IDEAS home Printed from https://ideas.repec.org/a/spr/empeco/v54y2018i2d10.1007_s00181-016-1198-x.html
   My bibliography  Save this article

On predicting the semiconductor industry cycle: a Bayesian model averaging approach

Author

Listed:
  • Wen-Hsien Liu

    (National Chung Cheng University)

  • Shu-Shih Weng

    (National Chung Cheng University)

Abstract

This study considers the model uncertainty and utilizes the Bayesian model averaging (BMA) approach to identify useful predictors of the semiconductor industry cycle from a list of 70 potential predictors. The posterior inclusion probabilities, posterior means, and posterior standard deviations over the period of 1995:05–2012:10 are estimated and consequently used to identify the main determinants of the industry cycle. It is found that the Philadelphia Semiconductor Index and total inventories in various downstream industries have important roles in signaling the industry growth. The results from an out-of-sample forecasting exercise also reveal the predictive potential and usefulness of BMA for the long-term prediction.

Suggested Citation

  • Wen-Hsien Liu & Shu-Shih Weng, 2018. "On predicting the semiconductor industry cycle: a Bayesian model averaging approach," Empirical Economics, Springer, vol. 54(2), pages 673-703, March.
    • Handle: RePEc:spr:empeco:v:54:y:2018:i:2:d:10.1007_s00181-016-1198-x
    DOI: 10.1007/s00181-016-1198-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00181-016-1198-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00181-016-1198-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Patricia Renou-Maissant & Mathilde Aubry, 2014. "Semiconductor Industry Cycles: Explanatory Factors and Forecasting," Post-Print hal-02562533, HAL.
    2. Jonathan H. Wright, 2009. "Forecasting US inflation by Bayesian model averaging," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(2), pages 131-144.
    3. Levine, Ross & Renelt, David, 1992. "A Sensitivity Analysis of Cross-Country Growth Regressions," American Economic Review, American Economic Association, vol. 82(4), pages 942-963, September.
    4. M. Aubry & P. Renou-Maissant, 2013. "Investigating the semiconductor industry cycles," Applied Economics, Taylor & Francis Journals, vol. 45(21), pages 3058-3067, July.
    5. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    6. Sune Karlsson & Tor Jacobson, 2004. "Finding good predictors for inflation: a Bayesian model averaging approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(7), pages 479-496.
    7. Min, Chung-ki & Zellner, Arnold, 1993. "Bayesian and non-Bayesian methods for combining models and forecasts with applications to forecasting international growth rates," Journal of Econometrics, Elsevier, vol. 56(1-2), pages 89-118, March.
    8. Liu, Wen-Hsien, 2005. "Determinants of the semiconductor industry cycles," Journal of Policy Modeling, Elsevier, vol. 27(7), pages 853-866, October.
    9. Mathilde Aubry & Patricia Renou-Maissant, 2014. "Semiconductor industry cycles: Explanatory factors and forecasting," Post-Print halshs-01101975, HAL.
    10. Liu, Wen-Hsien & Chyi, Yih-Luan, 2006. "A Markov regime-switching model for the semiconductor industry cycles," Economic Modelling, Elsevier, vol. 23(4), pages 569-578, July.
    11. Leamer, Edward E, 1983. "Let's Take the Con Out of Econometrics," American Economic Review, American Economic Association, vol. 73(1), pages 31-43, March.
    12. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    13. Wright, Jonathan H., 2008. "Bayesian Model Averaging and exchange rate forecasts," Journal of Econometrics, Elsevier, vol. 146(2), pages 329-341, October.
    14. Sala-i-Martin, Xavier, 1997. "I Just Ran Two Million Regressions," American Economic Review, American Economic Association, vol. 87(2), pages 178-183, May.
    15. Aubry, Mathilde & Renou-Maissant, Patricia, 2014. "Semiconductor industry cycles: Explanatory factors and forecasting," Economic Modelling, Elsevier, vol. 39(C), pages 221-231.
    16. Xavier Sala-I-Martin & Gernot Doppelhofer & Ronald I. Miller, 2004. "Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach," American Economic Review, American Economic Association, vol. 94(4), pages 813-835, September.
    17. Liu, Wen-Hsien & Chung, Ching-Fan & Chang, Kuang-Liang, 2013. "Inventory change, capacity utilization and the semiconductor industry cycle," Economic Modelling, Elsevier, vol. 31(C), pages 119-127.
    18. Chow, Hwee Kwan & Choy, Keen Meng, 2006. "Forecasting the global electronics cycle with leading indicators: A Bayesian VAR approach," International Journal of Forecasting, Elsevier, vol. 22(2), pages 301-315.
    19. Hansen, Bruce E. & Racine, Jeffrey S., 2012. "Jackknife model averaging," Journal of Econometrics, Elsevier, vol. 167(1), pages 38-46.
    20. Wen-Hsien Liu, 2007. "Forecasting the semiconductor industry cycles by bootstrap prediction intervals," Applied Economics, Taylor & Francis Journals, vol. 39(13), pages 1731-1742.
    21. Bruce E. Hansen, 2007. "Least Squares Model Averaging," Econometrica, Econometric Society, vol. 75(4), pages 1175-1189, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chiou, Hai-Tang & Guo, Meihui & Ing, Ching-Kang, 2020. "Variable selection for high-dimensional regression models with time series and heteroscedastic errors," Journal of Econometrics, Elsevier, vol. 216(1), pages 118-136.
    2. Syed Jawad Hussain Shahzad & Elie Bouri & Jose Arreola-Hernandez & David Roubaud & Stelios Bekiros, 2019. "Spillover across Eurozone credit market sectors and determinants," Applied Economics, Taylor & Francis Journals, vol. 51(59), pages 6333-6349, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    2. Aubry, Mathilde & Renou-Maissant, Patricia, 2014. "Semiconductor industry cycles: Explanatory factors and forecasting," Economic Modelling, Elsevier, vol. 39(C), pages 221-231.
    3. Dimitris Korobilis, 2018. "Machine Learning Macroeconometrics: A Primer," Working Paper series 18-30, Rimini Centre for Economic Analysis.
    4. Moral-Benito, Enrique, 2010. "Model averaging in economics," MPRA Paper 26047, University Library of Munich, Germany.
    5. Cheng, Xu & Hansen, Bruce E., 2015. "Forecasting with factor-augmented regression: A frequentist model averaging approach," Journal of Econometrics, Elsevier, vol. 186(2), pages 280-293.
    6. Xu Cheng & Bruce E. Hansen, 2012. "Forecasting with Factor-Augmented Regression: A Frequentist Model Averaging Approach, Second Version," PIER Working Paper Archive 13-061, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 03 Sep 2013.
    7. Rockey, James & Temple, Jonathan, 2016. "Growth econometrics for agnostics and true believers," European Economic Review, Elsevier, vol. 81(C), pages 86-102.
    8. Enrique Moral-Benito, 2015. "Model Averaging In Economics: An Overview," Journal of Economic Surveys, Wiley Blackwell, vol. 29(1), pages 46-75, February.
    9. Garett Jones & W. Schneider, 2006. "Intelligence, Human Capital, and Economic Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach," Journal of Economic Growth, Springer, vol. 11(1), pages 71-93, March.
    10. Matthias Pelster & Johannes Vilsmeier, 2018. "The determinants of CDS spreads: evidence from the model space," Review of Derivatives Research, Springer, vol. 21(1), pages 63-118, April.
    11. Hansen, Bruce E., 2008. "Least-squares forecast averaging," Journal of Econometrics, Elsevier, vol. 146(2), pages 342-350, October.
    12. Pelster, Matthias & Vilsmeier, Johannes, 2016. "The determinants of CDS spreads: Evidence from the model space," Discussion Papers 43/2016, Deutsche Bundesbank.
    13. Ebersberger, Bernd & Galia, Fabrice & Laursen, Keld & Salter, Ammon, 2021. "Inbound Open Innovation and Innovation Performance: A Robustness Study," Research Policy, Elsevier, vol. 50(7).
    14. Neil R. Ericsson, 2008. "The Fragility of Sensitivity Analysis: An Encompassing Perspective," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(s1), pages 895-914, December.
    15. R Burger & S du Plessis, 2011. "Examining the Robustness of Competing Explanations of Slow Growth in African Countries," Studies in Economics and Econometrics, Taylor & Francis Journals, vol. 35(3), pages 21-47, December.
    16. Ley, Eduardo & Steel, Mark F. J., 2007. "On the effect of prior assumptions in Bayesian model averaging with applications to growth regression," Policy Research Working Paper Series 4238, The World Bank.
    17. Martin Guth, 2022. "Predicting Default Probabilities for Stress Tests: A Comparison of Models," Papers 2202.03110, arXiv.org.
    18. Tumala, Mohammed M & Olubusoye, Olusanya E & Yaaba, Baba N & Yaya, OlaOluwa S & Akanbi, Olawale B, 2017. "Forecasting Nigerian Inflation using Model Averaging methods: Modelling Frameworks to Central Banks," MPRA Paper 88754, University Library of Munich, Germany, revised Feb 2018.
    19. Klump, R. & Prüfer, P., 2006. "Prioritizing Policies for Pro-Poor Growth : Applying Bayesian Model Averaging to Vietnam," Other publications TiSEM dc14add6-f581-4eea-92dd-3, Tilburg University, School of Economics and Management.
    20. Anwen Yin, 2024. "Predictive model averaging with parameter instability and heteroskedasticity," Bulletin of Economic Research, Wiley Blackwell, vol. 76(2), pages 418-442, April.

    More about this item

    Keywords

    Bayesian model averaging; Semiconductor; Industry cycle;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • L16 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Industrial Organization and Macroeconomics; Macroeconomic Industrial Structure
    • L63 - Industrial Organization - - Industry Studies: Manufacturing - - - Microelectronics; Computers; Communications Equipment

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:empeco:v:54:y:2018:i:2:d:10.1007_s00181-016-1198-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.