Working Papers
The Effect of Official Purchases of European Sovereign Debt (with M. De Pooter and R. Martin)
Measuring Monetary Policy Rules When Interest Rates are Stuck at the Zero Lower Bound (with J. Kim)
Under Review
We forecast a single time series using many predictor variables with a new estimator called the three-pass regression filter (3PRF). It is calculated in closed form and conveniently represented as a set of ordinary least squares regressions. 3PRF forecasts converge to the infeasible best forecast when both the time dimension and cross section dimension become large. This requires only specifying the number of relevant factors driving the forecast target, regardless of the total number of common (and potentially irrelevant) factors driving the cross section of predictors. We derive inferential theory in the form of limiting distributions for estimated relevant factors, predictive coefficients and forecasts, and provide consistent standard error estimators. We explore two empirical applications that exemplify the many predictor problem: Forecasting macroeconomic aggregates with a panel of economic indices, and forecasting stock market aggregates with many individual assets' price-dividend ratios. These, combined with a range of Monte Carlo experiments, demonstrate the 3PRF's forecasting power.
Returns and cash flow growth for the aggregate U.S. stock market are highly and robustly predictable. Using information extracted from the cross section of book-to-market ratios, we find an \emph{out-of-sample} forecasting R2 as high as 10% for returns and 25% for dividend growth at the annual frequency. We present a general economic framework linking aggregate market expectations to disaggregated valuation ratios in a dynamic latent factor system. To construct our forecasts we use a regression-based filter to extract factors driving aggregate expected returns and cash flow growth from the cross section of valuation ratios.
Our findings suggest that market discount rates are more volatile and less persistent than previously believed.
A long-standing challenge in the business cycle literature is explaining the near identical volatility of output and hours worked. We refer to this as the hours volatility puzzle. We conjecture that resolving this puzzle boils down to accounting for the volatility of age specific hours. Our motivation comes from observing that aggregate hours' fluctuations are disproportionately accounted for by the young, whose hours vary much more over the business cycle than the prime-aged. Differences in age-specific hours' volatility can arise from differences in labor supply, labor demand, or both. We first show that the joint behavior of hours and wages indicate the importance of age-specific labor demand differences over the cycle. We then investigate different expressions of this labor demand explanation in a quantitative framework. Based on both economic and econometric evidence we demonstrate that the most promising explanation features a greater diminishing marginal product of prime-age labor relative to young labor input in production. Our preferred model accounts for the volatility of age-specific hours and wages relative to output observed in the data. Moreover, it replicates the relative volatility of aggregate hours to output, providing a solution to the hours volatility puzzle.
The two sector model presented in this note suggests a simple structural decomposition of movements in the price of investment goods into exogenous and endogenous sources. The endogenous fluctuations arise in the presence of countercyclical markups which vary differently across the consumption and investment sectors. In turn, the movements in the markups are due to endogenous procyclical net business formation. The model, while being consistent with the countercyclicality of the price of investment goods, suggests that about a quarter of the movement in the price series can be attributed to this endogenous mechanism.
Refereed Publications
Economic agents who are uncertain of their economic model learn, and this learning is sensitive to the presence of data uncertainty. I investigate this idea in a framework that successfully describes inflation as a learning Federal Reserve's optimal policy, but fails to satisfactorily motivate these policy shifts. I modify the framework to account for data uncertainty: The learning process is made more sluggish by its presence. Consequently the estimated model provides an explanation for the rise and fall in inflation: the concurrent rise and fall in the perceived Philips curve trade-off between inflation and unemployment.
We introduce a novel method for estimating a monetary policy rule using macroeconomic news. We estimate directly the policy rule agents use to form their expectations by linking news' effects on forecasts of both economic conditions and monetary policy. Evidence between 1994 and 2007 indicates that the market-perceived Federal Reserve policy rule changed: the output response vanished, and the inflation response path became more gradual but larger in long-run magnitude. These response coefficient estimates are robust to measurement and theoretical issues with both potential output and the inflation target.
These papers and computer codes reflect the views and opinions of the author(s) and does not reflect on the views or opinions of the Federal Reserve Board, the Federal Reserve System, or their respective staffs.
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