The Unassuming Economist

From a paper by Emmanouil Sofianos, Christos Alexakis, Periklis Gogas, and Theophilos Papadimitriou:

“This paper aims to forecast deviations of the US output measured by the industrial production index (IPI), from its long-run potential output, known as output gaps. These gaps are important for policymakers when designing relevant economic policies, especially when a negative output gap may show economic slack or underperformance, often associated with higher unemployment and low inflation. We use a dataset that includes 32 explanatory economic and financial variables and 18 lags of the IPI, spanning the period from 2000:1 to 2022:12, resulting in 50 variables and 276 monthly observations. The dataset is fed to five well-established machine learning (ML) methods, namely decision trees, random forests, XGBoost, long short-term memory (LSTM) and support vector machines (SVMs), coupled with the linear, the RBF and the polynomial kernel. Moreover, we use the standard elastic net logit method from the area of econometrics as a benchmark. Our results indicate that the tree-based ML techniques perform better in-sample, and the best overall forecasting model is the XGBoost achieving an out-of-sample accuracy of 91.67%.”

From funds for NGOs:

“The International Monetary Fund (IMF) is a pivotal institution in the global economic landscape, established in 1944 with the primary goal of fostering international monetary cooperation and ensuring financial stability. With its headquarters in Washington, D.C., the IMF comprises 190 member countries, each contributing to a pool of financial resources that can be accessed by nations in need. The organization plays a crucial role in monitoring the global economy, providing policy advice, and offering financial assistance to countries facing economic challenges.

Its mission is not only to stabilize economies but also to promote sustainable growth and reduce poverty worldwide. The IMF’s influence extends beyond mere financial transactions; it serves as a forum for dialogue among its member countries, facilitating discussions on economic policies and strategies. By analyzing global economic trends and providing data-driven insights, the IMF helps nations navigate complex economic landscapes.

Its work is particularly vital in an increasingly interconnected world where economic crises can have far-reaching implications. As such, the IMF stands as a guardian of global economic stability, striving to create an environment conducive to growth and prosperity for all nations.

Promoting Economic Growth through Policy Advice and Technical Assistance

One of the IMF’s core functions is to provide policy advice and technical assistance to its member countries. This support is tailored to the specific needs of each nation, taking into account its unique economic circumstances and challenges. The IMF’s expertise in macroeconomic policy, fiscal management, and monetary policy allows it to offer valuable recommendations that can help countries achieve sustainable economic growth.

By engaging with governments and policymakers, the IMF fosters an environment where sound economic policies can flourish. Technical assistance from the IMF often includes training programs, workshops, and on-the-ground support aimed at strengthening institutional capacities. For instance, the organization may assist a country in developing its tax administration system or enhancing its public financial management practices.

Such initiatives not only improve the efficiency of government operations but also bolster transparency and accountability. By equipping countries with the necessary tools and knowledge, the IMF empowers them to implement effective policies that drive economic growth and development.

(…)

“In recent years, the IMF has increasingly recognized the importance of sustainable development and inclusive growth as integral components of its mission.”

Continue reading here.

We at this website have long encouraged the IMF’s embrace of inclusive growth as a goal. See the blog on inclusive growth and the IMF.

From a paper by Moritz Uhl:

“Reoccurring instability keeps forcing central banks repeatedly to intervene in financial markets, since the 2007-2008 crisis most notably with massive asset purchases, whose popularisation was spearheaded by the Bank of Japan. This paper exploits the world’s first implementation of quantitative easing in the vicinity of zero interest rates in Japan from 1999 through 2006 to evaluate their distributional impact by means of the synthetic control method. Comparing the actual and counterfactual development demonstrates that unconventional monetary policy increased the top 10 percent to bottom 50 percent income ratio by more than 28 percent. This exercise also detects a rise of more than 7 percent for the Gini coefficient which is beneath the corresponding value of 12.5 percent for the share of the top income decile. These results, together with evidence from capital and labour income trends as well as data on household ownership of financial assets, suggest that inequality widened via heightening asset prices converting into gains for richer income groups. Conditional upon structural features of an economy a negative distributional side effect of central banking’s new tools may turn out to be of severe magnitude.”

From a paper by Emmanouil Sofianos, Christos Alexakis, Periklis Gogas, and Theophilos Papadimitriou:

“This paper aims to forecast deviations of the US output measured by the industrial production index (IPI), from its long-run potential output, known as output gaps. These gaps are important for policymakers when designing relevant economic policies, especially when a negative output gap may show economic slack or underperformance, often associated with higher unemployment and low inflation. We use a dataset that includes 32 explanatory economic and financial variables and 18 lags of the IPI, spanning the period from 2000:1 to 2022:12, resulting in 50 variables and 276 monthly observations. The dataset is fed to five well-established machine learning (ML) methods, namely decision trees, random forests, XGBoost, long short-term memory (LSTM) and support vector machines (SVMs), coupled with the linear, the RBF and the polynomial kernel. Moreover, we use the standard elastic net logit method from the area of econometrics as a benchmark. Our results indicate that the tree-based ML techniques perform better in-sample, and the best overall forecasting model is the XGBoost achieving an out-of-sample accuracy of 91.67%.:

From a VoxEU post by Ralph De Haas, Pablo García Guzmán, Zsóka Kóczán, and Victoria Marino:

“The Life in Transition Survey collects data on household income, employment, education, attitudes, beliefs, and personal experiences across 37 economies in emerging Europe, the Caucasus, Central Asia, and North Africa. This column introduces the 4th wave of the survey, which reveals declining intergenerational mobility in post-socialist economies, growing inequality of opportunity, and trade-offs between wages and job benefits. It also documents substantial wage premiums for digital skills and how concerns about climate change often fail to translate into support for environmental policies. The (publicly available) data can generate valuable insights for policymakers working to promote inclusive growth and social cohesion in emerging economies.”