Because firms invest heavily in R&D, software, brands, and other intangible assets—at a rate close to that of tangible assets—changes in GDP, which does not include all intangible investments, understate the actual changes in total output. If labor inputs are more precisely measured, then it is possible to observe little change in measured total factor productivity (TFP) coincidentally with large changes in hours and investment. The output mismeasurement leaves business cycle modelers with large and unexplained labor wedges accounting for most of the fluctuations in aggregate data. To address this issue, I incorporate intangible investments into a multi-sector general equilibrium model and use data from an updated U.S. input and output table to parameterize income and cost shares, with intangible investments reassigned from intermediate to final uses. I employ maximum likelihood methods and quarterly observations on sectoral gross outputs for the United States to estimate processes for latent sectoral TFPs that have common and sector-specific components. I do not use aggregate hours to estimate TFPs but find that the predicted hours series compares closely with the actual series and accounts for roughly two-thirds of its standard deviation. I find that sector-specific shocks and industry linkages play an important role in accounting for fluctuations and comovements in aggregate and industry-level U.S. data, and I find that at business-cycle frequencies, the model's common component of TFP is not correlated with the standard measures of aggregate TFP used in the macroeconomic literature. Adding financial frictions and stochastic shocks to financing constraints has a negligible impact on the results.
Published in _Review of Economic Dynamics_ (Vol. 37, Supp. 1, August 2020, pp. S147-166), https://doi.org/10.1016/j.red.2020.06.007.