This paper suggests a new procedure for evaluating the fit of a dynamic structural economic model. The procedure begins by augmenting the variables in the model with just enough stochastic error so that the model can exactly match the second moments of the actual data. Measures of fit for the model can then be constructed on the basis of the size of this error. The procedure is applied to a standard real business cycle model. Over the business cycle frequencies, the model must be augmented with a substantial error to match data for the postwar U.S. economy. Lower bounds on the variance of the error range from 40 percent to 60 percent of the variance in the actual data.

This paper suggests a new procedure for evaluating the fit of a dynamic structural economic model. The procedure begins by augmenting the variables in the model with just enough stochastic error so that the model can exactly match the second moments of the actual data. Measures of fit for the model can then be constructed on the basis of the size of this error. The procedure is applied to a standard real business cycle model. Over the business cycle frequencies, the model must be augmented with a substantial error to match data for the postwar U.S. economy. Lower bounds on the variance of the error range from 40 percent to 60 percent of the variance in the actual data.

Using factor methods, we decompose industrial production (IP) into components arising from aggregate and sector-specific shocks. An approximate factor model finds that nearly all of IP variability is associated with common factors. We then use a multisector growth model to adjust for the effects of input-output linkages in the factor analysis. Thus, a structural factor analysis indicates that the Great Moderation was characterized by a fall in the importance of aggregate shocks while the volatility of sectoral shocks was essentially unchanged. Consequently, the role of idiosyncratic shocks increased considerably after the mid-1980s, explaining half of the quarterly variation in IP.

We describe how capital accumulation and the network structure of US production interact to amplify the effects of sectoral trend growth rates in total factor productivity and labor on trend GDP (gross domestic product) growth. We derive expressions that conveniently summarize this long-run amplification effect by way of sectoral multipliers. We estimate that sector-specific factors have historically accounted for approximately three-fourths of long-run changes in GDP growth. Trend GDP growth fell by nearly 3 percentage points over the postwar period, with especially significant contributions from the Construction sector in 1950–80 and the Durable Goods sector in 2000–2018. No sector has contributed any steady significant increase to the trend growth rate of GDP in the past 70 years.