The main substantive finding of the recent structural vector autoregression literature with a differenced specification of hours (DSVAR) is that technology shocks lead to a fall in hours. Researchers have used these results to argue that business cycle models in which technology shocks lead to a rise in hours should be discarded. We evaluate the DSVAR approach by asking, is the specification derived from this approach misspecified when the data are generated by the very model the literature is trying to discard? We find that it is misspecified. Moreover, this misspecification is so great that it leads to mistaken inferences that are quantitatively large. We show that the other popular specification that uses the level of hours (LSVAR) is also misspecified. We argue that alternative state space approaches, including the business cycle accounting approach, are more fruitful techniques for guiding the development of business cycle theory.

We elaborate on the business cycle accounting method proposed by Chari, Kehoe, and McGrattan (2007), clear up some misconceptions about the method, and then apply it to compare the Great Recession across OECD countries as well as to the recessions of the 1980s in these countries. We have four main …findings. First, with the notable exception of the United States, Spain, Ireland, and Iceland, the Great Recession was driven primarily by the efficiency wedge. Second, in the Great Recession, the labor wedge plays a dominant role only in the United States, and the investment wedge plays a dominant role in Spain, Ireland, and Iceland. Third, in the recessions of the 1980s, the labor wedge played a dominant role only in France, the United Kingdom, Belgium, and New Zealand. Finally, overall in the Great Recession the efficiency wedge played a more important role and the investment wedge played a less important role than they did in the recessions of the 1980s.

The central finding of the recent structural vector autoregression (SVAR) literature with a differenced specification of hours is that technology shocks lead to a fall in hours. Researchers have used this finding to argue that real business cycle models are unpromising. We subject this SVAR specification to a natural economic test and show that when applied to data from a multiple-shock business cycle model, the procedure incorrectly concludes that the model could not have generated the data as long as demand shocks play a nontrivial role. We also test another popular specification, which uses the level of hours, and show that with nontrivial demand shocks, it cannot distinguish between real business cycle models and sticky price models. The crux of the problem for both SVAR specifications is that available data require a VAR with a small number of lags and such a VAR is a poor approximation to the model’s VAR.

This paper proposes a simple method for guiding researchers in developing quantitative models of economic fluctuations. We show that a large class of models, including models with various frictions, are equivalent to a prototype growth model with time-varying wedges that, at least at face value, look like time-varying productivity, labor taxes, and capital income taxes. We label the time-varying wedges as efficiency wedges, labor wedges, and investment wedges. We use data to measure these wedges and then feed them back into the prototype growth model. We then assess the fraction of fluctuations accounted for by these wedges during the great depressions of the 1930s in the United States, Germany, and Canada. We find that the efficiency and labor wedges in combination account for essentially all of the declines and subsequent recoveries. Investment wedges play, at best, a minor role.

We propose a simple method to help researchers develop quantitative models of economic fluctuations. The method rests on the insight that many models are equivalent to a prototype growth model with time-varying wedges which resemble productivity, labor and investment taxes, and government consumption. Wedges corresponding to these variables—efficiency, labor, investment, and government consumption wedges—are measured and then fed back into the model in order to assess the fraction of various fluctuations they account for. Applying this method to U.S. data for the Great Depression and the 1982 recession reveals that the efficiency and labor wedges together account for essentially all of the fluctuations; the investment wedge plays a decidedly tertiary role, and the government consumption wedge, none. Analyses of the entire postwar period and alternative model specifications support these results. Models with frictions manifested primarily as investment wedges are thus not promising for the study of business cycles. (See Additional Material for a response to Christiano and Davis (2006).)

The central puzzle in international business cycles is that fluctuations in real exchange rates are volatile and persistent. We quantity the popular story for real exchange rate fluctuations: they are generated by monetary shocks interacting with sticky goods prices. If prices are held fixed for at least one year, risk aversion is high, and preferences are separable in leisure, then real exchange rates generated by the model are as volatile as in the data and quite persistent, but less so than in the data. The main discrepancy between the model and the data, the consumption—real exchange rate anomaly, is that the model generates a high correlation between real exchange rates and the ratio of consumption across countries, while the data show no clear pattern between these variables.

The conventional wisdom is that monetary shocks interact with sticky goods prices to generate the observed volatility and persistence in real exchange rates. We investigate this conventional wisdom in a quantitative model with sticky prices. We find that with preferences as in the real business cycle literature, irrespective of the length of price stickiness, the model necessarily produces only a fraction of the volatility in exchange rates seen in the data. With preferences which are separable in leisure, the model can produce the observed volatility in exchange rates. We also show that long stickiness is necessary to generate the observed persistence. In addition, we show that making asset markets incomplete does not measurably increase either the volatility or persistence of real exchange rates.

We make three comparisons relevant for the business cycle accounting approach. We show that in theory representing the investment wedge as a tax on investment is equivalent to representing this wedge as a tax on capital income as long as the probability distributions over this wedge in the two representations are the same. In practice, convenience dictates that the underlying probability distributions over the investment wedge are different in the two representations. Even so, the quantitative results under the two representations are essentially identical. We also compare our methodology, the CKM methodology, to an alternative one used in Christiano and Davis (2006) as well as by us in early incarnations of the business cycle accounting approach. We argue that the CKM methodology rests on more secure theoretical foundations. Finally, we show that the results from the VAR-style decomposition of Christiano and Davis reinforce the results of the business cycle decomposition of CKM.

We make three comparisons relevant for the business cycle accounting approach. We show that in theory, representing the investment wedge as a tax on investment is equivalent to representing this wedge as a tax on capital income as long as the probability distributions over this wedge in the two representations are the same. In practice, convenience dictates that the underlying probability distributions over the investment wedge are different in the two representations. Even so, the quantitative results under the two representations are essentially identical. We also compare our methodology, the CKM methodology, to an alternative one used in Christiano and Davis (2006) and by us in early incarnations of the business cycle accounting approach. We argue that the CKM methodology rests on more secure theoretical foundations. Finally, we show that the results from the VAR-style decomposition of Christiano and Davis reinforce the results of the business cycle decomposition of CKM.

In the 1970s macroeconomists often disagreed bitterly. Macroeconomists have now largely converged on method, model design, and macroeconomic policy advice. The disagreements that remain all stem from the practical implementation of the methodology. Some macroeconomists think that New Keynesian models are on the verge of being useful for quarter-to-quarter quantitative policy advice. We do not. We argue that the shocks in these models are dubiously structural and show that many of the features of the model as well as the implications due to these features are inconsistent with microeconomic evidence. These arguments lead us to conclude that New Keynesian models are not yet useful for policy analysis.

Macroeconomists have largely converged on method, model design, reduced-form shocks, and principles of policy advice. Our main disagreements today are about implementing the methodology. Some think New Keynesian models are ready to be used for quarter-to-quarter quantitative policy advice; we do not. Focusing on the state-of-the-art version of these models, we argue that some of its shocks and other features are not structural or consistent with microeconomic evidence. Since an accurate structural model is essential to reliably evaluate the effects of policies, we conclude that New Keynesian models are not yet useful for policy analysis.

We construct a quantitative equilibrium model with price setting and use it to ask whether with staggered price setting monetary shocks can generate business cycle fluctuations. These fluctuations include persistent output fluctuations along with the other defining features of business cycles, like volatile investment and smooth consumption. We assume that prices are exogenously sticky for a short period of time. Persistent output fluctuations require endogenous price stickiness in the sense that firms choose not to change prices very much when they can do so. We find that for a wide range of parameter values the amount of endogenous stickiness is small. As a result, we find that in a standard quantitative business cycle model staggered price setting, by itself, does not generate business cycle fluctuations.

In recent financial crises and in recent theoretical studies of them, abrupt declines in capital inflows, or sudden stops, have been linked with large drops in output. Do sudden stops cause output drops? No, according to a standard equilibrium model in which sudden stops are generated by an abrupt tightening of a country’s collateral constraint on foreign borrowing. In this model, in fact, sudden stops lead to output increases, not decreases. An examination of the quantitative effects of a well-known sudden stop, in Mexico in the mid-1990s, confirms that a drop in output accompanying a sudden stop cannot be accounted for by the sudden stop alone. To generate an output drop during a financial crisis, as other studies have done, the model must include other economic frictions which have negative effects on output large enough to overwhelm the positive effect of the sudden stop.

Technical appendix for Working Paper 619, https://doi.org/10.21034/wp.619

This technical appendix supports Staff Report 223 and Staff Report 277.

We ask what fraction of the variation in incomes across countries can be accounted for by investment distortions. In our neoclassical growth model the relative price of investment to consumption is a good measure of the distortions. Using data on relative prices we estimate a stochastic process for distortions and compare the resulting variance of incomes in the model to that in the data. We find that the variation of incomes in the model is roughly 4/5 of the variability of incomes in the data. Our model does well in accounting for 6 key regularities on income and investment in the data.

The paper itself is followed by three appendices: Appendix 1 describing the log-likelihood function, Appendix 2 describing the construction of labor share of income associated with the production of consumption and investment goods, and the Data Appendix.