Observed expectations, news shocks, and the business cycle

Abstract

This paper exploits information from the term structure of survey expectations to identify news shocks in a a DSGE model with rational expectations. We estimate a structural business-cycle model with price and wage stickiness. We allow for both unanticipated and anticipated components ("news") in each structural disturbance: neutral and investment-specific technology shocks, government spending shocks, risk premium, price and wage markup shocks, and monetary policy shocks. We show that the estimation of a standard DSGE model with realized data obfuscates the identification of news shocks and yields weakly or non-identified parameters pertaining to such shocks. The identification of news shocks greatly improves when we re-estimate the model using data on observed expectations regarding future output, consumption, investment, government spending, inflation, and interest rates-at horizons ranging from one-period to five-periods ahead. The news series thus obtained largely differ from their counterparts that are estimated using only data on realized variables. Moreover, the results suggest that the identified news shocks explain a sizable portion of aggregate fluctuations. News about investment-specific technology and risk premium shocks play the largest role, followed by news about labor supply (wage markup) and monetary policy.

Identifying News Shocks With Forecast Data

“…7. Real-time actual data as well as forecast data are used by Milani (2017), Milani and Rajbhandari (2012), Miyamoto and Nguyen (2018), and Slobodyan and Wouters (2017) when estimating DSGE models. We estimated our model using revised data on output growth and inflation and confirmed that the main results obtained in the estimation with the real-time actual data still hold in that with the revised one.…”

“…Credible intervals of estimated parameters are all concentrated around their posterior mean when exploiting the forecast data; otherwise, the intervals are dispersed. 11 In the literature there are two closely related studies, Milani and Rajbhandari (2012) and Miyamoto and Nguyen (2018), both of which estimate DSGE models with news shocks using forecast data as well as actual data. All the three studies, including ours, reach the same conclusion that exploiting forecast data in addition to actual data leads to more precise estimates of news shocks and other model parameters.…”

“…Yet there are differences among them in what kinds of news shocks are the most important for US business cycles and to what extent news shocks generate aggregate fluctuations purely through changes in expectations, so-called "expectation driven business cycles." Milani and Rajbhandari (2012) employ several forecast data in the SPF when estimating a model of Smets and Wouters (2007) augmented with news shocks. Their estimation result suggests that news shocks explain a sizable portion of US aggregate fluctuations; in particular, news shocks about investment-specific technology and risk premium play the largest role.…”

Identifying News Shocks with Forecast Data

“…Leduc and Sill (2013) use forecast data in the SPF and the Livingston Survey when estimating a VAR. 7 Real-time actual data as well as forecast data are used by Milani (2017), Milani and Rajbhandari (2012), Miyamoto and Nguyen (2018), and Slobodyan and Wouters (2017) when estimating DSGE models. We estimated our model using revised data on output growth and inflation and confirmed that the main results obtained in the estimation with the real-time actual data still hold in that with the revised one.…”

“…Credible intervals of estimated parameters are all concentrated around their posterior mean when exploiting the forecast data; otherwise, the intervals are dispersed. 11 In the literature there are two closely related studies, Milani and Rajbhandari (2012) and Miyamoto and Nguyen (2018), both of which estimate DSGE models with news shocks using forecast data as well as actual data. All the three studies, including ours, reach the same conclusion that exploiting forecast data in addition to actual data leads to more precise estimates of news shocks and other model parameters.…”