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The effect of uncertainty on UK investment authorisation: Homogenous vs. heterogeneous estimators

Author

Listed:
  • Ciaran Driver
  • Katsushi Imai
  • Paul Temple
  • Giovanni Urga

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Abstract

This paper compares pooled and non-pooled models of UK capital investment using the Confederation of British Industry’s (CBI) Industrial Trends Survey, focusing on the impact of uncertainty. The uncertainty measure is based on the cross sectional dispersion of optimism about the future business conditions in the industry in which the firm operates. The panel data estimation shows that uncertainty has quantitatively important negative effects on investment. However, if we look at the estimation results at the industry level, we find a great diversity in both estimated elasticities and t-statistics, providing valuable information not available from the pooled model. Finally, we compare the forecast performances of the above models; this analysis confirms that pooled estimators are generally better than non-pooled estimators in terms of out-of-sample forecast performance, but the difference between the two is not very large. Copyright Springer-Verlag 2004

Suggested Citation

  • Ciaran Driver & Katsushi Imai & Paul Temple & Giovanni Urga, 2004. "The effect of uncertainty on UK investment authorisation: Homogenous vs. heterogeneous estimators," Empirical Economics, Springer, vol. 29(1), pages 115-128, January.
  • Handle: RePEc:spr:empeco:v:29:y:2004:i:1:p:115-128
    DOI: 10.1007/s00181-003-0192-2
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    Citations

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    Cited by:

    1. Driver, Ciaran & Temple, Paul & Urga, Giovanni, 2008. "Real options -- delay vs. pre-emption: Do industrial characteristics matter?," International Journal of Industrial Organization, Elsevier, vol. 26(2), pages 532-545, March.
    2. Bettina Becker & Stephen Hall, 2013. "Do R&D strategies in high-tech sectors differ from those in low-tech sectors? An alternative approach to testing the pooling assumption," Economic Change and Restructuring, Springer, vol. 46(2), pages 183-202, May.
    3. Ciaran Driver & Paul Temple & Giovanni Urga, 2005. "Explaining the Diversity of Industry Investment Responses to Uncertainty Using Long Run Panel Survey Data," School of Economics Discussion Papers 0405, School of Economics, University of Surrey.
    4. Badi H. Baltagi, 2008. "Forecasting with panel data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(2), pages 153-173.
    5. Marina Riem, 2016. "Corporate investment decisions under political uncertainty," ifo Working Paper Series 221, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    6. Baltagi, Badi H., 2013. "Panel Data Forecasting," Handbook of Economic Forecasting, Elsevier.
    7. Binding, Garret & Dibiasi, Andreas, 2017. "Exchange rate uncertainty and firm investment plans evidence from Swiss survey data," Journal of Macroeconomics, Elsevier, vol. 51(C), pages 1-27.
    8. Kang, Wensheng & Lee, Kiseok & Ratti, Ronald A., 2014. "Economic policy uncertainty and firm-level investment," Journal of Macroeconomics, Elsevier, vol. 39(PA), pages 42-53.
    9. Badi H. Baltagi, 2013. "Dynamic panel data models," Chapters,in: Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 10, pages 229-248 Edward Elgar Publishing.

    More about this item

    Keywords

    Investment; uncertainty; panel data estimation; E22; C23;

    JEL classification:

    • E22 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Investment; Capital; Intangible Capital; Capacity
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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