Interpreting Permanent and Transitory Shocks to Output When Aggregate Demand May Not Be Neutral in the Long-run

I examine the statistical model of permanent and transitory shocks to output under the following structural assumptions: An aggregate supply shock that raises output will cause the price level to fall and an aggregate demand shock that initially raises output will cause the price level to rise. No assumption is made about the long-run effect of aggregate demand on output. Based on these assumptions I obtain three primary results. First, if a permanent increase in output is associated with an increase in the price level, then aggregate demand shocks have a positive long-run effect on output. Second, the output variance explained by permanent shocks exceeds the variance attributable to aggregate supply when aggregate demand shocks have a positive effect on output in the long run. Third, permanent and transitory shocks will affect price and output in qualitatively the same way as aggregate supply and aggregate demand shocks, respectively, from textbook macro theory over a range of values for the structural parameter describing the long-run effect of aggregate demand on output. These results are used to explain and interpret empirical findings from the literature and to motivate directions for future researchvector autoregression, identification restriction, permanent and transitory shocks, supply and demand shocks

Structural approaches to vector autoregressions

Vector autoregression

Interpreting Permanent Shocks to Output When Aggregate Demand May Not be Neutral in the Long Run

This paper studies Blanchard and Quah’s (1989) statistical model of permanent and transitory shocks to output using a set of arguably more plausible structural assumptions. Economists typically motivate this statistical model by assuming aggregate demand shocks have no long-run effect on the level of output. Many economic theories are, however, inconsistent with that assumption. We reinterpret this statistical model assuming a positive shock to aggregate supply lowers the price level and in the long run raises output while a positive shock to aggregate demand raises the price level. No assumption is made about the long-run output effect of aggregate demand. Based on these assumptions, we show that a puzzling finding from the empirical literature implies that a positive (negative) aggregate demand shock had a long-run positive (negative) effect on the level of output in a number of pre-World War I economies.vector autoregression, identification restrictions, moving average representations, aggregate demand and supply theory, permanent and transitory shock decomposition

Superneutrality in postwar economies

A structural vector autoregression is employed to estimate the real output level response to permanent inflation shocks. We identify the model by assuming that in the long run, inflation is a monetary phenomenon. Well-known economic theory is used to establish this identification restriction. The model is estimated for a sample of 16 countries from the larger pool based on data quality, existence of long uninterrupted series on output and inflation, and evidence that the country experienced permanent shocks to inflation and output. The VAR is estimated for each country separately. We find some evidence of non-superneutrality, particularly for some low inflation countries, but in general our results suggest that superneutrality describes well most of the postwar economies we study.Regression analysis

Rethinking the Liquidity Puzzle: Application of a New Measure of the Economic Money Stock

Historically, attempts to solve the liquidity puzzle have focused on narrowly defined monetary aggregates, such as non-borrowed reserves, the monetary base, or M1. Many of these efforts have failed to find a short-term negative correlation between interest rates and monetary policy innovations. More recent research uses sophisticated macroeconomic and econometric modeling. However, little research has investigated the role measurement error plays in the liquidity puzzle, since in nearly every case, work investigating the liquidity puzzle has used one of the official monetary aggregates, which have been shown to exhibit significant measurement error. This paper examines the role that measurement error plays in the liquidity puzzle by (i) providing a theoretical framework explaining how the official simple-sum methodology can lead to a liquidity puzzle, and (ii) testing for the liquidity effect by estimating an unrestricted VAR.Liquidity Puzzle, Monetary Policy, Monetary Aggregation, Money Stock, Divisia Index Numbers

Forecast Design in Monetary Capital Stock Measurement

We design a procedure for measuring the United States capital stock of money implied by the Divisia monetary aggregate service flow, in a manner consistent with the present-value model of economic capital stock. We permit non-martingale expectations and time varying discount rates. Based on Barnett’s (1991) definition of the economic stock of money, we compute the U.S. economic stock of money by discounting to present value the flow of expected expenditure on the services of monetary assets, where expenditure on monetary services is evaluated at the user costs of the monetary components. As a theoretically consistent measure of money stock, our economic stock of money nests Rotemberg, Driscoll, and Poterba’s (1995) currency equivalent index as a special case, under the assumption of martingale expectations. To compute the economic stock of money without imposing martingale expectations, we define a procedure for producing the necessary forecasts based on an asymmetric vector autoregressive model and a Bayesian vector autoregressive model. In application of this proposed procedure, Barnett, Chae, and Keating (2005) find the resulting capital-stock growth-rate index to be surprisingly robust to the modeling of expectations. Similarly the primary conclusions of this supporting paper regard robustness. We believe that further experiments with other forecasting models would further confirm our robustness conclusion. Different forecasting models can produce substantial differences in forecasts into the distant future. But since the distant future is heavily discounted in our stock formula, and since alternative forecasting formulas rarely produce dramatic differences in short term forecasts, we believe that our robustness result obviates prior concerns about the dependency of theoretical monetary capital stock computations upon forecasts of future expected flows. Even the simple martingale forecast, which has no unknown parameters and is easily computed with current period data, produces a discounted stock measure that is adequate for most purposes. Determining an easily measured extended index that can remove the small bias that we identify under the martingale forecast remains a subject for our future research. At the time that Milton Friedman (1969) was at the University of Chicago, the “Chicago School” view on the monetary transmission mechanism was based upon the wealth effect, called the “real balance effect” or “Pigou (1943) effect,” of open market operations. Our research identifies very large errors in the wealth effects computed from the conventional simple sum monetary aggregates and makes substantial progress in the direction of accurate measurement of monetary-policy wealth effects.Monetary aggregation, Divisia money aggregate, economic stock of money, user cost of money, currency equivalent index, Bayesian vector autoregression, asymmetric vector autoregression.

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.

Rethinking the Liquidity Puzzle: Application of a New Measure of the Economic Money Stock

Historically, attempts to solve the liquidity puzzle have focused on narrowly defined monetary aggregates, such as non-borrowed reserves, the monetary base, or M1. Many of these efforts have failed to find a short-term negative correlation between interest rates and monetary policy innovations. More recent research uses sophisticated macroeconomic and econometric modeling. However, little research has investigated the role measurement error plays in the liquidity puzzle, since in nearly every case, work investigating the liquidity puzzle has used one of the official monetary aggregates, which have been shown to exhibit significant measurement error. This paper examines the role that measurement error plays in the liquidity puzzle by (i) providing a theoretical framework explaining how the official simple-sum methodology can lead to a liquidity puzzle, and (ii) testing for the liquidity effect by estimating an unrestricted VAR.North-South, growth model, innovation assimilation