Search Results
Working Paper
Embodying Embodiment in a Structural, Macroeconomic Input-Output Model
This paper describes an attempt to build a regression-based system of labor productivity equations that incorporate the effects of capital-embodied technological change into IDLIFT, a structural, macroeconomic input-output model of the U.S. economy. Builders of regression-based forecasting models have long had difficulty finding labor productivity equations that exhibit the Neoclassical or Solowian property that movements in investment should cause accompanying movements in labor productivity. Theory dictates that this causation is driven by the effect of traditional capital deepening as well ...
Working Paper
Measuring Labor-Force Participation and the Incidence and Duration of Unemployment
The underlying data from which the U.S. unemployment rate, labor-force participation rate, and duration of unemployment are calculated contain numerous internal contradictions. This paper catalogs these inconsistencies and proposes a reconciliation. We find that the usual statistics understate the unemployment rate and the labor-force participation rate by about two percentage points on average and that the bias in the latter has increased since the Great Recession. The BLS estimate of the average duration of unemployment overstates by 50% the true duration of uninterrupted spells of ...
Working Paper
On the Wisdom of Crowds (of Economists)
We study the properties of macroeconomic survey forecast response averages as the number of survey respondents grows. Such averages are “portfolios” of forecasts. We characterize the speed and pattern of the gains from diversification as a function of portfolio size (the number of survey respondents) in both (1) the key real-world data-based environment of the U.S. Survey of Professional Forecasters, and (2) the theoretical model-based environment of equicorrelated forecast errors. We proceed by proposing and comparing various direct and model-based “crowd size signature plots,” which ...
Working Paper
On the Aggregation of Probability Assessments: Regularized Mixtures of Predictive Densities for Eurozone Inflation and Real Interest Rates
We propose methods for constructing regularized mixtures of density forecasts. We explore a variety of objectives and regularization penalties, and we use them in a substantive exploration of Eurozone inflation and real interest rate density forecasts. All individual inflation forecasters (even the ex post best forecaster) are outperformed by our regularized mixtures. From the Great Recession onward, the optimal regularization tends to move density forecasts’ probability mass from the centers to the tails, correcting for overconfidence.
Working Paper
The post-crisis slump in the Euro Area and the US: evidence from an estimated three-region DSGE model
The global financial crisis (2008-09) led to a sharp contraction in both Euro Area (EA) and US real activity, and was followed by a long-lasting slump. However, the post-crisis adjustment in the EA and the US shows striking differences?in particular, the EA slump has been markedly more protracted. We estimate a three-region (EA, US and Rest of World) New Keynesian DSGE model (using quarterly data for 1999-2014) to quantify the drivers of the divergent EA and US adjustment paths. Our results suggest that financial shocks were key drivers of the 2008-09 Great Recession, for both the EA and the ...
Working Paper
A Unified Framework to Estimate Macroeconomic Stars
We develop a flexible semi-structural time-series model to estimate jointly several macroeconomic "stars" — i.e., unobserved long-run equilibrium levels of output (and growth rate of output), the unemployment rate, the real rate of interest, productivity growth, the price inflation, and wage inflation. The ingredients of the model are in part motivated by economic theory and in part by the empirical features necessitated by the changing economic environment. Following the recent literature on inflation and interest rate modeling, we explicitly model the links between long-run survey ...
Working Paper
The Macroeconomy and the Yield Curve: A Nonstructural Analysis
We estimate a model with latent factors that summarize the yield curve (namely, level, slope, and curvature) as well as observable macroeconomic variables (real activity, inflation, and the stance of monetary policy). Our goal is to provide a characterization of the dynamic interactions between the macroeconomy and the yield curve. We find strong evidence of the effects of macro variables on future movements in the yield curve and much weaker evidence for a reverse influence. We also relate our results to a traditional macroeconomic approach based on the expectations hypothesis.
Working Paper
Recession forecasting using Bayesian classification
The authors demonstrated the use of a Nave Bayes model as a recession forecasting tool. The approach has a close connection to Markov-switching models and logistic regression but also important differences. In contrast to Markov-switching models, Nave Bayes treats National Bureau of Economic Research business cycle turning points as data rather than hidden states to be inferred by the model. Although Nave Bayes and logistic regression are asymptotically equivalent under certain distributional assumptions, the assumptions do not hold for business cycle data.
Working Paper
A Unified Framework to Estimate Macroeconomic Stars
We develop a flexible semi-structural time-series model to estimate jointly several macroeconomic "stars" -- i.e., unobserved long-run equilibrium levels of output (and growth rate of output), the unemployment rate, the real rate of interest, productivity growth, price inflation, and wage inflation. The ingredients of the model are in part motivated by economic theory and in part by the empirical features necessitated by the changing economic environment. Following the recent literature on inflation and interest rate modeling, we explicitly model the links between long-run survey expectations ...
Working Paper
Bayesian Estimation of Epidemiological Models: Methods, Causality, and Policy Trade-Offs
We present a general framework for Bayesian estimation and causality assessment in epidemiological models. The key to our approach is the use of sequential Monte Carlo methods to evaluate the likelihood of a generic epidemiological model. Once we have the likelihood, we specify priors and rely on a Markov chain Monte Carlo to sample from the posterior distribution. We show how to use the posterior simulation outputs as inputs for exercises in causality assessment. We apply our approach to Belgian data for the COVID-19 epidemic during 2020. Our estimated time-varying-parameters SIRD model ...