Structural Quarterly Projection Model For Belarus

Belarusian authorities contemplate transiting to inflation targeting. The paper suggests a small structural model at the core of the forecasting and policy analysis system. A well-researched canonical structure of Berg, A., Karam, P. and D. Laxton (2006) is extended to capture specifics of Belarusian economy and macroeconomic policy. The modified model’s policy block reflects a monetary targeting regime and allows for transition from it to an interest-rate-based framework. Adding wages, directed lending and dollarization allow for studying implications of activist wage policy, state program lending, and dollarization for macroeconomic stability and the strength of the policy transmission mechanism.

The National Bank of the Republic of Belarus (NBRB) visited Minsk during for the seventh of the planned eight short-term technical assistance (TA) missions to help the NBRB enhance its modeling, forecasting and policy analysis capacity, and the forecasting and policy analysis system, sponsored by the Swedish International Development Agency. The NBRB is reforming its monetary policy framework in line with recommendations of past IMF TA missions and its Road Map for Transitioning to Inflation Targeting with the aim of eventually adopting inflation targeting (IT). Transitioning to IT would require, among other strengthening the monetary policy forecasting and analysis system (FPAS) and better integrating the core quarterly projection model (QPM) into the decision-making process. The mission was mainly aimed at helping with reviewing the initial conditions and compiling a QPM-based forecast as a part of the NBRB’s September forecasting round. The mission, in addition, worked on strengthening processes within the FPAS.

National Bank of Rwanda (BNR) modernized monetary policy and transited to the price-based policy framework in January 2019. The Forecasting and Policy Analysis System (FPAS) is the cornerstone for the new forward-looking framework, which mobilizes and organizes resources and sets processes for regular forecasting rounds. The core of this system is a structural macroeconomic model for macroeconomic analysis and projections to support the BNR staff’s policy recommendations to the monetary policy committee. This paper documents the quarterly projection model (QPM) at the core of the FPAS at the BNR. The model is an extension of the canonical structure in Berg et al (2006) to reflect specifics of the interest-rate-based policy framework with a managed exchange rate, the effect of agricultural sector and harvests on prices, and the role of fiscal policies and aid flows.

This paper takes stock of forecasting and policy analysis system capacity development (FPAS CD), drawing extensively on the experience and lessons learned from developing FPAS capacity in the central banks. By sharing the insights gained during FPAS CD delivery and outlining the typical tools developed in the process, the paper aims to facilitate the understanding of FPAS CD within the IMF and to inform future CD on building macroeconomic frameworks. As such, the paper offers a qualitative assessment of the experience with FPAS CD delivery and the use of FPAS in the decision-making process in central banks.

This Technical Assistance (TA) report on the Republic of Belarus focuses on various aspects of monetary policy modeling. This TA mission was the fifth from series of quarterly IMF TA missions focused on the forecasting and analysis system capacity building. It was mainly aimed to simulate initial conditions and compile a quarterly projection model (QPM)-based forecast scenario as a part of a practical forecasting round at the National Bank of the Republic of Belarus (NBRB) in March. Moreover, the mission worked with the modeling team to deepen its understanding of the QPM’s role in policy decision making and in internal communication. Adopting Inflation Targeting and increasing monetary policy effectiveness would require broad-based reforms as compressively outlined in the developed Road Map for Transitioning to Inflation Targeting. This medium-term TA project aims to primarily help the NBRB with medium-term inflation forecasting and policy analysis and related tools to effectively support policy making. The project composed of series of TA and training missions particularly focused on the preparation of forecasts and policy analyses, the medium-term forecasting and policy analysis model, and presentations of the forecasts and policy analysis.

This paper attempts to explain short- and long-term dynamics of-and forecast-inflation in Tajikistan using the Vector Error Correction Model (VECM) and Autoregressive Moving Average Model (ARMA). By analyzing different transmission channels through the VECM, we were able to evaluate their relative dominance, magnitude, and speed of transition to the equilibrium price level, with the view of identifying those policy tools that will enhance the effectiveness of monetary policy. We found that excess supply of broad money is inflationary in both the short and long term. The dynamic analysis also demonstrates that the exchange rate and international inflation have a strong impact on local prices. Available monetary instruments, such as the refinancing rate, have proven to be ineffective. Therefore, the Tajik monetary authority could greatly benefit from enhancing its monetary instruments toolkit, including by developing the interest rate channel, to improve its monetary policy execution and to achieve stable inflationary conditions.

This study documents a semi-structural model developed for Sri Lanka. This model, extended with a fiscal sector block, is expected to serve as a core forecasting model in the process of the Central Bank of Sri Lanka’s move towards flexible inflation targeting. The model includes a forward-looking endogenous interest rate and foreign exchange rate policy rules allowing for flexible change in policy behavior. It is a gap model that allows for simultaneous identification of business cycle position and long-term equilibrium. The model was first calibrated and then its data-fit was improved using Bayesian estimation technique with relatively tight priors.