This review paper articulates the relationship between prediction market data and event studies, with a special focus on applications in political economy. Event studies have been used to address a variety of political economy questions - from the economic effects of party control of government...

A framework for forecasting new COVID-19 cases jointly with hospital admissions and hospital beds with COVID-19 cases is presented. This project, dubbed CovidMod, produced 21-days ahead forecasts each working day from March 2021 to April 2022, and forecast errors that were used to assess...

We forecast economic activity in Argentina on a quarterly real-time basis using dynamic factors models (DFM) (Blanco et al. 2018) and evaluate their forecasting performance during the COVID19 pandemic of 2020. We compare the results of forecasts based on a pre-pandemic estimation of the...

We study how individual unemployment expectations are shaped and updated using a unique longitudinal survey data set with subjective unemployment expectations. The survey data is linked with third-party reported administrative data on unemployment realizations, such that we are able to examine...

In this paper we describe the newly developed System for Model Analysis in Real Time (SMART) used for forecasting and model analysis in Norges Bank. While the long-term goal is to include all empirical models used in forecasting in Norges Bank, the emphasis in this paper will be on the empirical...

We consider the effects of 'precision' screening policies for cancer guided by algorithms. We first show that complex machine learning models can indeed predict cancer better than simpler models that use established risk factors. We then tackle the evaluation challenge: an algorithm that can...

This paper analyses the information content of M1 for euro area real GDP since the beginning of the 1980s and reviews theoretical arguments on why real narrow money should help predict real GDP. We find that, unlike in the U.S., in the euro area, M1 has better and more robust forecasting...

We investigate the relationship between long-term U.S. stock market risks and the macroeconomic environment using a two component GARCH-MIDAS model. Our results provide strong evidence in favor of counter-cyclical behavior of long-term stock market volatility. Among the various macro variables...

Assessment of the fiscal sector performance in the last decade has often laid the blame on poor revenue performance to below-target tax collection performance of government tax collection agencies, frequently attributed to graft and corruption among its officials and employees. The attribution,...

This paper proposes a new procedure for shock identification of macroeconomic forecasts based on factor analysis. Our identification scheme for information shocks relies on data reduction techniques for daily panels and the recognition that macroeconomic releases exhibit a high level of...