Skip to main content
SpringerLink
Log in

The Optimal Economic Uncertainty Index: A Grid Search Application

  • Published:
Computational Economics Aims and scope Submit manuscript

Abstract

A noteworthy characteristic of empirical studies on the economic uncertainty index is that very few published papers depend on normative analysis. Therefore, normative analysis cannot be used to refute the precision of the economic uncertainty index; the lack of precision is simply the outcome of a misspecification of a commonly used model and a complex data collection process. To overcome this shortcoming, this paper uses the optimal form of the economic uncertainty index and determines its empirical validity based on a sample of 7 countries, including 3 developed and 4 developing countries. Using a grid search optimization procedure, the findings provide some policy implications; the optimal economic uncertainty index can characterize the uncertainty level of macroeconomic conditions and serve as a guiding policy tool for improving uncertainty levels in macroeconomic conditions. The estimated response function of the optimal economic uncertainty index suggests that the exchange rate, inflation, interest rate and output are useful indicators for central banks’ decision-making and that the optimal index supports the prediction of economic uncertainty.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. The systemic financial risk entails crises that result in a sharp decline in economic activities and asset values (cf. World Economic Forum 2008, p. 7).

  2. In this paper, the potential value is computed by running the Hodrick-Prescott filter on the actual value. The potential variable can also be estimated by employing the Baxter and King (BP) filter and the unobservable components (UC) model. Their results did not vary a great deal compared to the Hodrick-Prescott approach.

  3. One may use other optimization algorithms, such as the SIMPLEX algorithm, the GENETIC search method or the BFGS algorithm, to find the optimal reaction coefficients; however, this approach is not advisable when the models are large (Levieuge 2008).

  4. One may soften the economic policy to mitigate the negative level of the economic uncertainty index if the above process is the other way round.

  5. The real exchange rate used in this paper is the real effective exchange rate (REER). Real appreciation means that the value of the domestic currency is increasing relative to its trading partners, and real depreciation means that the value of the domestic currency is decreasing relative to its trading partners.

  6. Cf. Golob (1994) for a further discussion of how economic uncertainty interacts with the economy.

  7. In our example, given \(A_{1}\), we assume that \(\Omega \) is the identity matrix.

  8. The sample RATS codes (for the grid search procedure) in estimating the optimal economic uncertainty index and the loss function are available online at https://staf-digital.upsi.edu.my/Fakulti/FPE/gan.pt/Optimaleconomicuncertaintyindex_US_forCE_.PRG (Note this sample program works when executed in RATS version 6.1 onwards).

  9. See Cf. International Monetary Fund (2005) for further explanations of the real effective exchange rate.

  10. The real interest rate is measured as the difference between the nominal interest rate and the rate of inflation.

  11. Equation 5 is not included in the GMM method because the actual estimate of economic uncertainty index cannot be determined; however, the optimal form (i.e., best form) of the economic uncertainty index could be found through the use of the grid search optimization procedure.

  12. The appropriate values of the loss function remain debatable (see Levin and Williams 2003). The parameter values chosen here are fairly standard in the monetary policy literature (Jaaskela (2005): p. 146).

  13. Evidence is available from the central banks’ website, Ito and Hayashi (2004) and the International Monetary Fund (2009). Among others, this objective is also reported by (1) Indonesia—Fane (2005), (2) Thailand—Hataiseree and Rattanalungkarn (1988) and (3) Malaysia—McCauley (2006)

  14. The Phillips-Perron unit root test allows us to reject the null hypothesis regarding the existence of a non-stationary or unit root.

  15. This paper presents only a short summary of the results because the complete results require more space than is allowed here.

References

  • Allison, T., & Suwanraks, R. (1999). Financial reforms in Thailand. Thailand Development Research Institute Quarterly Review, 14(4), 6–18.

    Google Scholar 

  • Apergis, N. (1999). Inflation uncertainty and money demand: Evidence from a monetary regime change and the case of Greece. International Economic Journal, 13(2), 21–30.

    Article  Google Scholar 

  • Atta-Mensah, J. (2004). Money demand and economic uncertainty. Bank of Canada Working Paper No. 2004–25.

  • Ball, L. (1997). Efficient rules for monetary policy. National Bureau of Economic Research Working Paper No. 5952.

  • Ball, L. (1999). Policy rules for open economies. In J. Taylor (Ed.), Monetary policy rules (pp. 129–156). Chicago: University of Chicago Press.

    Google Scholar 

  • Bean, C. (2011). Joseph Schumpeter lecture the great moderation, the great panic, and the great contraction. Journal of the European Economic Association, 8(2), 289–325.

    Google Scholar 

  • Bernanke, B. S. (2010a). On the implications of the financial crisis for economics. Speech Presented at the Center for Economic Policy Studies and the Bendheim Center for Finance. Princeton, New Jersey. http://www.federalreserve.gov/newsevents/speech/bernanke20100924a.htm. Accessed 25 May 2011.

  • Bernanke, B. S. (2010b). Monetary policy and the housing bubble. Speech Presented at the annual meeting of the American Economic Association. Atlanta, Georgia. www.federalreserve.gov/newsevents/speech/bernanke20100103a.htm, Accessed 25 May 2011.

  • Berument, H., & Dincer, N. N. (2005). Inflation and inflation uncertainty in the G-7 countries. Physica A, 348, 371–379.

    Google Scholar 

  • Bofinger, P., & Wollmershauser, T. (2001). Managed floating: Understanding the new international monetary order. Economic Papers No: 30, University of Wurzburg.

  • Burger, P., & Knedlik, T. (2004). The MCI as a monetary policy guide in a small, open emerging market economy. South African Journal of Economics, 72(2), 366–384.

    Google Scholar 

  • Caplin, A., & Schotte, A. (2008). The foundations of positive and normative economics: A handbook. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Casellina, S., & Uberti, M. (2008). Optimal monetary policy and long-term interest rate dynamics: Taylor rule extensions. Computational Economics, 32(1), 183–198.

    Article  Google Scholar 

  • Choi, W. G., & Oh, S. (2003). A money demand function with output uncertainty, monetary uncertainty, and financial innovations. Journal of Money, Credit and Banking, 35(5), 685–709.

    Article  Google Scholar 

  • Clarida, R., Gali, J., & Gertler, M. (1998). Monetary policy rules in practice: Some international evidence. European Economic Review, 42(6), 1033–1067.

    Article  Google Scholar 

  • Cronin, D., Kelly, R., & Kennedy, B. (2010). Money growth, uncertainty and macroeconomics activity: A multivariate GARCH analysis. Empirica, 38(2), 155–167.

    Google Scholar 

  • Du, H., Wang, J., Watzl, J., Zhang, X., & Hu, Z. (2008). Classification structure-activity relationship (CSAR) studies for prediction of genotoxicity of thiophene derivatives. Toxicology Letters, 177(1), 10–19.

    Article  Google Scholar 

  • Edge, R. M., Laubach, T., & Williams, J. C. (2010). Welfare-maximizing monetary policy under parameter uncertainty. Journal of Applied Econometrics, 25(1), 129–143.

    Article  Google Scholar 

  • Ellsberg, D. (1961). Risk, ambiguity, and the savage axioms. The Quarterly Journal of Economics, 75(4), 643–669.

    Article  Google Scholar 

  • Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.

    Article  Google Scholar 

  • Epstein, L. G. (1999). A definition of uncertainty aversion. Review of Economic Studies, 66(3), 579–608.

    Article  Google Scholar 

  • Fane, G. (2005). Post-crisis monetary and exchange rate policies in Indonesia, Malaysia and Thailand. Bulletin of Indonesian Economic Studies, 41(2), 175–195.

    Article  Google Scholar 

  • Feldstein, M. (2010). What power for the Federal Reserve? Journal of Economic Literature, 48(1), 134–145.

    Article  Google Scholar 

  • Gali, J., & Gertler, M. (1999). Inflation dynamic: A structural econometric approach. Journal of Monetary Economics, 44(2), 195–222.

    Article  Google Scholar 

  • Golob, J. E. (1994). Does inflation uncertainty increase with inflation? Economic Review (Federal Reserve Bank of Kansas City), 3, 27–38.

    Google Scholar 

  • Guender, A. (2005). On optimal monetary policy rules and the Role of MCIs in the open economy. Open Economies Review, 16(2), 189–207.

    Article  Google Scholar 

  • Hall, T. E., & Noble, N. R. (1987). Velocity and the variability of money growth: Evidence from Granger causality tests. Journal of Money, Credit and Banking, 19(1), 112–116.

    Article  Google Scholar 

  • Hataiseree, R., & Rattanalungkarn, T. (1998). Strategies for Thailand’s monetary policy operations in the year 2000: Inflation targeting versus monetary targeting? Bank of Thailand Monthly Bulletin, 38(11), 11–42.

    Google Scholar 

  • International Monetary Fund. (2005). International financial statistic yearbook. International Monetary Fund Statistics Department.

  • International Monetary Fund. (2009). World economic outlook: World economic and financial surveys. Washington, DC: International Monetary Fund.

    Google Scholar 

  • Ito, T., & Hayashi, T. (2004). Inflation targeting in Asia. Occasional Paper 1, Hong Kong Institute for Monetary Research.

  • Jaaskela, J. P. (2005). Inflation, price level and hybrid rules under inflation uncertainty. Scandinavian Journal of Economics, 107(1), 141–156.

    Article  Google Scholar 

  • Jondeau, E., & Bihan, H. L. (2000). Evaluating monetary policy rules in estimated forward-looking models: A comparison of US and German monetary policies. Banque de France Notes D’Etudes Et De Recherché 76.

  • Jondeau, E., & Sahuc, J. (2008). Optimal monetary policy in an estimated DSGE model of the Euro area with cross-country heterogeneity. International Journal of Central Banking, 4(2), 23–72.

    Google Scholar 

  • Knight, F. H. (1921). Risk, uncertainty, and profit. Boston: The Riverside Press.

  • Levieuge, G. (2008). Implementing the Svensson’s approach to determine simple optimal monetary policy with ESTIMA RATS. In Collection Lecture & Methodologie 2008–01. Laboratoire d’Economies D’Orleans.

  • Levin, A., & Williams, J. C. (2003). Robust monetary policy with competing reference models. Journal of Monetary Economics, 50(5), 945–947.

    Google Scholar 

  • Makinen, G. E. (2008). Current economic conditions and selected forecasts. Congressional Research Service Report RL30329. http://fpc.state.gov/documents/organization/112048.pdf, Accessed 29 May 2011.

  • Mankiw, N. G., & Reis, R. (2011). Imperfect information and aggregate demand. In B. Friedman & M. Woodford (Eds.), Handbook of monetary economics (Vol. 3(A), pp. 182–230). North Holland: Elsevier.

  • McCauley, R. N. (2006). Understanding monetary policy in Malaysia and Thailand: Objectives, instruments and independence. Bank for International Settlements Papers 31 (pp. 172–196). http://www.bis.org/pub1/bppdf/bispap31o.pdf, Accessed 25 May 2009.

  • Pindyck, R. S., & Rubinfeld, D. L. (1998). Econometric models and economic forecasts. New York: McGraw-Hill.

    Google Scholar 

  • Rudebusch, G., & Svensson, L. E. O. (2001). Policy rule for inflation targeting. In J. Taylor (Ed.), Monetary policy rules (pp. 203–263). Chicago: University of Chicago Press.

    Google Scholar 

  • Ruud, P. (2000). An introduction to classical econometric theory. In Grid search. Oxford: Oxford University Press.

  • Serletis, A., & Shahmoradi, A. (2006). Velocity and the variability of money growth: Evidence from a VARMA. GARCH-M model. Macroeconomic Dynamics, 10(5), 652–666.

    Article  Google Scholar 

  • Smets, F. (2002). Output gap uncertainty: Does it matter for the Taylor rule? Empirical Economics, 27, 113–129.

    Article  Google Scholar 

  • Smets, F. (2003). Maintaining price stability: How long is the medium term? Journal of Monetary Economics, 50(6), 1293–1309.

    Article  Google Scholar 

  • Svensson, L. E. O. (2000). Open-economy inflation targeting. Journal of International Economics, 50(1), 155–183.

    Article  Google Scholar 

  • Taylor, J. B. (2009). The financial crisis and the policy responses: An empirical analysis of what went wrong. National Bureau of Economic Research Working Paper No. 14631.

  • Thornton, J. (1995). Friedman’s money supply volatility hypothesis: Some international evidence. Journal of Money, Credit and Banking, 27(1), 288–292.

    Article  Google Scholar 

  • Woodford, M. (2003). Interest and prices: Foundations of a theory of monetary policy. Princeton: Princeton University Press.

    Google Scholar 

  • World Economic Forum (2008). Global risks. Geneva: Global Risk Network of World Economic Forum.

Download references

Acknowledgments

The author is very grateful to the anonymous referees for their helpful comments and constructive suggestions. The author would like to thank Professor Dr. Takatoshi Ito from the University of Tokyo, who suggested the idea of positivism and normativism in the study of macroeconomic conditions during a Postgraduate Seminar on 14\(^{\mathrm{th}}\) August 2008 organized by the University of Malaya. Additionally, the author acknowledges financial support from the ’Fundamental Research Grant Scheme (FRGS), Ministry of Higher Education Malaysia’ under Grant 2012-0018-106-02.

Author information

Authors and Affiliations

  1. Department of Economics, Faculty of Management and Economics, Universiti Pendidikan Sultan Idris (Sultan Idris Education University), 35900 , Tanjong Malim, Perak, Malaysia

    Pei-Tha Gan

Authors
  1. Pei-Tha Gan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pei-Tha Gan.

Appendix

Appendix

See Table 5.

Table 5 Summary statistics for the Phillips-Perron (PP) unit root test
Full size table

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gan, PT. The Optimal Economic Uncertainty Index: A Grid Search Application. Comput Econ 43, 159–182 (2014). https://doi.org/10.1007/s10614-013-9366-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10614-013-9366-y

Keywords

JEL Classification

Search

Navigation