What's so Great about the Great Moderation? A Multi-Country Investigation of Time-Varying Volatilities of Output Growth and Inflation

Changes in volatility of output growth and inflation are examined for eight countries with at least 140 years of uninterrupted data. Time-varying parameter vector autoregressions are used to estimate standard deviations of each variable. Both volatilities rise quickly with World War I and its aftermath, stay relatively high until the end of World War II, and then drop rapidly until the mid- to late 1960s. This Postwar Moderation typically yields the largest decline in output growth volatilities. For all countries, volatilities of both output growth and inflation fall more during this Postwar Moderation than during the Great Moderation, and often the difference is huge. Both volatilities typically reach their lowest levels following the Great Moderation. The Great Moderation often counteracts an increase in volatility that took place in the 1970s, particularly for inflation. In nearly all the countries in our sample, the recent financial crisis has eliminated the stability gains associated with the Great Moderation, and sometimes it has even eroded gains made during the Postwar Moderation. Periods in which a fixed exchange rate system was widespread are associated with relatively low volatilities for both variables. Based on our structural VAR identification, permanent shocks to output account for nearly all of the fluctuations in the volatility of output growth while shocks that have only a temporary effect on output explain most of the fluctuations in inflation volatility. These last two findings suggest that changes in the volatility for each variable are primarily driven by a fundamentally different type of disturbance.The Great Moderation, The Postwar Moderation, stochastic volatility, permanent-transitory shock decompositions, Markov Chain Monte Carlo, structural vector autoregressions.

The Time Varying Effects of Permanent and Transitory Shocks to Real Output

Annual changes in volatility of U.S. real output growth and inflation are documented in data from 1870 to 2009 using a time varying parameter VAR model. Both volatilities rise quickly with World War I and its aftermath, stay relatively high until the end of World War II and drop rapidly until the mid to late-1960s. This Postwar Moderation represents the largest decline in volatilities in our sample, much greater than the Great Moderation that began in the 1980s. Fluctuations in output growth volatility are primarily associated with permanent shocks to output while fluctuations in inflation volatility are primarily accounted for by temporary shocks to output. Conditioning on temporary shocks, inflation and output growth are positively correlated. This finding and the ensuing impulse responses are consistent with an aggregate demand interpretation for the temporary shocks. Our model suggests aggregate demand played a key role in the changes in inflation volatility. Conversely, the two variables are negatively correlated when conditioning on permanent shocks, suggesting that these disturbances are associated primarily with aggregate supply. Our results suggest that aggregate supply played an important role in output volatility fluctuations. Most of the impulse responses support an aggregate supply interpretation of permanent shocks. However, for the pre-World War I period, we find that at longer horizons a permanent increase in output is generally associated with an increase in the price level that is frequently statistically significant. This evidence suggests aggregate demand may have had a long-run positive effect on output during the pre-World War I period.The Great Moderation, stochastic volatility, permanent-transitory decompositions, Markov Chain Monte Carlo, structural vector autoregressions.

Greater Moderations

We decompose a 219 year sample of U.S. real output data into permanent and transitory shocks. We find reductions in volatility of output growth and inflation, starting in the mid 1980s, consistent with the “Great Moderation” noted by many others. More importantly, we find periods of even more substantial reduction in volatilities. Output growth and inflation volatilities fell by 60% and 76%, respectively, from shortly after World War II until the mid 1960s. We label this period the Postwar Moderation. Also, the largest reduction in inflation volatility occurred during the Classical Gold Standard period. Results from our empirical model suggest that aggregate supply shocks account for most of the changes in output growth volatility while aggregate demand shocks account for most of the changes in inflation volatility. The timing of the Postwar Moderation, which began almost immediately after passage of the Employment Act of 1946, suggests that policy played a crucial role in this period’s impressive decline in volatilities.The Great Moderation, stochastic volatility, structural VAR

Embedding Rational Expectations in a Structural VAR: Internal and External Instruments for Set Identification

We propose a novel approach that embeds Rational Expectations (RE) into a low-dimensional structural vector autoregression (SVAR). We establish an instrumental variable procedure internal to the SVAR founded on a purely theoretical framework, which does not rely on any mapping strategy to a reduced form. Alternatively, a separate strategy considers data external to the SVAR to aid in the identification of structural shocks on a purely empirical basis. We report clouds of responses from a RE-consistent theoretical model as well as regions of plausible responses from the empirical approach. We conclude that a Taylor Rule characterization of monetary policy shocks remains relevant when the theoretical RE-SVAR is properly augmented with information from fluctuations—or momentous events—in markets that garnered increased attention since 2008, such as reserves and various money markets

Predictability and underreaction in industry-level returns: Evidence from commodity markets

This paper finds significant evidence that commodity log price changes can predict industry-level returns for horizons of up to six trading weeks (30 days). We find that for the 1985-2010 period, 40 out of 49 U.S. industries can be predicted by at least one commodity. Our findings are consistent with Hong and Stein’s (1999) “underreaction hypothesis.” Unlike prior literature, we pinpoint the length of underreaction by employing daily data. We provide a comprehensive examination of the return linkages among 25 commodities and 49 industries. This provides a more detailed investigation of underreaction and investor inattention hypotheses than most related literature. Finally, we implement data-mining robust methods to assess the statistical significance of industry returns reactions to commodity log price changes, with precious metals (such as gold) featuring most prominently. While our results indicate modest out-of-sample forecast ability, they confirm evidence that commodity data can predict equity returns more than four trading weeks ahead

A Refinement of the Relationship between Economic Growth and Income Inequality in Developing Countries

There is mixed evidence in the literature of a clear relationship between income inequality and economic growth. Most of that work has focused almost exclusively on developed economies. In what we believe to be a first effort, our emphasis is solely on developing economics, which we classify as high-income and low-income developing countries (HIDC and LIDC). We make such distinction on theoretical and empirical grounds. Empirically, the World Bank has classified developing economies in this manner since 1978. The data in our sample is also supportive of such classifications. We provide a theoretical scaffolding that uses asymmetric credit constraints as a premise for separating developing economies in such a way. We find strong evidence of a negative relationship between income inequality and economic growth in LIDC to be in stark contrast with a positive inequality-growth relationship for HIDC. Both correlations are statistically significant across multiple econometric specifications. These results are robust to degree of persistence in the variables of interest as well as a measure threshold of income that is estimated endogenously for our sample