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Citations for "Thick modeling"

by Granger, Clive W. J. & Jeon, Yongil

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  1. Pesaran, M Hashem & Timmermann, Allan G, 2004. "Real Time Econometrics," CEPR Discussion Papers 4402, C.E.P.R. Discussion Papers.
  2. Michael Fritsch & Alina Sorgner & Michael Wyrwich & Evguenii Zazdravnykh, 2016. "Historical Shocks and Persistence of Economic Activity: Evidence from a Unique Natural Experiment," Papers in Evolutionary Economic Geography (PEEG) 1607, Utrecht University, Section of Economic Geography, revised Apr 2016.
  3. Aiolfi, Marco & Favero, Carlo A., 2003. "Model Uncertainty, Thick Modelling and the Predictability of Stock Returns," CEPR Discussion Papers 3997, C.E.P.R. Discussion Papers.
  4. Arnulfo Rodriguez & Pedro N. Rodriguez, 2006. "Recursive Thick Modeling and the Choice of Monetary Policy in Mexico," Computing in Economics and Finance 2006 30, Society for Computational Economics.
  5. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, Elsevier.
  6. Favero, Carlo A. & Milani, Fabio, 2005. "Parameter Instability, Model Uncertainty and the Choice of Monetary Policy," CEPR Discussion Papers 4909, C.E.P.R. Discussion Papers.
  7. Vanina Forget, 2012. "Doing well and doing good: a multi-dimensional puzzle," Working Papers hal-00672037, HAL.
  8. Marco Aiolfi & Carlos Capistrán & Allan Timmermann, 2010. "Forecast Combinations," Working Papers 2010-04, Banco de México.
  9. David Hendry & Michael P. Clements, 2010. "Forecasting from Mis-specified Models in the Presence of Unanticipated Location Shifts," Economics Series Working Papers 484, University of Oxford, Department of Economics.
  10. Bagliano, Fabio C. & Morana, Claudio, 2014. "Determinants of US financial fragility conditions," Research in International Business and Finance, Elsevier, vol. 30(C), pages 377-392.
  11. M. Hashem Pesaran & Paolo Zaffaroni, 2004. "Model Averaging and Value-at-Risk Based Evaluation of Large Multi Asset Volatility Models for Risk Management," CESifo Working Paper Series 1358, CESifo Group Munich.
  12. Sancetta, Alessio, 2009. "Nearest neighbor conditional estimation for Harris recurrent Markov chains," Journal of Multivariate Analysis, Elsevier, vol. 100(10), pages 2224-2236, November.
  13. Angel J. Ubide & Kevin Ross, 2001. "Mind the Gap; What is the Best Measure of Slack in the Euro Area?," IMF Working Papers 01/203, International Monetary Fund.
  14. Ana Lamo & Javier J. Pérez & Ludger Schuknecht, 2013. "The cyclicality of consumption, wages and employment of the public sector in the euro area," Applied Economics, Taylor & Francis Journals, vol. 45(12), pages 1551-1569, April.
  15. Joshua Gallin & Randal Verbrugge, 2007. "Improving the CPI’s Age-Bias Adjustment: Leverage, Disaggregation and Model Averaging," Working Papers 411, U.S. Bureau of Labor Statistics.
  16. Espasa, Antoni & Albacete, Rebeca, 2004. "Considerations on economic forecasting: method developed in the bulletin of EU and US inflation and macroeconomic analysis," DES - Working Papers. Statistics and Econometrics. WS ws045013, Universidad Carlos III de Madrid. Departamento de Estadística.
  17. Maria Elena Bontempi & Silvia Giannini & Roberto Golinelli, 2005. "Corporate Tax Reforms and Financial Choices: An Empirical Analysis," Giornale degli Economisti, GDE (Giornale degli Economisti e Annali di Economia), Bocconi University, vol. 64(2-3), pages 271-294, November.
  18. Claudio Morana, 2013. "Oil price dynamics, macro-finance interactions and the role of financial speculation," Working Papers 225, University of Milano-Bicocca, Department of Economics, revised Nov 2013.
  19. Christian Pierdzioch & Marian Risse & Sebastian Rohloff, 2016. "Fluctuations of the real exchange rate, real interest rates, and the dynamics of the price of gold in a small open economy," Empirical Economics, Springer, vol. 51(4), pages 1481-1499, December.
  20. Lessmann, Stefan & Sung, Ming-Chien & Johnson, Johnnie E.V. & Ma, Tiejun, 2012. "A new methodology for generating and combining statistical forecasting models to enhance competitive event prediction," European Journal of Operational Research, Elsevier, vol. 218(1), pages 163-174.
  21. Teräsvirta, Timo & van Dijk, Dick & Medeiros, Marcelo, 2004. "Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination," SSE/EFI Working Paper Series in Economics and Finance 561, Stockholm School of Economics, revised 04 Nov 2004.
  22. Rosario Dell'Aquila & Elvezio Ronchetti, 2004. "Stock and Bond Return Predictability : The Discrimination Power of Model Selection Criteria," Research Papers by the Institute of Economics and Econometrics, Geneva School of Economics and Management, University of Geneva 2004.05, Institut d'Economie et Econométrie, Université de Genève.
  23. Fabio C. Bagliano & Claudio Morana, 2010. "The Great Recession: US dynamics and spillovers to the world economy," ICER Working Papers - Applied Mathematics Series 34-2010, ICER - International Centre for Economic Research.
  24. McNelis, Paul & McAdam, Peter, 2004. "Forecasting inflation with thick models and neural networks," Working Paper Series 0352, European Central Bank.
  25. Aiolfi, Marco & Timmermann, Allan, 2006. "Persistence in forecasting performance and conditional combination strategies," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 31-53.
  26. Claudio Morana, 2013. "Insights on the global macro-finance interface: Structural sources of risk factors fluctuations and the cross-section of expected stock returns," Working Papers 264, University of Milano-Bicocca, Department of Economics, revised Dec 2013.
  27. Man, Georg, 2015. "Competition and the growth of nations: International evidence from Bayesian model averaging," Economic Modelling, Elsevier, vol. 51(C), pages 491-501.
  28. Chollete, Loran & Schmeidler, David, 2014. "Demand-Theoretic Approach to Choice of Priors," UiS Working Papers in Economics and Finance 2014/14, University of Stavanger.
  29. Cassola, Nuno & Morana, Claudio, 2010. "Comovements in volatility in the euro money market," Journal of International Money and Finance, Elsevier, vol. 29(3), pages 525-539, April.
  30. Espasa, Antoni & Albacete, Rebeca, 2005. "Forecasting inflation in the euro area using monthly time series models and quarterly econometric models," DES - Working Papers. Statistics and Econometrics. WS ws050401, Universidad Carlos III de Madrid. Departamento de Estadística.
  31. Lamo, Ana & Pérez, Javier J. & Schuknecht, Ludger, 2013. "Are government wages interlinked with private sector wages?," Journal of Policy Modeling, Elsevier, vol. 35(5), pages 697-712.
  32. Fabio Bagliano & Claudio Morana, 2010. "Business cycle comovement in the G-7: common shocks or common transmission mechanisms?," Applied Economics, Taylor & Francis Journals, vol. 42(18), pages 2327-2345.
  33. Haakon O. Aa. Solheim, 2005. "Evaluating Macroeconometric Modelling with Regard to Usefulness: a Survey," Nordic Journal of Political Economy, Nordic Journal of Political Economy, vol. 31, pages 3-15.
  34. Kascha, Christian & Mertens, Karel, 2009. "Business cycle analysis and VARMA models," Journal of Economic Dynamics and Control, Elsevier, vol. 33(2), pages 267-282, February.
  35. Claudio Morana, 2005. "The Japanese deflation: has it had real effects? Could it have been avoided?," Applied Economics, Taylor & Francis Journals, vol. 37(12), pages 1337-1352.
  36. Caldeira, João F. & Moura, Guilherme V. & Santos, André A.P., 2016. "Predicting the yield curve using forecast combinations," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 79-98.
  37. Bagliano, Fabio C. & Morana, Claudio, 2009. "International macroeconomic dynamics: A factor vector autoregressive approach," Economic Modelling, Elsevier, vol. 26(2), pages 432-444, March.
  38. Morales-Arias, Leonardo & Moura, Guilherme V., 2013. "Adaptive forecasting of exchange rates with panel data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 493-509.
  39. M. Hashem Pesaran & Christoph Schleicher & Paolo Zaffaroni, 2008. "Model Averaging in Risk Management with an Application to Futures Markets," CESifo Working Paper Series 2231, CESifo Group Munich.
  40. Dell'Aquila, Rosario & Ronchetti, Elvezio, 2006. "Stock and bond return predictability: the discrimination power of model selection criteria," Computational Statistics & Data Analysis, Elsevier, vol. 50(6), pages 1478-1495, March.
  41. Garner, Thesia I. & Verbrugge, Randal, 2009. "Reconciling user costs and rental equivalence: Evidence from the US consumer expenditure survey," Journal of Housing Economics, Elsevier, vol. 18(3), pages 172-192, September.
  42. Tom Bernhardsen & ØYvind Eitrheim, 2005. "Real-time data for Norway: Output gap revisions and challenges for monetary policy," Computing in Economics and Finance 2005 274, Society for Computational Economics.
  43. Carlo A. Favero, "undated". "Parameters´ Instability, Model Uncertainty and Optimal Monetary Policy," Working Papers 196, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  44. Andrawis, Robert R. & Atiya, Amir F. & El-Shishiny, Hisham, 2011. "Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition," International Journal of Forecasting, Elsevier, vol. 27(3), pages 672-688, July.
  45. Bordignon, Silvano & Bunn, Derek W. & Lisi, Francesco & Nan, Fany, 2013. "Combining day-ahead forecasts for British electricity prices," Energy Economics, Elsevier, vol. 35(C), pages 88-103.
  46. Yang Yang & Tae-Hwy Lee, 2004. "Bagging Binary Predictors for Time Series," Econometric Society 2004 Far Eastern Meetings 512, Econometric Society.
  47. W A Razzak, 2002. "Monetary policy and forecasting inflation with and without the output gap," Reserve Bank of New Zealand Discussion Paper Series DP2002/03, Reserve Bank of New Zealand.
  48. Hsiao, Cheng & Wan, Shui Ki, 2014. "Is there an optimal forecast combination?," Journal of Econometrics, Elsevier, vol. 178(P2), pages 294-309.
  49. Hall, Stephen G. & Mitchell, James, 2007. "Combining density forecasts," International Journal of Forecasting, Elsevier, vol. 23(1), pages 1-13.
  50. Pierdzioch, Christian & Risse, Marian & Rohloff, Sebastian, 2014. "The international business cycle and gold-price fluctuations," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(2), pages 292-305.
  51. Antoni Espasa & Rebeca Albacete, 2007. "Econometric modelling for short-term inflation forecasting in the euro area," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(5), pages 303-316.
  52. Clive W. J. Granger & Lykke E. Andersen, 2006. "Modeling Amazon Deforestation for Policy Purposes," Development Research Working Paper Series 12/2006, Institute for Advanced Development Studies.
  53. Alexander Yu. Apokin & Irina B. Ipatova, 2016. "Structural Breaks in Potential GDP Of Three Major Economies: Just Impaired Credit or the “New Normal”?," HSE Working papers WP BRP 142/EC/2016, National Research University Higher School of Economics.
  54. Mariano Matilla-Garcia & Carlos Arguello, 2005. "A hybrid approach based on neural networks and genetic algorithms to the study of profitability in the Spanish Stock Market," Applied Economics Letters, Taylor & Francis Journals, vol. 12(5), pages 303-308.
  55. Lim, G.C. & McNelis, Paul D., 2008. "Computational Macroeconomics for the Open Economy," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262123061.
  56. George Papadopoulos & Savas Papadopoulos & Thomas Sager, 2016. "Credit risk stress testing for EU15 banks: a model combination approach," Working Papers 203, Bank of Greece.
  57. Fabio C. Bagliano & Claudio Morana, 2006. "A New Approach to Factor Vector Autoregressive Estimation with an Application to Large-Scale Macroeconometric Modelling," Carlo Alberto Notebooks 28, Collegio Carlo Alberto.
  58. Lee, Tae-Hwy & Yang, Yang, 2006. "Bagging binary and quantile predictors for time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 465-497.
This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.