Predicting IMF-Supported Programs: A Machine Learning Approach - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Predicting IMF-Supported Programs: A Machine Learning Approach write by Tsendsuren Batsuuri. This book was released on 2024-03-08. Predicting IMF-Supported Programs: A Machine Learning Approach available in PDF, EPUB and Kindle. This study applies state-of-the-art machine learning (ML) techniques to forecast IMF-supported programs, analyzes the ML prediction results relative to traditional econometric approaches, explores non-linear relationships among predictors indicative of IMF-supported programs, and evaluates model robustness with regard to different feature sets and time periods. ML models consistently outperform traditional methods in out-of-sample prediction of new IMF-supported arrangements with key predictors that align well with the literature and show consensus across different algorithms. The analysis underscores the importance of incorporating a variety of external, fiscal, real, and financial features as well as institutional factors like membership in regional financing arrangements. The findings also highlight the varying influence of data processing choices such as feature selection, sampling techniques, and missing data imputation on the performance of different ML models and therefore indicate the usefulness of a flexible, algorithm-tailored approach. Additionally, the results reveal that models that are most effective in near and medium-term predictions may tend to underperform over the long term, thus illustrating the need for regular updates or more stable – albeit potentially near-term suboptimal – models when frequent updates are impractical.
Predicting Fiscal Crises: A Machine Learning Approach
Predicting Fiscal Crises: A Machine Learning Approach - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Predicting Fiscal Crises: A Machine Learning Approach write by Klaus-Peter Hellwig. This book was released on 2021-05-27. Predicting Fiscal Crises: A Machine Learning Approach available in PDF, EPUB and Kindle. In this paper I assess the ability of econometric and machine learning techniques to predict fiscal crises out of sample. I show that the econometric approaches used in many policy applications cannot outperform a simple heuristic rule of thumb. Machine learning techniques (elastic net, random forest, gradient boosted trees) deliver significant improvements in accuracy. Performance of machine learning techniques improves further, particularly for developing countries, when I expand the set of potential predictors and make use of algorithmic selection techniques instead of relying on a small set of variables deemed important by the literature. There is considerable agreement across learning algorithms in the set of selected predictors: Results confirm the importance of external sector stock and flow variables found in the literature but also point to demographics and the quality of governance as important predictors of fiscal crises. Fiscal variables appear to have less predictive value, and public debt matters only to the extent that it is owed to external creditors.
Repeated Use of IMF-Supported Programs: Determinants and Forecasting
Repeated Use of IMF-Supported Programs: Determinants and Forecasting - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Repeated Use of IMF-Supported Programs: Determinants and Forecasting write by Martin Iseringhausen. This book was released on 2019-11-08. Repeated Use of IMF-Supported Programs: Determinants and Forecasting available in PDF, EPUB and Kindle. This paper studies the determinants of repeated use of Fund-supported programs in a large sample covering virtually all General Resources Account (GRA) arrangements that were approved between 1952 and 2012. Generally, the revolving nature of the IMF’s resources calls for the temporary sup-port of member countries to address balance of payments problems while repeated use has often been viewed as program failure. First, using probit models we show that a small number of country-specific variables such as growth, the current account balance, the international reserves position, and the institutional framework play a significant role in explaining repeated use. Second, we discuss the role of IMF-specific and program-specific variables and find evidence that a country’s track record with the Fund is a good predictor of repeated use. Finally, we conduct an out-of-sample forecasting exer-cise. While our approach has predictive power for repeated use, exact forecasting remains challenging. From a policy perspective, the results could prove useful to assess the risk IMF programs pose to the revolving nature of the Fund’s financial resources.
Machine Learning and Causality: The Impact of Financial Crises on Growth
Machine Learning and Causality: The Impact of Financial Crises on Growth - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Machine Learning and Causality: The Impact of Financial Crises on Growth write by Mr.Andrew J Tiffin. This book was released on 2019-11-01. Machine Learning and Causality: The Impact of Financial Crises on Growth available in PDF, EPUB and Kindle. Machine learning tools are well known for their success in prediction. But prediction is not causation, and causal discovery is at the core of most questions concerning economic policy. Recently, however, the literature has focused more on issues of causality. This paper gently introduces some leading work in this area, using a concrete example—assessing the impact of a hypothetical banking crisis on a country’s growth. By enabling consideration of a rich set of potential nonlinearities, and by allowing individually-tailored policy assessments, machine learning can provide an invaluable complement to the skill set of economists within the Fund and beyond.
An Algorithmic Crystal Ball: Forecasts-based on Machine Learning
An Algorithmic Crystal Ball: Forecasts-based on Machine Learning - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook An Algorithmic Crystal Ball: Forecasts-based on Machine Learning write by Jin-Kyu Jung. This book was released on 2018-11-01. An Algorithmic Crystal Ball: Forecasts-based on Machine Learning available in PDF, EPUB and Kindle. Forecasting macroeconomic variables is key to developing a view on a country's economic outlook. Most traditional forecasting models rely on fitting data to a pre-specified relationship between input and output variables, thereby assuming a specific functional and stochastic process underlying that process. We pursue a new approach to forecasting by employing a number of machine learning algorithms, a method that is data driven, and imposing limited restrictions on the nature of the true relationship between input and output variables. We apply the Elastic Net, SuperLearner, and Recurring Neural Network algorithms on macro data of seven, broadly representative, advanced and emerging economies and find that these algorithms can outperform traditional statistical models, thereby offering a relevant addition to the field of economic forecasting.
