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International Monetary Fund. European Dept.

With a demonstrated resilience to the crisis and the recovery gaining strength, macroeconomic policies should aim at preserving stability and complementing structural reforms that address long-standing challenges. A medium-term plan to rebuild buffers, support potential growth, and target pockets of vulnerability would help address pre-existing disparities and poverty. Sustained productivity growth, supported by the implementation of politically difficult but needed structural reforms, is the only way to support high wage growth and convergence with Western Europe. Failure to do so could jeopardize Lithuania’s hard-earned competitiveness gains.

International Monetary Fund. European Dept.

The Lithuanian authorities highly appreciate the continuation of the constructive, candid, and productive engagement with the Fund staff, as well as the insightful and well-balanced report for the 2021 Article IV consultation. Our authorities agree with the thrust of staff’s findings and recommendations, which are broadly in line with their own assessment and policy priorities. The authorities also emphasize their commitment to multilateralism, as well as their deepening engagement in the Fund’s financial initiatives: in 2018, Lithuania became a participant in the FTP; in 2020, Lithuania joined the new BBAs; and currently, Lithuania is in the process of joining the VTAs.

Marijn A. Bolhuis and Brett Rayner
We leverage insights from machine learning to optimize the tradeoff between bias and variance when estimating economic models using pooled datasets. Specifically, we develop a simple algorithm that estimates the similarity of economic structures across countries and selects the optimal pool of countries to maximize out-of-sample prediction accuracy of a model. We apply the new alogrithm by nowcasting output growth with a panel of 102 countries and are able to significantly improve forecast accuracy relative to alternative pools. The algortihm improves nowcast performance for advanced economies, as well as emerging market and developing economies, suggesting that machine learning techniques using pooled data could be an important macro tool for many countries.