Browsing by Author "Gupta, Rangan"

Now showing 1 - 4 of 4
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    Inflation Dynamics in Uganda: a Quantile Regression Approach
    (Macroeconomics and Finance in Emerging Market Economies, 2020) Anguyo, Francis Leni; Gupta, Rangan; Kotzé, Kevin
    This paper considers the measurement of inflation persistence in Uganda and how this has changed over time within different quantiles. The measures of inflation include headline inflation and two measures of core inflation. The results suggest that while a unit root is found in many of the upper quantiles of headline inflation, there is evidence of mean reversion within the lower quantiles, which implies that large positive deviations influence the permanent behaviour of inflation. In addition, we find higher levels of persistence after 2006 and during the inflation-targeting period, where potential structural changes may have arisen within the regression quantiles.
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    Monetary Policy and Financial Frictions in a Small Open-Economy Model for Uganda
    (Empirical Economics, 2020) Anguyo, Francis Leni; Gupta, Rangan; Kotzé, Kevin
    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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    Monetary Policy, Financial Frictions and Structural Changes in Uganda: A Markov-Switching DSGE Approach
    (Economic research-Ekonomska istraživanja, 2020) Anguyo, Francis Leni; Gupta, Rangan; Kotzé, Kevin
    This paper considers the use of regime-switching dynamic stochastic general equilibrium models for monetary policy analysis and forecasting purposes. The objective is to determine whether or not the inclusion of these regime-switching features provide a more accurate description of the economy in a particular lowbincome country. All of the models incorporate financial frictions that are introduced through the activities of heterogeneous agents in the household and several other features that are incorporated in most small open-economy models. Two variants of regime-switching models are considered: one includes switching in the monetary policy rule (only) and the other employs switching in both the monetary policy rule and the volatility of the shocks. The models are applied to the quarterly macroeconomic data for Uganda and most of the parameters are estimated with the aid of Bayesian techniques. The results of the extensive inand out-of-sample evaluation suggest that the model parameters do not remain constant over the two regimes. In addition, the transition probabilities suggest that there are three distinct periods where the central bank response has been more aggressive. These periods relate to a change in policy framework and significant shocks that have affected the Ugandan economy. It is also noted that the forecasting performance of the regime-switching models are possibly superior to the model that excludes these features over certain horizons.
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    Predicting BRICS stock returns using ARFIMA models
    (Applied Financial Economics, 2014) Aye, Goodness C.; Balcilar, Mehmet; Gupta, Rangan; Kilimani, Nicholas; Nakumuryango, Amandine; Redford, Siobhan
    This article examines the existence of long memory in daily stock market returns from Brazil, Russia, India, China and South Africa (BRICS) countries and also attempts to shed light on the efficacy of autoregressive fractionally integrated moving average (ARFIMA) models in predicting stock returns. We present evidence which suggests that ARFIMA models estimated using a variety of estimation procedures yield better forecasting results than the non-ARFIMA (AR, MA, ARMA and GARCH) models with regard to prediction of stock returns. These findings hold consistently for the different countries whose econo- mies differ in size, nature and sophistication.