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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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    References listed on IDEAS

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    1. 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.
    2. 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.
    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, 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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