Further Evidence on Information Rigidity in Output and Inflation Forecasts

Over the past few decades, numerous studies have documented that forecast of many macroeconomic variables and their revisions are biased. Many studies show that agents expectations are subject to behavioural biases. Data problems are cited as another explanation for the bias. Some studies have found that forecasters are too slow to incorporate new information in their forecast revisions (sluggishness). A few recent studies investigate the reasons for this sluggishness. They cite information rigidity as a reason for the sluggishness in forecast revisions, and thus, the source of the forecast bias. The finding of information rigidity in forecast imply that macroeconomic models should be built around the assumption of information rigidities rather than the assumption of full information rational expectations. Thus, this finding of information rigidity has significant implication for future macroeconomic model building. Our study proposes to contribute to the growing literature on information rigidity as a possible source of forecast bias and propose recommendations to minimize the bias. More specifically, we follow Coibion and Gorodnichenko (2012), Loungani, Stekler and Tamirisi (2013), and Jonas Dovern, et al. (2015) and apply their methodology to a new data set to understand the extent of information rigidities in GDP and inflation forecasts among the professional forecasters in the data set. We perform 4 tests of information rigidity across 43 countries as well as two panels of 25 developed and 18 developing countries. We also look at the extent of rigidity during recession and during the calm period. Data on the two variables (GDP and Inflation) will be collected from www.Fx4casts.com for this study (2001 2017). The journal provides updated monthly consensus forecasts of these variables, where forecasts for a target year starts two years early and forecasts are revised every month. Data on actual rates are also provided in the journal. Thus we have 24 revisions for each year, which provides an ample opportunity to investigate information rigidities in forecast revisions. The results of this study will enhance our understanding of the source of bias in output and inflation forecast, which will be useful to both practitioners and policy makers. Policy makers (fiscal and monetary) may devise better ways to provide more information to economic agents. They may find more efficient ways to disseminate the policy information in order to reduce cost of information acquisition and also devise ways to reduce noise in the relevant information set. The study will particularly enhance our understanding of forecast rigidities in the developing countries as these markets are of particular interest to many investors and policy makers.