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Working Paper
Business Cycles and Low-Frequency Fluctuations in the US Unemployment Rate
I show that business cycles can generate most of the low-frequency movements in the unemployment rate. First, I provide evidence that the unemployment rate is stationary, while its flows have unit roots. Then, I model the log unemployment rate as the error correction term of log labor flows in a vector error correction model (VECM) with intercepts that change over the business cycle. Feeding historical expansions and recessions into the VECM generates large low-frequency movements in the unemployment rate. Frequent recessions from the late 1960s to the early 1980s interrupt labor market ...
Journal Article
The Discrepancy Between Expenditure- and Income-Side Estimates of US Output
The United States has two measures of economic output: gross domestic product (GDP) and gross domestic income (GDI). While these are conceptually equivalent, their initial estimates differ because these initial estimates are computed from different and incomplete data sources. I study the difference, or “statistical discrepancy,” between GDP and GDI in percent and document three features. First, its size does not materially shrink on average as more data become available. Second, the size of the initial discrepancy in absolute value does not predict the size of the discrepancy in absolute ...
Journal Article
Lingering Residual Seasonality in GDP Growth
Measuring economic growth is complicated by seasonality, the regular fluctuation in economic activity that depends on the season of the year. The Bureau of Economic Analysis uses statistical techniques to remove seasonality from its estimates of GDP, and, in 2015, it took steps to improve the seasonal adjustment of data back to 2012. I show that residual seasonality in GDP growth remains even after these adjustments, has been a longer-term phenomenon, and is particularly noticeable in the 1990s. The size of this residual seasonality is economically meaningful and has the ability to change the ...
Journal Article
Productivity Growth and Real Interest Rates in the Long Run
Despite the unemployment rate's return to low levels, inflation-adjusted or "real" interest rates have remained negative. One popular explanation for persistently negative real interest rates is that long-run productivity growth has slowed. I study the long-run relationship between real interest rates and productivity growth from 1914 to 2016 and find a negative correlation between these two variables. Hence, low productivity growth has been historically associated with high real interest rates. Since World War II, the correlation between these variables has been near zero. This suggests ...
Working Paper
Some Evidence on Secular Drivers of US Safe Real Rates
We study long-run correlations between safe real interest rates in the United States and over 20 variables that have been hypothesized to influence real rates. The list of variables is motivated by the familiar intertermporal IS equation, by models of aggregate savings and investment, and by reduced form studies. We use annual data, mostly from 1890 to 2016. We find that safe real interest rates are correlated as expected with demographic measures. For example, the long-run correlation with labor force hours growth is positive, which is consistent with overlapping generations models. For ...
Working Paper
The Effects of the Federal Reserve Chair’s Testimony on Interest Rates and Stock Prices
We study how congressional testimony about monetary policy by the Chair of the Board of Governors of the Federal Reserve System affects interest rates and stock prices. First, we study testimony associated with the Federal Reserve’s Monetary Policy Reports (MPRs) to Congress. Testimony for a particular MPR is usually given on two days, one day for each chamber of Congress. We separately study the first day and second day of MPR testimony. We also study testimonies not associated with MPRs but that are still related to monetary policy. We find that first-day MPR testimonies cause the largest ...
Journal Article
Can Yield Curve Inversions Be Predicted?
An inverted Treasury yield curve?a yield curve where short-term Treasury interest rates are higher than long-term Treasury interest rates?is a good predictor of recessions. Because of this, economists and policymakers often assess the risk of a yield curve inversion when the yield curve is flattening. I study the forecastability of yield curve inversions. Professional forecasters did not predict the beginning of the yield curve inversions prior to the 1990?1991, 2001, and 2008?2009 recessions. In all three cases, professional forecasters failed to predict the magnitude of the rise in ...
Working Paper
Proxy SVARs: Asymptotic Theory, Bootstrap Inference, and the Effects of Income Tax Changes in the United States
Proxy structural vector autoregressions (SVARs) identify structural shocks in vector autoregressions (VARs) with external proxy variables that are correlated with the structural shocks of interest but uncorrelated with other structural shocks. We provide asymptotic theory for proxy SVARs when the VAR innovations and proxy variables are jointly ?-mixing. We also prove the asymptotic validity of a residual-based moving block bootstrap (MBB) for inference on statistics that depend jointly on estimators for the VAR coefficients and for covariances of the VAR innovations and proxy variables. These ...
Working Paper
Identifying Structural VARs with a Proxy Variable and a Test for a Weak Proxy
This paper develops a simple estimator to identify structural shocks in vector autoregressions (VARs) by using a proxy variable that is correlated with the structural shock of interest but uncorrelated with other structural shocks. When the proxy variable is weak, modeled as local to zero, the estimator is inconsistent and converges to a distribution. This limiting distribution is characterized, and the estimator is shown to be asymptotically biased when the proxy variable is weak. The F statistic from the projection of the proxy variable onto the VAR errors can be used to test for a weak ...
Working Paper
Random Walk Forecasts of Stationary Processes Have Low Bias
We study the use of a zero mean first difference model to forecast the level of a scalar time series that is stationary in levels. Let bias be the average value of a series of forecast errors. Then the bias of forecasts from a misspecified ARMA model for the first difference of the series will tend to be smaller in magnitude than the bias of forecasts from a correctly specified model for the level of the series. Formally, let P be the number of forecasts. Then the bias from the first difference model has expectation zero and a variance that is O(1/P-squared), while the variance of the bias ...