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This paper develops country-specific VAR models with block exogeneity restrictions to analyze how exogenous factors affect business cycles in the Eastern Caribbean. It finds that external shocks play a key role, explaining more than half of macroeconomic fluctuations in the region. Domestic business cycles are especially vulnerable to changes in climatic conditions, with a natural disaster leading to an immediate and significant fall in output-but the effects do not appear to be persistent. Oil price and external demand shocks also contribute significantly to domestic macroeconomic fluctuation

This paper examines the adjustment of developing country labor markets to macroeconomic shocks. It models as having two sectors: a formal salaried (tradable) sector that may or may not be affected by union or legislation induced wage rigidities, and an informal (nontradable) self-employment sector facing liquidity constraints to entry. This is embedded in a standard small economy macro model that permits the derivation of patterns of comovement among relative salaried/self-employed incomes, salaried/self-employed sector sizes and the real exchange rate with respect to different types of shocks in contexts with and without wage rigidities. The paper then explores time series data from Argentina, Brazil, Colombia and Mexico to test for cointegrating relationships corresponding to the patterns predicted by theory. We confirm episodes of expansion of informal self-employment consistent with the traditional segmentation views. However, we also identify episodes consistent with the sectoral expansion being driven by relative demand or productivity shocks to the nontradables sector that lead to procyclical" behavior of the informal self-employed sector.

This paper combines a data-rich environment with a machine learning algorithm to provide new estimates of time-varying systematic expectational errors ("belief distortions") embedded in survey responses. We find sizable distortions even for professional forecasters, with all respondent-types overweighting the implicit judgmental component of their forecasts relative to what can be learned from publicly available information. Forecasts of inflation and GDP growth oscillate between optimism and pessimism by large margins, with belief distortions evolving dynamically in response to cyclical shocks. The results suggest that artificial intelligence algorithms can be productively deployed to correct errors in human judgment and improve predictive accuracy. (JEL C45, D83, E23, E27, E31, E32, E37)