This paper measures a neutral interest rate in Ukraine by means of applying a Kalman filter to a semistructural model with unobserved components. We rely on a medium-term concept of a neutral interest rate, where it is defined as a real interest rate consistent with output at its potential level and inflation at its target level after the effects of all cyclical shocks have disappeared. Under this concept, and accounting for the small open nature of Ukrainian economy, the neutral interest rate is determined by the global economy's cost of capital and domestic long-term factors that influence risk-premium and changes in the real exchange rate. Conditional on long-term forecasts for output, demographic trends, real exchange rate changes and risk premium, the neutral rate is projected to decrease gradually from its 2.5% level as of the beginning of 2018 to 2% in real terms, or to 7% in nominal terms under a 5% inflation target. However, in the following years the gap between the National Bank of Ukraine's policy rate and the neutral rate should remain positive -reflecting the tight monetary stance needed to ensure stable disinflation. JEL Codes C32, E43, E52
І. INTRODUCTIONWhere is the economy heading and what is the current rate of economic growth? Real quarterly GDP is one of the best indicators of an economy's course and growth rate. However, GDP is reported well after the end of a quarter, while decisions on economic policy require real-time information about the current state of the economy. International practice has shown it possible to obtain that information through so-called nowcasting models that allow an assessment of the state of an economy before official data is disclosed.Nowcasting refers to the forecasting of data for the current time period. The central banks of most advanced economies use nowcasting. Given the time lag between changes to monetary policy and their effect on the economy, central banks need early readings of the current economic situation. Among other things, the timely detection of economic shifts is needed to achieve and maintain price stability.In macroeconomic forecasting, there may be a large number of possible explanatory variables. Factor analysis makes it possible to distinguish the main drivers of variation within a set of variables. Thus, a lower number of estimated factors can summarize a sizable amount of information from a large system. Factor-augmented (factor) models carry numerous advantages. First, they can cover many variables to avoid the problem of a lack of degrees of freedom. Moreover, incorporating a large amount of information helps to yield more accurate estimates for forecasting and macroeconomic analysis. Second, factor analysis can discard a variable's own shocks that have no impact on overall trends within a system. Those shocks may include errors of measurement. In this way, monetary policy receives a reliable signal from the economy and is not compelled to react to noise. In addition, distinguishing general economic factors and shocks is an important task in macroeconomic analysis. Third, factor models remain agnostic about an economy's structure and are not dependent on economic assumptions.This article develops and investigates the forecasting performance of a factor model for nowcasting Ukraine's GDP. This type of model should be based on accessible leading indicators and should make use of information as new data is released. Forecasting performance is assessed using experiments on pseudo out-of-sample forecasting. We simulate a real situation with limited time spans of data available for estimating factors and equation coefficients. The model is capable of re-estimating the factors and coefficients as new data becomes available and to gradually adjust GDP nowcasts. However, the design of these types of experiments differs from the ideal one, because it uses the latest available data . Some historical data might have been reviewed post factum.This article is built as follows: section 2 contains an overview of literature; section 3 analyzes Ukraine's GDP dynamics over the past 10 years; section 4 describes the data and the factors built on the basis of the data, provides an economic backgrou...
This study examines applying foreign exchange interventions under Inflation Targeting regime in an emerging market economy. For this purpose, we employ the Quarterly Projection Model of the National Bank of Ukraine and simulate different policy responses to various macroeconomic shocks. We discuss monetary policy objectives, which are low inflation volatility and accumulation of international reserves, and conclude that monetary policy could benefit from using interventions in addition to the key policy rate. We advise on particular policy reactions (with or without FX intervention) in case of different macroeconomic shocks.
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