Analyzing Inflation Dynamics in Ghana: Evidence as of Quantile Autoregressive Model

Document Type: Original Research


College of Statistics & Mathematics, Zhejiang Gongshang University, Hangzhou, 310018, China


Inflation persistence (or inertia) has been a problem in many developing countries and due to the relationship between inflation and economic growth, much research has been conducted on the literature to study closely these macroeconomic variables in developing countries (Brick, 2010; Gokal and Hanif, 2004). This paper however made use of a superior method known as quantile autoregressive model proposed by Koenker and Xiao (2006) to estimate the persistence of inflation, the dynamic behavior and examine how diverse shocks may perhaps affect the rate of inflation within different quantiles. The data employed in this study is the monthly year-on-year Ghana inflation rate from January 2000 to July 2019. The result shows that Ghana inflation rates exhibits low persistence at both lower and higher quantiles and a mean reversion behavior across quantiles. Also, we observe that Ghana inflation rate is globally stationary as well as portraying non-stationary behavior in about 10% of the sampled observations. Evidently, the results again reveal that Ghana inflation rate has irregular characteristics at different quantiles in its conditional distribution. Also, there is a bidirectional relationship between Ghana overall inflation rate and its components (food and non-food inflation).  


Andrews, D. W. K. (1993). Tests for Parameter Instability and Structural Change With Unknown Change Point. Econometrica, 61(4), 821.
Brick, A. (2010). Threshold effects of inflation on economic growth in developing countries. Economics Letters, 108(2), 126–129.
Cecchetti, S. G., Hooper, P., Kasman, B. C., Schoenholtz, K. L., & Watson, M. W. (2007). U . S . Monetary Policy Forum 2007 Understanding the Evolving Inflation Process By.
Cicek, S., & Akar, C. (2013). The asymmetry of inflation adjustment in Turkey. Journal of Economic Modelling, 31, 104–118.
Engle, R. F., & Manganelli, S. (2004). CAViaR: Conditional autoregressive value at risk by regression quantiles. Journal of Business and Economic Statistics, 22(4), 367–381.
Gaglianone, W. P., & Lima, L. R. (2012). Constructing Density Forecasts from Quantile Regressions. Journal of Money, Credit and Banking, 44(8), 1589–1607.
Gaglianone, W. P., & Lima, L. R. (2014). Constructing optimal density forecasts from point forecast combinations. Journal of Applied Econometrics, 29(5), 736–757.
Gaglianone, W. P., Lima, L. R., Linton, O., & Smith, D. R. (2011). Evaluating Value-at-Risk Models via Quantile Regression. Journal of Business and Economic Statistics, 29(1).
Gerlach, S., & Tillmann, P. (2012). Inflation targeting and inflation persistence in Asia–Pacific. Journal of Asian Economics, 23(4), 360–373.
IMF. (2017). World Economic Outlook.
Koenker, R, & Basset, G. (1978). Asymptotic theory of least absolute error regression. Journal of the American Statistical Association, 73(363), 618–622.
Koenker, R, & Machado, J. (1998). Likelihood ratio and goodness of fit processes for quantile regression. Praxis, 94(448).
Koenker, R, & Xiao, Z. (2004). Unit root quantile autoregression inference. Journal of the American Statistical Association, 99(467), 775–787.
Koenker, R, & Xiao, Z. (2006). Quantile autoregression. Journal of the American Statistical Association, 101(475), 980–990.
Koenker, Roger, & Zhao, Q. (1996). Conditional Quantile Estimation and Inference for Arch Models. In Econometric Theory (Vol. 12).
Lima, L. R., Gaglianone, W. P., & Sampaio, R. M. B. (2008). Debt ceiling and fiscal sustainability in Brazil: A quantile autoregression approach. Journal of Development Economics, 86(2), 313–335.
Lima, L. R., & Sampaio, R. (2005). The Asymmetric Behavior of the U . S . Public.
Lin, S., & Ye, H. (2009). Does inflation targeting make a difference in developing countries? Journal of Development Economics, 89(1), 118–123.
Maia, A. L. S., & Cribari-Neto, F. (2006). Brazilian inflationary dynamics: results of quantile autoregression. Brazilian Journal of Economics, 60(2).
Mallick, S. K., & Sousa, R. M. (2012). REAL EFFECTS OF MONETARY POLICY IN LARGE EMERGING ECONOMIES. Macroeconomic Dynamics, 16(S2), 190–212.
Mavikela, N., Mhaka, S., & Phiri, A. (2018). The inflation-growth relationship in SSA inflation targeting countries. Munich Personal RePEc Archive, (73357).
Mendonça, H. F., & Souza, G. J. G. e. (2012). Is inflation targeting a good remedy to control inflation? Journal of Development Economics, 98(2), 178–191.
Mishkin, F. S. (2007). Inflation Dynamics. International Finance, 10(3), 317–334.
Ocran, M. K. (2007). A modelling of Ghana’s inflation experience : 1960-2003. Studies in Economics and Econometrics, 31, 119-144(26).
Oliveira, F. N. De, & Petrassi, M. (2014). Is Inflation Persistence Over ? 393–422.
Osei, V. (2015). Inflation Dynamics in Ghana. International Finance and Banking, 2(1), 38.
Phiri, A. (2012). Threshold effects and inflation persistence in South Africa. Journal of Financial Economic Policy, 4(3), 247-269.
Phiri, A. (2016a). Inflation persistence and monetary policy in South Africa: is the 3% to 6% inflation target too persistent? International Journal of Sustainable Economy, 8(2).
Phiri, A. (2016b). Inflation persistence in African countries : Does inflation targeting matter ? 5(3), 65–71.
Piazza, W., Teixeira, O., Guillén, D. C., Marcos, F., & Figueiredo, R. (2018). Estimating inflation persistence by quantile autoregression with quantile-specific unit roots ☆. Economic Modelling, 73(June), 407–430.
Pivetta, F., & Reis, R. (2007). The persistence of inflation in the United States. Journal of Economic Dynamics and Control, 31(4), 1326–1358.
Roache, S. (2014). Inflation Persistence in Brazil: A Cross Country Comparison. IMF Working Papers, 14(55), 1.
Rudd, J., & Whelan, K. (2007). Modeling Inflation Dynamics: A Critical Review of Recent Research. Journal of Money, Credit and Banking, 39(s1), Pages 155-170.
S.Gonçalves, C. E., & M.Salles, J. (2008). Inflation targeting in emerging economies: What do the data say? Journal of Development Economics, 85(1–2), 312–318.
Samarina, A., Terpstra, M., & Haan, J. De. (2014). Inflation targeting and inflation performance: a comparative analysis. Journal of Applied Econometrics, 46(1), 41–56.
Schmidt-hebbel, K., & Mishkin, F. S. (2007). DOES INFLATION TARGETING MAKE A DIFFERENCE?
Stock, J. H., & Watson, M. W. (2007). Why Has U.S. Inflation Become Harder to Forecast? 39(1), 3–33.
Vega, M., & Winkelried, D. (2005). Inflation Targeting and Inflation Behavior: A Successful Story? International Journal of Central Banking, 1(3), 18.
Walsh, C. E. (2009). Inflation Targeting: What Have We Learned? International Finance, 12(2), 195–233.
Waltrup, L. S., Sobotka, F., & Kneib, T. (2015). Expectile and quantile regression — David and Goliath ? 15(5), 433–456.
Wolters, M. H., & Tillmann, P. (2015). The changing dynamics of US inflation persistence: a quantile regression approach. Studies in Nonlinear Dynamics & Econometrics, 19(2).
Xiao, Z. (2009). Quantile cointegrating regression. Journal of Econometrics, 150(2), 248–260.
Xiao, Z. (2014). Right-Tail Information in Financial Markets. Econometric Theory, 30(1), 94–126.