The expectations-augmented Phillips curve (PC) is a cornerstone of many macroeconomic models. We consider the extent to which professional forecasters' inflation and unemployment rate forecasts are `theory consistent', and find much heterogeneity. Perceptions about the responsiveness of...

In this paper we consider the value of Google Trends search data for nowcasting (and forecasting) GDP growth for a developed (U.S.) and emerging-market economy (Brazil). Our focus is on the marginal contribution of "Big Data" in the form of Google Trends data over and above that of traditional...

Application of the Bernhardt, Campello and Kutsoati (2006) test of herding to the calendar-year annual output growth and inflation forecasts suggests forecasters tend to exaggerate their differences, except at the shortest horizon when they tend to herd. We consider whether these types of...

We consider a number of ways of testing whether macroeconomic forecasters herd or anti-herd, i.e., whether they shade their forecasts towards those of others or purposefully exaggerate their differences. When applied to survey respondents' expectations of inflation and output growth the tests...

In this paper, we forecast Bitcoin's returns and return jumps using a self-exciting process embedded in a stochastic volatility model. We show the existence of the jump clustering feature, which varies depending on the frequency of the data. In an out-of-sample setting, we use a particle filter...

Macro-variables such as consumption, investment and output are expected to move together in the long run. We consider whether survey forecasts of these quantities suggest beliefs about equilibrium relationships play a prominent role in expectations formation. Evidence is brought to bear from an...

Macroeconomic data are subject to revision over time as later vintages are released, yet the usual way of generating real-time out-of-sample forecasts from models effectively makes no allowance for this form of data uncertainty. We analyse a simple method which has been used in the context of...

A structural break is viewed as a permanent change in the parameter vector of a model. Using taxonomies of all sources of forecast errors for both conditional mean and conditional variance processes, we consider the impacts of breaks and their relevance in forecasting models: (a) where the...

We show that factor forecasting models deliver real-time gains over autoregressive models for US real activity variables during the recent period, but are less successful for nominal variables. The gains are largely due to the Financial Crisis period, and are primarily at the shortest (one...

While there has been a great deal of interest in the modelling of non-linearities in economic time series, there is no clear consensus regarding the forecasting abilities of non-linear time-series models. We evaluate the performance of two leading non-linear models in forecasting post-war US...