Abstract: This paper compares eight widely used methods for estimating the output gap, ranging from simple deterministic trends to state space models, using both revised and real time U.S. quarterly data from 1980 onward. The resulting measures differ heavily across approaches. Average gap estimates vary by nearly four percentage points, volatility differs by an order of magnitude, and correlations across methods span from strongly positive to negative. Stability across data vintages also varies substantially. Hamilton type filters show relatively strong agreement between real time and final estimates, while simpler trend based methods are considerably less stable. These differences matter for empirical inference. The choice of output gap measure has important implications for Phillips curve estimates and for forecasting performance. Beveridge Nelson decompositions display strong predictive power for inflation when estimated using revised data but perform less well in real time, whereas refined Beveridge Nelson and modified Hamilton filters deliver more consistent results across vintages. Time varying analysis shows that the relationship between economic slack and inflation strengthens during periods of macroeconomic stress, including the early 1990s recession, the global financial crisis, and the post-pandemic period, rather than declining monotonically. For output growth forecasting, HP filter gaps reduce forecast errors using revised data, while unobserved components models perform best in real time. Although Beveridge Nelson based measures are informative for inflation, they tend to worsen growth forecasts. Combining forecasts across gap measures, particularly using Bates Granger weights, yields more reliable performance by offsetting weaknesses of individual methods. Overall, the findings highlight that methodological uncertainty in measuring slack translates directly into policy uncertainty, cautioning against exclusive reliance on any single output gap estimate.
Work In Progress
Impact of Financial Development on Industrial R&D: Evidence from OECD Countries, with Rebecca Neumann
Abstract:Whether financial development promotes industrial innovation depends not just on how developed a country’s financial system is, but on which dimensions of that system are well developed. This paper examines how depth, access, and ef- ficiency of both financial institutions and financial markets shape R&D investment across industries that differ in their reliance on external finance. Using industry- level data from the ISIC Rev. 4 classification across 18 OECD countries from 1995 to 2019, and drawing on the IMF’s multidimensional Financial Development In- dex, we analyze how country-level financial development measures interact with an industry-level external finance dependence measure to influence R&D intensity measured relative to output and value added. Our findings show that the overall level of financial development matters primarily through depth. In particular, the depth of financial institutions and, to a lesser extent, the depth of financial mar- kets significantly raise R&D intensity in industries that depend more heavily on external funding. Measures of access and efficiency display little systematic effect. The results are strongest within manufacturing industries, where innovation activ- ity is concentrated. These findings highlight the importance of financial structure and, in particular, the scale and capacity of financial intermediation, in shaping the allocation of innovative investment across industries.