," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1384-1412.
    40. Zeynalov, Ayaz, 2017. "Forecasting Tourist Arrivals in Prague: Google Econometrics," MPRA Paper 83268, University Library of Munich, Germany.
    41. Dimitrios Anastasiou & Konstantinos Drakos, 2021. "Nowcasting the Greek (semi‐) deposit run: Hidden uncertainty about the future currency in a Google search," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 1133-1150, January.
    42. Simionescu, Mihaela & Zimmermann, Klaus F., 2017. "Big Data and Unemployment Analysis," GLO Discussion Paper Series 81, Global Labor Organization (GLO).
    43. Ronald MacDonald & Xuxin Mao, 2015. "An Alternative way of Predicting the Outcome of the Scottish Independence Referendum: The Information in the Ether," SIRE Discussion Papers 2015-69, Scottish Institute for Research in Economics (SIRE).
    44. Simionescu, Mihaela & Raišienė, Agota Giedrė, 2021. "A bridge between sentiment indicators: What does Google Trends tell us about COVID-19 pandemic and employment expectations in the EU new member states?," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    45. Mihaela, Simionescu, 2020. "Improving unemployment rate forecasts at regional level in Romania using Google Trends," Technological Forecasting and Social Change, Elsevier, vol. 155(C).
    46. Nicolas Woloszko, 2020. "Tracking activity in real time with Google Trends," OECD Economics Department Working Papers 1634, OECD Publishing.
    47. Gutiérrez, Antonio, 2022. "Movilidad urbana y datos de alta frecuencia [Urban mobility and high frequency data]," MPRA Paper 114854, University Library of Munich, Germany.
    48. González-Fernández, Marcos & González-Velasco, Carmen, 2020. "A sentiment index to measure sovereign risk using Google data," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 406-418.
    49. María Gil & Javier J. Pérez & A. Jesús Sánchez & Alberto Urtasun, 2018. "Nowcasting private consumption: traditional indicators, uncertainty measures, credit cards and some internet data," Working Papers 1842, Banco de España.
    50. Caperna, Giulio & Colagrossi, Marco & Geraci, Andrea & Mazzarella, Gianluca, 2022. "A babel of web-searches: Googling unemployment during the pandemic," Labour Economics, Elsevier, vol. 74(C).
    51. Pavlicek, Jaroslav & Kristoufek, Ladislav, 2015. "Nowcasting unemployment rates with Google searches: Evidence from the Visegrad Group countries," FinMaP-Working Papers 34, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    52. Juan Camilo Anzoátegui-Zapata & Juan Camilo Galvis-Ciro, 2020. "Disagreements in Consumer Inflation Expectations: Empirical Evidence for a Latin American Economy," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 16(2), pages 99-122, November.
    53. VAN DER WIELEN Wouter & BARRIOS Salvador, 2020. "Fear and Employment During the COVID Pandemic: Evidence from Search Behaviour in the EU," JRC Working Papers on Taxation & Structural Reforms 2020-08, Joint Research Centre.
    54. Ken Wong & Max Kwong & Paul Luk & Michael Cheng, 2023. "A robust textual analysis of the dynamics of Hong Kong property market," Pacific Economic Review, Wiley Blackwell, vol. 28(3), pages 314-346, August.
    55. Blazquez, Desamparados & Domenech, Josep, 2018. "Big Data sources and methods for social and economic analyses," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 99-113.
    56. Marcos González-Fernández & Carmen González-Velasco, 2019. "An approach to predict Spanish mortgage market activity using Google data," Economics and Business Letters, Oviedo University Press, vol. 8(4), pages 209-214.
    57. Jaroslav Pavlicek & Ladislav Kristoufek, 2014. "Can Google searches help nowcast and forecast unemployment rates in the Visegrad Group countries?," Papers 1408.6639, arXiv.org.
    58. Vicente, María Rosalía & López-Menéndez, Ana J. & Pérez, Rigoberto, 2015. "Forecasting unemployment with internet search data: Does it help to improve predictions when job destruction is skyrocketing?," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 132-139.
    59. Gupta, Kartick & Banerjee, Rajabrata, 2019. "Does OPEC news sentiment influence stock returns of energy firms in the United States?," Energy Economics, Elsevier, vol. 77(C), pages 34-45.
    60. Anastasiou, Dimitrios & Drakos, Konstantinos, 2021. "European depositors’ behavior and crisis sentiment," Journal of Economic Behavior & Organization, Elsevier, vol. 184(C), pages 117-136.
    61. Rodrigo Mulero & Alfredo García-Hiernaux, 2021. "Forecasting Spanish unemployment with Google Trends and dimension reduction techniques," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 12(3), pages 329-349, September.
    62. Jakub Szymkowiak & Lechosław Kuczyński, 2015. "Avoiding predators in a fluctuating environment: responses of the wood warbler to pulsed resources," Behavioral Ecology, International Society for Behavioral Ecology, vol. 26(2), pages 601-608.
    63. Mahalia Jackman & Simon Naitram, 2015. "Research Note: Nowcasting Tourist Arrivals in Barbados – Just Google it!," Tourism Economics, , vol. 21(6), pages 1309-1313, December.
    64. Alessia Naccarato & Andrea Pierini & Stefano Falorsi, 2015. "Using Google Trend Data To Predict The Italian Unemployment Rate," Departmental Working Papers of Economics - University 'Roma Tre' 0203, Department of Economics - University Roma Tre.
    65. Naccarato, Alessia & Falorsi, Stefano & Loriga, Silvia & Pierini, Andrea, 2018. "Combining official and Google Trends data to forecast the Italian youth unemployment rate," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 114-122.
    66. Cristea, R. G., 2020. "Can Alternative Data Improve the Accuracy of Dynamic Factor Model Nowcasts?," Cambridge Working Papers in Economics 20108, Faculty of Economics, University of Cambridge.
    67. Branislav Saxa, 2014. "Forecasting Mortgages: Internet Search Data as a Proxy for Mortgage Credit Demand," Working Papers 2014/14, Czech National Bank.
    68. Tuhkuri, Joonas, 2016. "ETLAnow: A Model for Forecasting with Big Data – Forecasting Unemployment with Google Searches in Europe," ETLA Reports 54, The Research Institute of the Finnish Economy.
    69. Simionescu, Mihaela & Cifuentes-Faura, Javier, 2022. "Can unemployment forecasts based on Google Trends help government design better policies? An investigation based on Spain and Portugal," Journal of Policy Modeling, Elsevier, vol. 44(1), pages 1-21.
    70. Botezat, Alina, 2017. "Austerity plan announcements and the impact on the employees’ wellbeing," Journal of Economic Psychology, Elsevier, vol. 63(C), pages 1-16.
    71. Afkhami, Mohamad & Ghoddusi, Hamed & Rafizadeh, Nima, 2021. "Google Search Explains Your Gasoline Consumption!," Energy Economics, Elsevier, vol. 99(C).
    72. Gulsah Senturk, 2022. "Can Google Search Data Improve the Unemployment Rate Forecasting Model? An Empirical Analysis for Turkey," Journal of Economic Policy Researches, Istanbul University, Faculty of Economics, vol. 9(2), pages 229-244, July.
    73. Alberto Urtasun & Mara Gil & Javier J. Perez, 2017. "Nowcasting private consumption: traditional indicators, uncertainty measures, and the role of internet search query data," EcoMod2017 10745, EcoMod.
    74. Ladislav Kristoufek, 2013. "Can Google Trends search queries contribute to risk diversification?," Papers 1310.1444, arXiv.org.
    75. Voraprapa Nakavachara & Nuarpear Lekfuangfu, 2017. "Predicting the Present Revisited: The Case of Thailand," PIER Discussion Papers 70, Puey Ungphakorn Institute for Economic Research.
    76. KOCAK, Necmettin Alpay, 2021. "The Impacts Of Speeches On Nowcasting Gdp: A Case Study On Euro Area Markets," Studii Financiare (Financial Studies), Centre of Financial and Monetary Research "Victor Slavescu", vol. 25(1), pages 6-29, March.
    77. Shin, Sunny Y. & McKenzie, Jordi & Crosby, Paul, 2024. "Cultural affinity and international trade in motion pictures: Empirical evidence using categorised internet search activity," Economic Modelling, Elsevier, vol. 136(C).
    78. Rodrigo Mulero & Alfredo Garcia-Hiernaux, 2023. "Forecasting unemployment with Google Trends: age, gender and digital divide," Empirical Economics, Springer, vol. 65(2), pages 587-605, August.
    79. Clément Cariou & Amélie Charles & Olivier Darné, 2024. "Are national or regional surveys useful for nowcasting regional jobseekers? The case of the French region of Pays‐de‐la‐Loire," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 2341-2357, September.

  7. Adjemian, Stéphane & Bastani, Houtan & Juillard, Michel & Karamé, Fréderic & Maih, Junior & Mihoubi, Ferhat & Mutschler, Willi & Perendia, George & Pfeifer, Johannes & Ratto, Marco & Villemot, Sébasti, 2011. "Dynare: Reference Manual Version 4," Dynare Working Papers 1, CEPREMAP, revised Mar 2021.

    Cited by:

    1. Christoph Görtz & John D. Tsoukalas, 2013. "Sector Specific News Shocks in Aggregate and Sectoral Fluctuations," CESifo Working Paper Series 4269, CESifo.
    2. Paul Kitney, 2015. "Does the central bank respond to credit market factors? A Bayesian DSGE approach," CAMA Working Papers 2015-21, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    3. Federico Di Pace & Matthias S. Hertweck, 2012. "Labour Market Frictions, Monetary Policy and Durable Goods," Working Paper Series of the Department of Economics, University of Konstanz 2012-09, Department of Economics, University of Konstanz.
    4. Gelain, Paolo & Guerrazzi, Marco, 2014. "A demand-driven search model with self-fulfilling expectations: The new `Farmerian' framework under scrutiny," MPRA Paper 55773, University Library of Munich, Germany.
    5. Anella Munro, 2014. "Exchange rates, expected returns and risk: UIP unbound," CAMA Working Papers 2014-73, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    6. SENBETA, Sisay Regassa, 2013. "Foreign exchange constraints and macroeconomic dynamics in a small open economy," Working Papers 2013023, University of Antwerp, Faculty of Business and Economics.
    7. Guo, Lingyi & Xu, Wenli & Xu, Kun, 2016. "中国财政政策调整的宏观经济效应——基于消费者异质性的新凯恩斯模型 [The effect of Fiscal Consolidations on Chinese Maroeconomies——Based on The NK Model with Cosummer’s Heterogenicity]," MPRA Paper 69170, University Library of Munich, Germany.
    8. Nida Çakır Melek & Michael D. Plante & Mine K. Yücel, 2017. "The U.S. Shale Oil Boom, the Oil Export Ban, and the Economy: A General Equilibrium Analysis," Working Papers 1708, Federal Reserve Bank of Dallas.
    9. Angelo M. Fasolo, 2011. "The Accuracy of Perturbation Methods to Solve Small Open Economy Models," Working Papers Series 262, Central Bank of Brazil, Research Department.
    10. Meyer-Gohde, Alexander, 2014. "Risky linear approximations," SFB 649 Discussion Papers 2014-034, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    11. Junior Maih, 2015. "Efficient perturbation methods for solving regime-switching DSGE models," Working Paper 2015/01, Norges Bank.
    12. Tom Holden & Michael Paetz, 2012. "Efficient simulation of DSGE models with inequality constraints," School of Economics Discussion Papers 1612, School of Economics, University of Surrey.
    13. Konstantinos Angelopoulos & Stylianos Asimakopoulos & James Malley, 2014. "Optimal progressive taxation in a model with endogenous skill supply," Working Papers 2014_07, Business School - Economics, University of Glasgow.
    14. Fabio Verona & Maik H. Wolters, 2013. "Sticky Information Models in Dynare," CEF.UP Working Papers 1306, Universidade do Porto, Faculdade de Economia do Porto.
    15. Marc Pourroy & Benjamin Carton & Dramane Coulibaly, 2013. "Food Prices and Inflation Targeting in Emerging Economies," EconomiX Working Papers 2013-7, University of Paris Nanterre, EconomiX.
    16. in 't Veld, Jan & Kollmann, Robert & Pataracchia, Beatrice & Ratto, Marco & Roeger, Werner, 2014. "International capital flows and the boom-bust cycle in Spain," Journal of International Money and Finance, Elsevier, vol. 48(PB), pages 314-335.
    17. Simon Gilchrist & Raphael Schoenle & Jae W. Sim & Egon Zakrajšek, 2015. "Inflation Dynamics During the Financial Crisis," Finance and Economics Discussion Series 2015-12, Board of Governors of the Federal Reserve System (U.S.).
    18. Viktors Ajevskis, 2013. "Non-Local Solutions to Dynamic Equilibrium Models: the Approximate Stable Manifolds Approach," Working Papers 2013/03, Latvijas Banka.
    19. Gr gory Levieuge & Jean-Guillaume Sahuc, 2021. "Downward Interest Rate Rigidity," Working papers 828, Banque de France.
    20. Hall, Jamie, 2012. "Rapid estimation of nonlinear DSGE models," MPRA Paper 41218, University Library of Munich, Germany.
    21. Kaszab, Lorant & Marsal, Ales, 2013. "Fiscal Policy and the Nominal Term Premium," Cardiff Economics Working Papers E2013/13, Cardiff University, Cardiff Business School, Economics Section.
    22. Kenneth L. Judd & Lilia Maliar & Serguei Maliar, 2017. "Lower Bounds on Approximation Errors to Numerical Solutions of Dynamic Economic Models," Econometrica, Econometric Society, vol. 85, pages 991-1012, May.
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    89. Alain Gabler, 2014. "Relative Price Fluctuations in a Two-Sector Model with Imperfect Competition," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 17(3), pages 474-483, July.
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  8. Frédéric Karamé, 2010. "Impulse-Response Functions in Markov-Switching Structural Vector AutoRegressions: a Step Further," Documents de recherche 10-03, Centre d'Études des Politiques Économiques (EPEE), Université d'Evry Val d'Essonne.

    Cited by:

    1. Cavicchioli, Maddalena, 2024. "A matrix unified framework for deriving various impulse responses in Markov switching VAR: Evidence from oil and gas markets," The Journal of Economic Asymmetries, Elsevier, vol. 29(C).
    2. Pérez-Quirós, Gabriel & Camacho, Máximo, 2013. "Commodity prices and the business cycle in Latin America: Living and dying by commodities?," CEPR Discussion Papers 9367, C.E.P.R. Discussion Papers.
    3. Frédéric Karamé, 2015. "Asymmetries and Markov-switching structural VAR," Post-Print hal-02296101, HAL.
    4. James D. Hamilton, 2016. "Macroeconomic Regimes and Regime Shifts," NBER Working Papers 21863, National Bureau of Economic Research, Inc.
    5. Erik Kole & Dick van Dijk, 2022. "Moments, Shocks and Spillovers in Markov-switching VAR Models," Tinbergen Institute Discussion Papers 21-080/III, Tinbergen Institute, revised 11 Jan 2022.
    6. Cavicchioli, Maddalena, 2023. "Impulse response function analysis for Markov switching var models," Economics Letters, Elsevier, vol. 232(C).
    7. Frédéric Karamé, 2012. "An Algorithm for Generalized Impulse-Response Functions in Markov-Switching Structural VAR," Documents de recherche 12-04, Centre d'Études des Politiques Économiques (EPEE), Université d'Evry Val d'Essonne.

  9. Frédéric Karamé & Alexandra Olmedo, 2010. "Asymmetric Properties of Impulse Response Functions in Markov-Switching Structural Vector AutoRegressions," Documents de recherche 10-04, Centre d'Études des Politiques Économiques (EPEE), Université d'Evry Val d'Essonne.

    Cited by:

    1. Pérez-Quirós, Gabriel & Camacho, Máximo, 2013. "Commodity prices and the business cycle in Latin America: Living and dying by commodities?," CEPR Discussion Papers 9367, C.E.P.R. Discussion Papers.
    2. Frédéric Karamé, 2015. "Asymmetries and Markov-switching structural VAR," Post-Print hal-02296101, HAL.
    3. Aydanur GACENER-ATIŞ & Deniz ERER, 2019. "Effects of Capital Flows on Carry Trade Activities: The Case of TurkeyAbstract: Carry trade is described as the capital flow coming into a country based on interest rate differential. A negative chang," Sosyoekonomi Journal, Sosyoekonomi Society, issue 27(42).
    4. James D. Hamilton, 2016. "Macroeconomic Regimes and Regime Shifts," NBER Working Papers 21863, National Bureau of Economic Research, Inc.
    5. Erik Kole & Dick van Dijk, 2022. "Moments, Shocks and Spillovers in Markov-switching VAR Models," Tinbergen Institute Discussion Papers 21-080/III, Tinbergen Institute, revised 11 Jan 2022.

  10. Frédéric Karamé & Lise Patureau & Thepthida Sopraseuth, 2003. "Limited Participation and Exchange Rate Dynamics: Does Theory Meet the Data?," Documents de recherche 03-15, Centre d'Études des Politiques Économiques (EPEE), Université d'Evry Val d'Essonne.

    Cited by:

    1. de Blas, Beatriz, 2010. "Exchange rate dynamics in economies with portfolio rigidities," International Review of Economics & Finance, Elsevier, vol. 19(3), pages 366-382, June.
    2. Diego E. Vacaflores, 2013. "Monetary Transfers in the U.S.: How Efficient Are Tax Rebates?," Economies, MDPI, vol. 1(3), pages 1-23, November.
    3. Albertini, Julien & Lan, Hong, 2016. "The importance of time-varying parameters in new Keynesian models with zero lower bound," SFB 649 Discussion Papers 2016-013, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    4. Sacht, Stephen & Franke, Reiner & Jang, Tae-Seok, 2013. "Moment Matching versus Bayesian Estimation: Backward-Looking Behaviour in a New-Keynesian Baseline Model," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79694, Verein für Socialpolitik / German Economic Association.
    5. Diego E. Vacaflores, 2011. "Monetary Stimulus: Through Wall Street or Main Street?," Revista de Análisis del BCB, Banco Central de Bolivia, vol. 14(1), pages 9-40, June.
    6. Diego E. Vacaflores, 2012. "Remittances, Monetary Policy, and Partial Sterilization," Southern Economic Journal, John Wiley & Sons, vol. 79(2), pages 367-387, October.
    7. Franke, Reiner & Jang, Tae-Seok & Sacht, Stephen, 2011. "Moment matching versus Bayesian estimation: Backward-looking behaviour in the new-Keynesian three-equations model," Economics Working Papers 2011-10, Christian-Albrechts-University of Kiel, Department of Economics.
    8. Reiner Franke, 2018. "Competitive moment matching of a New-Keynesian and an Old-Keynesian model," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 13(2), pages 201-239, July.
    9. Franke, Reiner, 2013. "Competitive Moment Matching of a New-Keynesian and an Old-Keynesian Model," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79988, Verein für Socialpolitik / German Economic Association.

  11. Frédéric Karamé & Lise Patureau & Thepthida Sopraseuth, 2002. "Can We Beat the Random Walk Forecasts of Out-of-Sample Exchange Rates? A Structural Approach," Computing in Economics and Finance 2002 233, Society for Computational Economics.

    Cited by:

    1. Otavio De Medeiros, 2005. "Order Flow and Exchange Rate Dynamics in Brazil," Finance 0503019, University Library of Munich, Germany.

Articles

  1. Karamé, Frédéric, 2018. "A new particle filtering approach to estimate stochastic volatility models with Markov-switching," Econometrics and Statistics, Elsevier, vol. 8(C), pages 204-230.
    See citations under working paper version above.
  2. Karamé, Frédéric, 2015. "Asymmetries and Markov-switching structural VAR," Journal of Economic Dynamics and Control, Elsevier, vol. 53(C), pages 85-102.
    See citations under working paper version above.
  3. Fondeur, Y. & Karamé, F., 2013. "Can Google data help predict French youth unemployment?," Economic Modelling, Elsevier, vol. 30(C), pages 117-125.
    See citations under working paper version above.
  4. Karamé, F., 2012. "An algorithm for generalized impulse-response functions in Markov-switching structural VAR," Economics Letters, Elsevier, vol. 117(1), pages 230-234.
    See citations under working paper version above.
  5. Karamé, F., 2010. "Impulse-response functions in Markov-switching structural vector autoregressions: A step further," Economics Letters, Elsevier, vol. 106(3), pages 162-165, March.
    See citations under working paper version above.
  6. Karamé, Frédéric & Patureau, Lise & Sopraseuth, Thepthida, 2008. "Limited participation and exchange rate dynamics: Does theory meet the data?," Journal of Economic Dynamics and Control, Elsevier, vol. 32(4), pages 1041-1087, April.
    See citations under working paper version above.Sorry, no citations of articles recorded.
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