Search

David Hendry has made major contributions to many areas of economic forecasting. He has developed a taxonomy of forecast errors and a theory of unpredictability that have yielded valuable insights into the nature of forecasting. He has also provided new perspectives on many existing forecast techniques, including mean square forecast errors, add factors, leading indicators, pooling of forecasts, and multi-step estimation. In addition, David has developed new forecast tools, such as forecast encompassing; and he has improved existing ones, such as nowcasting and robustification to breaks. This interview for the International Journal of Forecasting explores David Hendry's research on forecasting. [ABSTRACT FROM AUTHOR]

Stekler and Symington (2016) construct indexes that quantify the Federal Open Market Committee's views about the U.S. economy, as expressed in the minutes of the FOMC's meetings. These indexes provide insights on the FOMC's deliberations, especially at the onset of the Great Recession. The current paper complements Stekler and Symington's analysis by showing that their indexes reveal relatively minor bias in the FOMC's views when the indexes are reinterpreted as forecasts. Additionally, these indexes provide a proximate mechanism for inferring the Fed staff's Greenbook forecasts of the U.S. real GDP growth rate, years before the Greenbook's public release. [ABSTRACT FROM AUTHOR]

This interview for Econometric Theory explores David Hendry's research. Issues discussed include estimation and inference for nonstationary time series; econometric methodology; strategies, concepts, and criteria for empirical modeling; the general-to-specific approach, as implemented in the computer packages PcGive and PcGets; computer-automated model selection procedures; David's textbook Dynamic Econometrics; Monte Carlo techniques (PcNaive); evaluation of these developments in simulation studies and in empirical investigations of consumer expenditure, money demand, inflation, and the housing and mortgage markets; economic forecasting and policy analysis; the history of econometric thought; and the use of computers for live empirical and Monte Carlo econometrics. [ABSTRACT FROM AUTHOR]

This paper serves as an introduction to forecast uncertainty in empirical economic modeling. The author defines forecast uncertainty and examines various measures of forecast uncertainty. Also, some sources and consequences of forecast uncertainty are analyzed. These issues are illustrated with the use of sample models with regards to the United States trade balance, United Kingdom inflation and real national income, and the U.S./U.K. exchange rate. This working paper can be found at the United States Federal Reserve Board's International Finance Discussion Papers. You can access it by going to http://www.federalreserve.gov/pubs/ifdp/.

This paper examines predictable uncertainty in economic forecasting and provides an introduction to predictable forecast uncertainty in empirical economic modeling. The study categorizes the sources of both predictable and unpredictable forecast uncertainty. Several analytical models, including static and dynamic models, illustrate the key features of predictable forecast uncertainty. Empirical models of the United States trade account, United Kingdom (U.K.) inflation, and U.K. real national income are used to help clarify the issues involved in this study. This working paper can be found at the United States Federal Reserve Board's International Finance Discussion Papers. You can access it by going to http://www.federalreserve.gov/pubs/ifdp/.

This paper proposes a framework design and post evaluation analysis for economic forecasts. This framework is illustrated by re-examining mean square forecast errors from dynamic models and nonlinearity biases of US external trade. Previous studies examined properties of nonlinearity bias and the possible nonmonotonicity and nonexistence of mean square forecast errors separate from other aspects of the forcasting process. This resulted in inefficient forecasting. This working paper can be found at the US Federal Reserve Board's International Finance Discussion Papers. You can access this by going to

This paper develops methods for the production and evaluation of censored density forecasts. Censored density forecasts quantify forecast risks in a middle region of the density covering a specified probability, and ignore the magnitude but not the frequency of outlying observations. We propose a new estimator that fits a potentially skewed and fat-tailed density to the inner observations, acknowledging that the outlying observations may be drawn from a different but unknown distribution. We also introduce a new test for calibration of censored density forecasts. An application using historical forecast errors from the Federal Reserve Board and the Monetary Policy Committee at the Bank of England illustrates the utility of censored density forecasts when quantifying forecast risks after shocks such as the global financial crisis and the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

I show that the probability that the Board of Governors of the Federal Reserve System staff's forecasts (the "Greenbooks") overpredicted quarterly real gross domestic product (GDP) growth depends on both the forecast horizon and also whether the forecasted quarter was above or below trend real GDP growth. For forecasted quarters that grew below trend, Greenbooks were much more likely to overpredict real GDP growth, with one-quarter ahead forecasts overpredicting real GDP growth more than 75% of the time, and this rate of overprediction was higher for further ahead forecasts. For forecasted quarters that grew above trend, Greenbooks were slightly more likely to underpredict real GDP growth, with one-quarter ahead forecasts underpredicting growth about 60% of the time. Unconditionally, on average, Greenbooks overpredicted real GDP growth. [ABSTRACT FROM AUTHOR]