Term Structure Forecasting: No‐Arbitrage Restrictions versus Large Information Set

This paper addresses the issue of forecasting term structure. We provide a unified state‐space modeling framework that encompasses different existing discrete‐time yield curve models. Within such a framework we analyze the impact of two modeling choices, namely the imposition of no‐arbitrage restrictions and the size of the information set used to extract factors, on forecasting performance. Using US yield curve data, we find that both no‐arbitrage and large information sets help in forecasting but no model uniformly dominates the other. No‐arbitrage models are more useful at shorter horizons for shorter maturities. Large information sets are more useful at longer horizons and longer maturities. We also find evidence for a significant feedback from yield curve models to macroeconomic variables that could be exploited for macroeconomic forecasting. Copyright © 2010 John Wiley & Sons, Ltd.     

New evidence on the robust identification of news shocks: Role of revisions in utilization‐adjusted TFP series and term structure data

Data revisions and selections of appropriate forwarding‐looking variables have a major impact on true identification of news shocks and quality of research findings derived from structural vector autoregression (SVAR) estimation. This paper revisits news shocks to identify the role of different vintages of total factor productivity (TFP) series and term structure of interest rates as major prognosticators of future economic growth. There is a growing strand of literature regarding the use of utilization‐adjusted TFP series, provided by Fernald (Federal Reserve Bank of San Francisco, Working Paper Series, 2014) for identification of news shocks. We reestimate Barsky and Sims' (Journal of Monetary Economics, 2011, 58, 273–289) empirical analysis by employing 2007 and 2015 vintages of TFP data. We find substantial quantitative as well as qualitative differences among impulse response functions when using 2007 and 2015 vintages of TFP data. Output and hours initially decline, followed by quick reversal of both variables. In sharp contrast to results achieved by the 2007 vintage of TFP data, results achieved by the 2015 vintage of TFP data depict that output and hours will increase in response to positive TFP shock. By including term structure data in our VAR specification, total surprise technology shock and news shock account for 97% and 92% of the forecast error variance in total TFP and total output respectively. We find that revisions in TFP series over time ultimately impact the conclusion regarding news shocks on business cycles. Our results support the notion that term structure data help in better identification of news shock as compared to other forward‐looking variables.