The COVID‐19 pandemic ushered in unprecedented social and economic conditions, alongside unexpected policy responses, challenging the effectiveness of traditional labor market forecasting approaches. This article presents a novel approach that integrates macroeconomic variables, traditional labor market metrics, and Google search data to develop a machine learning‐based indicator for the Colombian labor market. We employ support vector machine for regression and neural networks models to forecast monthly employment and unemployment rates, explicitly focusing on the third wave of COVID‐19 in the first half of 2021. Our study's findings reveal that the proposed models outperform the autoregressive benchmark regarding forecast accuracy, demonstrating a rapid adaptation to labor market shifts.

This paper develops and implements an Autoregressive Integrated Moving Average model with an Adaptive Neuro-Fuzzy Inference System (ARIMA-ANFIS) for BTCUSD price prediction and risk assessment. The goal of these forecasts is to identify patterns from past data and achieve an understanding of the future behavior of the price and its volatility. The proposed ARIMA-ANFIS model is compared with a benchmark ARIMA-GARCH model. To evaluated the adequacy of the models in terms of risk assessment, we compare the confidence intervals of the price and accuracy measures for the testing sample. Additionally, we implement the diebold and Mariano test to compare the accuracy of the two volatility forecasts. The results revealed that each volatility model focuses on different aspects of the data dynamics. The ANFIS model, while effective in certain scenarios, may expose one to unexpected risks due to its underestimation of volatility during turbulent periods. On the other hand, the GARCH(1,1) model, by producing higher volatility estimates, may lead to excessive caution, potentially reducing returns.

The exact percentage points of the likelihood ratio statistic for testing the hypothesis that two p-variate normal distributions are independent and their covariance matrices are equal have been computed for p = 2,3,4 and 5.