Inference on probabilistic surveys in macroeconomics with an application to the evolution of uncertainty in the survey of professional forecasters during the COVID pandemic

Probabilistic surveys on macroeconomic variables provide a wealth of information to the applied researcher. Extracting and using this information is not a trivial task, however. This chapter discusses the challenges involved in this task and the approaches used so far in the literature for conducting inference on probabilistic surveys. It also provides an application of some of these methods using the U.S. Survey of Professional Forecasters and investigates the evolution of uncertainty and tail risk for both output growth and inflation during the COVID pandemic. © 2023 Elsevier Inc. All rights reserved.

Forecasting Economic Indicators with Robust Factor Models

Outliers can cause significant errors in forecasting, and it is essential to reduce their impact without losing the information they store. Information loss naturally arises if observations are dropped from the dataset. Thus, two alternative procedures are considered here: the Fast Minimum Covariance Determinant and the Iteratively Reweighted Least Squares. The procedures are used to estimate factor models robust to outliers, and a comparison of the forecast abilities of the robust approaches is carried out on a large dataset widely used in economics. The dataset includes observations relative to the 2009 crisis and the COVID-19 pandemic, some of which can be considered outliers. The comparison is carried out at different sampling frequencies and horizons, in-sample and out-of-sample, on relevant variables such as GDP, Unemployment Rate, and Prices for both the US and the EU.

UNDERSTANDING ECONOMIC INSTABILITY DURING THE PANDEMIC: A FACTOR MODEL APPROACH

This chapter studies the effects of the COVID-19 pandemic on the economic structure of the US and EU economies by measuring its impact on some reference macro-economic variables. We use a factor model approach on a set of variables available at different frequencies (daily, weekly, monthly, and quarterly) and provide evidence of instability in the primary factors driving the economy. A sequential analysis of the factors allows us to evaluate the model’s forecasting performance and extract some instability measures based on the factor model’s eigenvalues. Finally, we show how to use COVID-related variables, such as policy, economic, and health indicators, to compute conditional forecasts with factor models, and perform a scenario analysis on the variables of interest to understand economic instability. © 2022 by Emerald Publishing Limited.

Modeling Turning Points in the Global Equity Market

Turning points in financial markets are often characterized by changes in the direction and/or magnitude of market movements with short-to-long term impacts on investors’ decisions. A Bayesian technique is developed for turning point detection in financial equity markets. The interconnectedness among stock market returns from a piece-wise network vector autoregressive model is derived. The turning points in the global equity market over the past two decades are examined in the empirical application. The level of interconnectedness during the Covid-19 pandemic and the 2008 global financial crisis are compared. Similarities and most central markets responsible for spillover propagation emerged from the analysis. © 2021 The Authors