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Forecasting macroeconomic variables using data of different periodicities

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  • Shen, Chung-Hua

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  • Shen, Chung-Hua, 1996. "Forecasting macroeconomic variables using data of different periodicities," International Journal of Forecasting, Elsevier, vol. 12(2), pages 269-282, June.
    • Handle: RePEc:eee:intfor:v:12:y:1996:i:2:p:269-282
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    1. Donihue, Michael R. & Howrey, E. Philip, 1992. "Using mixed frequency data to improve macroeconomic forecasts of inventory investment," International Journal of Production Economics, Elsevier, vol. 26(1-3), pages 33-41, February.
    2. Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1983. "Forecasting and Conditional Projection Using Realistic Prior Distributions," NBER Working Papers 1202, National Bureau of Economic Research, Inc.
    3. Carol Corrado, 1986. "Reducing uncertainty in current analysis and projections: the estimation of monthly GNP," Special Studies Papers 209, Board of Governors of the Federal Reserve System (U.S.).
    4. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
    5. Litterman, Robert, 1986. "Forecasting with Bayesian vector autoregressions -- Five years of experience : Robert B. Litterman, Journal of Business and Economic Statistics 4 (1986) 25-38," International Journal of Forecasting, Elsevier, vol. 2(4), pages 497-498.
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    Cited by:

    1. Alain MAURIN & Alain GUAY, 2008. "An Adaptation of the MIDAS Regression Model for Estimating and Forecasting Quarterly GDP : Application to the Case of Guadeloupe," EcoMod2008 23800085, EcoMod.
    2. Qing Cao & Mark Parry & Karyl Leggio, 2011. "The three-factor model and artificial neural networks: predicting stock price movement in China," Annals of Operations Research, Springer, vol. 185(1), pages 25-44, May.
    3. Klaus Wohlrabe, 2009. "Makroökonomische Prognosen mit gemischten Frequenzen," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(21), pages 22-33, November.
    4. Guay, Alain & Maurin, Alain, 2015. "Disaggregation methods based on MIDAS regression," Economic Modelling, Elsevier, vol. 50(C), pages 123-129.
    5. Periklis Gogas & Theophilos Papadimitriou & Emmanouil Sofianos, 2022. "Forecasting unemployment in the euro area with machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 551-566, April.
    6. Rodriguez, Abel & Puggioni, Gavino, 2010. "Mixed frequency models: Bayesian approaches to estimation and prediction," International Journal of Forecasting, Elsevier, vol. 26(2), pages 293-311, April.
    7. Kuo, Chen-Yin, 2016. "Does the vector error correction model perform better than others in forecasting stock price? An application of residual income valuation theory," Economic Modelling, Elsevier, vol. 52(PB), pages 772-789.
    8. Chen-Yin Kuo, 2017. "Is the accuracy of stock value forecasting relevant to industry factors or firm-specific factors? An empirical study of the Ohlson model," Review of Quantitative Finance and Accounting, Springer, vol. 49(1), pages 195-225, July.

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