Front Matter Page European Department Contents ABSTRACT I. INTRODUCTION II. ESTIMATION METHODS AND DESIRABLE PROPERTIES A. Estimation Methods B. Properties of Output Gap Estimates III. OUTPUT GAP ESTIMATES THROUGH WEO VINTAGES A. Definitions B. Properties of WEO Real-Time Output Gap Estimates C. Real-Time Estimates for Other Advanced Economies IV. EXPLAINING THE NEGATIVE BIAS IN REAL-TIME OUTPUT GAPS A. Decomposing the Real-Time Output Gap Bias B. Data Revisions C. Forecast Accuracy E. The Role of Judgment F
changes in business cycles and inflation, while final or real time inflation is the best predictor of future inflation. The rest of the paper is structured as follows. Section 2 reviews the most common methods to estimate the output gap and their key properties. Section 3 presents key stylized facts showing the persistent and often large negative bias in WEO real time output gap estimates for the countries in the euro area, while section 4 decomposes the output gap bias, attributing most of it to judgment and forecast errors. Section 5 looks at the role of
A careful review has revealed significant scope to modernize and better align the MAC DSA with its objectives and the IMF’s lending framework. This note proposes replacing the current framework with a new methodology based on risk assessments at three different horizons. Extensive testing has shown that the proposed framework has much better predictive accuracy than the current one. In addition to predicting sovereign stress, the framework can be used to derive statements about debt stabilization under current policies and about debt sustainability.
Information from Past Projections Source: IMF. Note: This figure shows a sign of potential at a 5-year horizon represented by the bright red cell. 1/ Calculated as the percentile rank of the country’s output gap revisions (defined as the difference between real time/period ahead estimates). 37. A second tool calculates output gap revisions from historical data and assesses optimism in potential output projections ( Figure 9.B ). It is based on Kangur et al (2019) and staff analyses showing the existence of real-time output gap biases for a majority of market