The Decline in U.S. Output Volatility (original) (raw)

The Decline In US Output Volatility: Structural Changes in Inventories or Sales?

2003

Explanations for the decline in US output volatility since the mid-1980s comprise: "better policy", "good luck", and technological change. Our multiple break estimates suggest that reductions in volatility since the mid-1980s extend not only to manufacturing inventories but also to sales. This finding, along with a concentration of the reduction in the volatility of inventories in material and supplies, and the lack of a significant break in the inventory-sales covariance, imply that new inventory technology cannot account for the majority of the decline in output volatility.

The Decline in U.S. Output Volatility: Structural Changes and Inventory Investment

Journal of Business & Economic Statistics, 2005

Explanations for the decline in U.S. output volatility since the mid-1980s include: “better policy,” “good luck,” and technological change. Our multiple-break estimates suggest that reductions in volatility since the mid-1980s extend not only to manufacturing inventories, but also to sales. This finding, along with a concentration of the reduction in the volatility of inventories in materials and supplies and the lack of a significant break in the inventory–sales covariance, imply that new inventory technology cannot account for most of the decline in output volatility.

Output Fluctuations in the United States: What Has Changed since the Early 1980's

American Economic Review, 2000

We document a structural decline in the volatility of real U.S. GDP growth in the¯rst quarter of 1984. As a means of understanding the dramatic volatility reduction, we decompose output growth by major product type and provide evidence that the break emanates from a reduction in the volatility of durable goods production. We further show that the break in durables is roughly coincident with a break in the proportion of durables accounted for by inventories. We note that the break in output volatility a®ects the implementation of a wide range of simulation and econometric techniques and o®er one important illustration of this in the context of a regime-switching model of output growth.

Inventory dynamics and business cycles: what has changed

2002

By historical standards, the U.S. economy has experienced a period of remarkable stability since the mid-1980s. One explanation attributes the diminished variability of economic activity to information-technology led improvements in inventory management. Our results, however, indicate that the changes in inventory dynamics since the mid-1980s played a reinforcing-rather than a leading-role in the volatility reduction. A decomposition of the reduction in the volatility of manufacturing output shows that it almost entirely reflects a decline in the variance of the growth contribution of shipments. And although the volatility of total inventory investment has fallen, the decline occurred well before the mid-1980s and was driven by the reduced variability of † We thank

What Caused the Decline in U.S. Business Cycle Volatility?

2005

This paper investigates the sources of the widely noticed reduction in the volatility of American business cycles since the mid 1980s. Our analysis of reduced volatility emphasizes the sharp decline in the standard deviation of changes in real GDP, of the output gap, and of the inflation rate. The primary results of the paper are based on a small three-equation macro model that includes equations for the inflation rate, the nominal Federal Funds rate, and the change in the output gap. The development and analysis of the model goes beyond the previous literature in two directions. First, instead of quantifying the role of shocks-in-general, it decomposes the effect of shocks between a specific set of supply shock variables in the model's inflation equation, and the error term in the output gap equation that is interpreted as representing "IS" shifts or "demand shocks". It concludes that the reduced variance of shocks was the dominant source of reduced business-cycle volatility. Supply shocks accounted for 80 percent of the volatility of inflation before 1984 and demand shocks the remainder. The high level of output volatility before 1984 is accounted for roughly two-thirds by the output errors (demand shocks) and the remainder by supply shocks. The output errors are tied to the paper's initial decomposition of the demand side of the economy, which concludes that three sectors residential and inventory investment and Federal government spending, account for 50 percent in the reduction in the average standard deviation of real GDP when the 1950-83 and 1984-2004 intervals are compared. The second innovation in this paper is to reinterpret the role of changes in Fed monetary policy. Previous research on Taylor rule reaction functions identifies a shift after 1979 in the Volcker era toward inflation fighting with no concern about output, and then a shift in the Greenspan era to a combination of inflation fighting along with strong countercyclical responses to positive or negative output gaps. Our results accept this characterization of the Volcker era but find that previous estimates of Greenspan-era reaction functions are plagued by positive serial correlation. Once a correction for serial correlation is applied, the Greenspan-era reaction function looks almost identical to the pre-1979 "Burns" reaction function!

Essays on Time-Varying Volatility and Structural Breaks in Macroeconomics and Econometrics

2018

This paper documents a new stylized fact—the correlation between labour productivity and real business investment in the U.S. data since 1990 is−0.1. This correlation was 0.54 in the post-WWII data until the end of 1980s. The response of investment to identified technology shocks have switched signs across these two sub-periods from positive to negative, whereas the response to a non-technology shock has remained approximately the same. Since the volatility of technology shocks has decreased less relative to non-technology shock over the two sub-periods, we explore the hypothesis whether the relatively more volatile technology shocks and the negative response of investment can together account for the decreased correlation. We consider a canonical DSGE model and simulate data under a variety of assumptions about the parameters representing structural features and volatility of shocks. The results show that although the smaller decline in the volatility of technology shocks relative ...

Secular Volatility Decline of the U.S. Composite Economic Indicator

2002

The paper treats the issue of decreasing volatility of the U.S. economy observed since the mid-1980s. As a measure of volatility the residual variance of a composite economic indicator with Markov switching is used which. Two additional regimes are included capturing the secular shift in volatility. The mixed frequency is allowed for permitting the use of both monthly and quarterly component series. The low-intercept regime probabilities comply to the NBER business cycle dating, while the low-variance regime probabilities indicate the beginning of 1984 as a possible date of the structural break in volatility.

Testing for volatility changes in US macroeconomic time series

Review of Economics and Statistics, 2004

We test for a change in the volatility of 214 US macroeconomic time series over the period . We find that about 80% of these series have experienced a break in unconditional volatility during this period. Even though more than half of the series experienced a break in conditional mean, most of the reduction in volatility appears to be due to changes in conditional volatility. Our results are robust to controlling for business cycle nonlinearity in both mean and variance. Volatility changes are more appropriately characterized as an instantaneous break rather than as a gradual change. Nominal variables such as inflation and interest rates experienced multiple volatility breaks and witnessed temporary increases in volatility during the 1970s. Based upon this evidence, we conclude that the increased stability of economic fluctuations is a wide-spread phenomenon. Timmermann, three anonymous referees and the editor James Stock for helpful comments and discussion. Any remaining errors and shortcomings are ours.

Production chains and aggregate output volatility

International Journal of Production Economics, 2013

A production chain's aggregate output volatility depends upon a number of factors including the number of firms in a chain, the management strategy of the firms, the point at which products are differentiated, lead times, and the persistence of demand shocks. The influence of these factors is quantified here with an N-firm model of the production chain. Under pure production-to-stock (PTS), the firms in the chain respond simultaneously to a demand shock producing a positive covariance across firms that raises aggregate volatility. Under production to order (PTO), the chain's response to a demand shock is staggered but when demand shocks are persistent there is a catch-up effect that raises the volatility of each firm's production above that of a firm that under PTS. Nevertheless, PTO chains typically exhibit less volatility than PTS chains thanks to the covariance effect. Further simulations demonstrate that the production chain stabilizes as the number of production cycles in an observation period increases.