Do Macro Variables, Asset Markets or Surveys Forecast Inflation Better?

Abstract

Surveys do! We examine the forecasting power of four alternative methods of forecasting U.S. inflation out-of-sample: time series ARIMA models; regressions using real activity measures motivated from the Phillips curve; term structure models that include linear, non-linear, and arbitrage-free specifications; and survey-based measures. We also investigate several optimal methods of combining forecasts. Our results show that surveys outperform the other forecasting methods and that the term structure specifications perform relatively poorly. We find little evidence that combining forecasts using means or medians, or using optimal weights with prior information produces superior forecasts to survey information alone. When combining forecasts, the data consistently places the highest weights on survey information.

Forecasting the yield curve: A statistical model with market survey data

“…13 On the other hand, the results show that market expectation surveys on price index data are indeed good information sources to explain the future behavior of interest rates. This observation is consistent with several other financial studies, such as Ang and Piazzesi (2003) and Ang et al (2007).…”

“…The Fischer equation (Fisher, 1930) and the Taylor rule (Taylor, 1993) help make price indexes the main macroeconomic variable used to model the TSIR, since they specify a direct relation between inflation and interest rates. 4 On the other hand, some authors find that market surveys are powerful predictors of future inflation (see, for instance, Ang, Bekaert, & Wei, 2007;Mehra, 2002). To combine these two features in single variable we use the inflation expectation calculated by the Central Bank of Brazil (CBB) from a survey among professional forecasters as the explanatory variable for future interest rates.…”

News on inflation and the epidemiology of inflation expectations in China

“…In response to this question, a number of important studies have emphasized the use of survey-based inflation expectations instead of rational expectations approximation in the relevant analyses since the 1990s (e.g. Roberts, 1995;Thomas, 1999;Rudebusch, 2002;Orphanides, 2003;Ang et. al.，2007).…”

Testing for multiple-period predictability between serially dependent time series

“…Fisher, Liu, and Zhou (2002), Stockton and Glassman (1987) and Stock and Watson (1999) find evidence that Phillips curve models can beat the benchmark at forecast horizons of up to 8 quarters, 12 months, and 12 and 24 months respectively. Ang, Bekaert, and Wei (2007), Atkeson and Ohanian (2001) and Cecchetti, Chu, and Steindel (2000) do not. Stock and Watson (2008) provide an extensive review of the literature and conclude that the good performance of Phillips curve models is episodic.…”