Monetary policy and financial frictions in a small open-economy model for Uganda


This paper considers the role of financial frictions and the conduct of monetary policy in Uganda. It makes use of a dynamic stochastic general equilibrium model, which incorporates small open-economy features and financial frictions that are introduced though the activities of heterogeneous agents in the household. Most of the parameters in the model are estimated with the aid of Bayesian techniques and quarterly macroeconomic data from 2000q1 to 2015q4. The results suggest that the central bank currently responds to changes in the interest rate spread, despite the fact that capital and financial markets are relatively inefficient in this low-income country. In addition, the analysis also suggests that to reduce macroeconomic volatility, the central bank should continue to respond to these financial sector frictions and that it may be possible to derive a more favourable sacrifice ratio by making use of a slightly more aggressive response to macroeconomic developments.

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  1. 1.

    Gray et al. (2011) describe how various economic factors (including changes to interest rates) affect financial sector credit risks. They also describe how the financial sector affects measures of economic activity. This has been particularly evident during the recent crises, as the Global Financial Crisis [2007], Latin American Crisis [1980], and Asian Crisis [1997] were all mainly triggered by financial sector weaknesses.

  2. 2.

    Important early contributions that consider the relationship between financial instability and the conduct of monetary policy include Patinkin (1956) and Tobin (1969).

  3. 3.

    Initial attempts to introduce financial sector frictions into DSGE models by Kiyotaki and Moore (1997) and Bernanke et al. (1999) were never adopted in central banks and other policy institutions prior to the onset of the recent crisis (Quadrini 2011).

  4. 4.

    This additional objective is been pursued in tandem with the primary role of fostering price stability. At present, financial sector stability models at the BOU are detached from the monetary policy models. This is in many ways similar to many other countries, which partly reflects the institutional arrangements in central banks, where macroeconomic models that are used for forecasting and policy analysis reside in monetary/economic policy analysis divisions, while financial system analysis models reside in the bank supervision/financial stability divisions (Vlcek and Roger 2012).

  5. 5.

    In the November 2012 monetary policy statement by the Governor of the BOU, it was noted that “...whereas inter-bank rates, wholesale deposit interest rates and securities yields have all followed the downward trend of the central bank rate, commercial bank lending rates have been sticky downwards” (Tumusiime-Mutebile 2012).

  6. 6.

    Such features would suggest that the bank lending channel for monetary policy transmission may be dominated by the effects of changes in the central bank short-term interest rates, which influence that rate that is charged by commercial banks on loans and paid on deposits (Mishra et al. 2010).

  7. 7.

    Where the definition of the sacrifice ratio is the amount of output growth that is sacrificed to reduce the level of inflation (Ball 1994).

  8. 8.

    An additional online appendix includes a brief review of the literature that considers the application of structural macroeconomic models in LICs. It also incorporates further details relating to the data, the full log-linear specification of this model and additional figures that relate to the estimation results.

  9. 9.

    The inclusion of exchange rate in the Taylor rule is supported by Blanchard et al. (2010), who suggest that central banks in small open-economies should openly recognise exchange rate stability as part of their objective function.

  10. 10.

    Further details relating to the data and the transformations that have been applied are included in the online appendix.

  11. 11.

    An and Schorfheide (2007) and Fernández-Villaverde (2010) provide an informative summary of the advantages that may be derived from estimating these models with Bayesian techniques.

  12. 12.

    The estimation procedure utilises a Markov Chain Monte Carlo (MCMC) algorithm and the Brooks and Gelman (1998) measure of convergence, where five chains of 100,000 draws are used for randomly selected starting values. The average acceptance rate for all the chains is about 25.07%, and for each chain, 40,000 draws are kept after the initial burn in phase. Convergence is monitored with the aid of univariate and the multivariate diagnostic MCMC plots and suggests that it is achieved after about 50,000 draws.

  13. 13.

    The existence of relatively underdeveloped financial and capital markets and statements by the central bank Governor on the inefficiency of monetary policy transmission would support a more conservative estimate for these parameters.

  14. 14.

    The prior and posterior density plots have been included in the online appendix.

  15. 15.

    A number of researchers have also suggested that large estimates for the interest rate smoothing parameter indicate relatively persistent inflationary shocks (Rudebusch 2002).

  16. 16.

    Since we are looking to compare the impulse response functions from two models in this case, the results reflect the mean dynamic responses of the variables. The corresponding Bayesian impulse response functions that include 90% confidence intervals for the posterior distributions are included in the online appendix.

  17. 17.

    The results from the historical decompositions of the real exchange rate, terms of trade, policy rate and lending rate are contained in the online appendix.

  18. 18.

    When comparing the influence of the shocks on inflation during the pre- and post-crisis subsamples, we note that cost-push have become more prominent, although this is possibly due to the inflationary spike in 2011, rather than the onset of global financial crisis.

  19. 19.

    As noted previously, Woodford (2012) suggests that measures of financial frictions should be included in monetary policy loss functions.

  20. 20.

    The loss function values for the estimated parameters have not been included in this table as it is ultimately derived from the likelihood function, while the loss function for optimal coefficients is derived from the weighted volatility of specific variables. As such, these are not comparable.

  21. 21.

    Additional counter-factual experiments were performed by varying the weights on exchange rate and interest rate spreads between 0.1 to 0.2. These did not change the reported results by a significant degree.

  22. 22.

    This would be consistent with the findings of Alpanda et al. (2010).

  23. 23.

    Note that the coefficient values for the estimated coefficients and the optimal coefficients in the third column are extremely similar, which would suggest that the central bank has been pursuing a near optimal monetary policy rule following the onset of the Global Financial Crisis. However, as shown in the online appendix, these differences do nevertheless result in a gap in the sacrifice ratio, when comparing the result that is produced by the estimated and optimal coefficients in the post-crisis subsample.

  24. 24.

    In the early literature, the sacrifice ratio was obtained from the relationship between output and inflation that was based on estimates for the Philips curve (c.f. Okun 1978; Gordon et al. 1982). More recently, Ball (1994) and Cecchetti and Rich (2001) use monetary policy impulse response functions that are derived from vector autoregressive (VAR) models to estimate the sacrifice ratio.

  25. 25.

    The results of these sacrifice ratios is contained in the online appendix.


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Anguyo, F.L., Gupta, R. & Kotzé, K. Monetary policy and financial frictions in a small open-economy model for Uganda. Empir Econ 59, 1213–1241 (2020).

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  • Monetary policy
  • Inflation targeting
  • Financial frictions
  • Small open economy
  • Low-income country
  • Dynamic stochastic general equilibrium model
  • Bayesian estimation

JEL Classification

  • E32
  • E52
  • F41