Narrow Your Choices

Refine By Topic

Refine By Series

Refine By Language

Refine By Date

—

Refine By IMF Taxonomy

Refine By JEL Taxonomy

Refine by LOC Taxonomy

Refine By THEMA Taxonomy

  • Print
  • Save

Western Hemisphere > Argentina

You are looking at 1 - 6 of 6 items for :

  • Type: Journal Issue x
  • Forecasting and Other Model Applications x
Clear All Modify Search
Cover IMF Working Papers
Predicting IMF-Supported Programs: A Machine Learning Approach
Tsendsuren Batsuuri
,
Shan He
,
Ruofei Hu
,
Jonathan Leslie
, and
Flora Lutz
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.
Keywords:
Early warning systems; IMF Lending; Machine Learning; IMF arrangement; ML model; machine learning approach; state-of-the-art machine learning; machine learning model; Emergency assistance; Data processing; Global financial crisis of 2008-2009; Global
Cover IMF Working Papers
Identifying Optimal Indicators and Lag Terms for Nowcasting Models
Jing Xie
Many central banks and government agencies use nowcasting techniques to obtain policy relevant information about the business cycle. Existing nowcasting methods, however, have two critical shortcomings for this purpose. First, in contrast to machine-learning models, they do not provide much if any guidance on selecting the best explantory variables (both high- and low-frequency indicators) from the (typically) larger set of variables available to the nowcaster. Second, in addition to the selection of explanatory variables, the order of the autoregression and moving average terms to use in the baseline nowcasting regression is often set arbitrarily. This paper proposes a simple procedure that simultaneously selects the optimal indicators and ARIMA(p,q) terms for the baseline nowcasting regression. The proposed AS-ARIMAX (Adjusted Stepwise Autoregressive Moving Average methods with exogenous variables) approach significantly reduces out-of-sample root mean square error for nowcasts of real GDP of six countries, including India, Argentina, Australia, South Africa, the United Kingdom, and the United States.
Keywords:
Nowcasting; Mixed Frequency; Forecasting; Business Cycles; selection procedure; Annex I. AS-ARIMAX procedure; nowcasting method; evaluation comparison; baseline model; Global
Cover IMF Working Papers
Investor Sentiment, Sovereign Debt Mispricing, and Economic Outcomes
Mr. Ramzy Al Amine
and
Tim Willems
We find that countries which are able to borrow at spreads that seem low given fundamentals (for example because investors take a bullish view on a country's future), are more likely to develop economic difficulties later on. We obtain this result through a two-stage procedure, where a first regression links sovereign spreads to fundamentals, after which residuals from this regression are deployed in a second stage to assess their impact on future outcomes (real GDP growth and the occurrence of fiscal crises). We confirm the relevance of past sovereign debt mispricing in several out-of-sample exercises, where they reduce the RMSE of real GDP growth forecasts by as much as 15 percent. This provides strong support for theories of sentiment affecting the business cycle. Our findings also suggest that countries shouldn't solely rely on spread levels when determining their fiscal strategy; underlying fundamentals should inform policy as well, since historical relationships between spreads and fundamentals often continue to apply in the medium-to-long run.
Keywords:
WP; sub; cross-validation procedure; sentiment; sovereign debt; sovereign spreads; debt crises; over-optimism; growth forecasting; real GDP growth; credit rating; growth forecast; spread discount; Current account deficits; Government consumption; Southern Europe; leave-one-out procedure; spreads data; investor sentiment
Cover IMF Working Papers
Assessing the Impact of Structural Reforms Through a Supply-side Framework: The Case of Argentina
Ms. Lusine Lusinyan
The paper uses a supply-side framework based on a production function approach to assess the role of structural reforms in boosting long-term GDP growth in Argentina. The impact of product, labor, trade, and tax reforms on each supply-side channel—capital accumulation, labor utilization, and total factor productivity, proxied with an efficiency estimate—is assessed separately and then combined to derive the total impact on growth. The largest effect of structural reforms, involving regulatory changes that promote competition and facilitate flexible forms of employment, comes through the productivity/efficiency channel. Pro-competition regulation also improves labor utilization, while lower entry barriers and trade tariffs are important for capital accumulation. Structural reforms could have substantial effects on Argentina’s long-term GDP growth; for example, an ambitious reform effort to improve business regulatory environment would add 1–1½ percent to average annual growth of GDP.
Keywords:
WP; product market; Argentina; employment; product market reform; Economic growth; structural reforms; productivity; product market competition; labor market reforms; trade openness; employment rate; technical efficiency; labor market indicator; trade tariff; Total factor productivity; Commodity markets; Capital productivity; Australia and New Zealand
Cover IMF Working Papers
Predicting Sovereign Debt Crises
Mr. Axel Schimmelpfennig
,
Nouriel Roubini
, and
Paolo Manasse
We develop an early-warning model of sovereign debt crises. A country is defined to be in a debt crisis if it is classified as being in default by Standard & Poor's, or if it has access to nonconcessional IMF financing in excess of 100 percent of quota. By means of logit and binary recursive tree analysis, we identify macroeconomic variables reflecting solvency and liquidity factors that predict a debt-crisis episode one year in advance. The logit model predicts 74 percent of all crises entries while sending few false alarms, and the recursive tree 89 percent while sending more false alarms.
Keywords:
WP; debt crisis; crisis entry; crises episode; debt-crisis indicator; currency crisis; Early-warning system; sovereign debt crises; sovereign default; debt-crisis entry; debt-crisis episode; crisis probability; debt-crisis event; crisis definition; debt crises episode; debt-servicing difficulties; probability rise; debt-servicing pressure; crisis exit; debt-servicing obligation; Debt default; Early warning systems; Africa
Cover IMF Working Papers
In Search of Coincident and Leading Indicators of Economic Activity in Argentina
Mr. Alejandro Simone
Time series on economic activity in developing countries, in particular real GDP, are reported with important lags. Therefore, it is useful to construct indicators that coincide or lead the actual direction and level of economic activity. A general methodology to construct these indicators is proposed and adapted for Argentina. Three coincident indicators could be constructed, but no reliable leading indicator could be found. From an econometric standpoint, the coincident indicators produce satisfactory point estimates of real GDP. The series that enter the indicator are broadly consistent with what many economists believe is the main source of real GDP fluctuations in Argentina: shocks to the capital account of the balance of payments. This enhances the confidence in the econometric results.
Keywords:
WP; time series; absolute deviation; growth rate; estimation result; Argentina; leading indicators; candidate series; model construction mechanism; point estimate; unrestricted model; Cyclical indicators; Business cycles; Econometric models