Microfounded forecasting

Our focus is on information in expectation surveys that can now be built on thousands (or millions) of respondents on an almost continuous-time basis (big data) and in continuous macroeconomic surveys with a limited number of respondents. We show that, under standard microeconomic and econometric techniques, survey forecasts are an affine function of the conditional expectation of the target variable. This is true whether or not the survey respondent knows the data-generating process (DGP) of the target variable or the econometrician knows the respondents individual loss function. If the econometrician has a mean-squared-error risk function, we show that asymptotically efficient forecasts of the target variable can be built using Hansens (Econometrica, 1982) generalized method of moments in a panel-data context, when N and T diverge or when T diverges with N xed. Sequential asymptotic results are obtained using Phillips and Moon s (Econometrica, 1999) framework. Possible extensions are also discussed.

Essays in applied econometrics

Using a unique dataset on Brazilian nominal and real yield curves combined with daily survey forecasts of macroeconomic variables such as GDP growth, inflation, and exchange rate movements, we identify the effect of surprises to the Brazilian interbank target rate on expected future nominal and real short rates, term premia, and inflation expectations. We find that positive surprises to target rates lead to higher expected nominal and real interest rates and reduced nominal and inflation term premia. We also find a strongly positive relation between both real and nominal term premia and measures of dispersion in survey forecasts. Uncertainty about future exchange rates is a particularly important driver of variations in Brazilian term premia.