The preliminary evidence in the literature suggests that changes in uncertainty have a role in shaping the U.S. economic cycle. But what is effectively measured by the different available indicators of uncertainty still remains an "uncertain" issue. This paper has two aims: (i) to introduce a...

Most macroeconomic data is continuously revised as additional information becomes available. We suggest that revisions of data is an important source of uncertainty about the state of the economy. This paper evaluates the quality of major real macroeconomic Euro area variables, published by...

We study the impact of diverse beliefs on conduct of monetary policy. We use a New Keynesian Model solved with a quadratic approximation. Aggregation renders the belief distribution an aggregate state variable. Diverse expectations change standard results about a smooth trade-off between...

Most macroeconomic data is continuously revised as additional information becomes available. We suggest that revisions of data is an increasingly important source of uncertainty about the state of the economy and offer an alternative channel of uncertainty - data uncertainty. This paper adds on...

Apart from the loss of time and money, disputes between public authority and private partner in China's public-private partnership (PPP) projects are destroying the government's image of PPP support and the private partner's investment confidence. This article aims to explore the main causes for...

Recent rises in macroeconomic volatility have prompted the introduction of quantile vector autoregression (QVAR) models to forecast macroeconomic risk. This paper provides an extensive evaluation of the predictive performance of QVAR models in a pseudo-out-of-sample experiment spanning 112...

This paper investigates the impact of news media information on improving short-term GDP growth forecasts by analyzing a large and unique corpus of 12.4 million news articles spanning from 1991 to 2018. We extract business cycle-related sentiment from each article using an annotated dataset from...

A flexible predictive density combination model is introduced for large financial data sets which allows for dynamic weight learning and model set incompleteness. Dimension reduction procedures allocate the large sets of predictive densities and combination weights to relatively small sets....

A flexible predictive density combination is introduced for large financial data sets which allows for model set incompleteness. Dimension reduction procedures that include learning allocate the large sets of predictive densities and combination weights to relatively small subsets. Given the...

We propose a prior for VAR models that exploits the panel structure of macroeconomic time series while also providing shrinkage towards zero to address overfitting concerns. The prior is flexible as it detects shared dynamics of individual variables across endogenously determined groups of...