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We show that the risk-shock business cycle model of Fernández-Villaverde et al. (2011) must be recalibrated because it underpredicts the targeted business cycle moments by a factor of three once a time aggregation error is corrected. Recalibrating the corrected model for the benchmark case of Argentina, the peak response and the contribution of interest rate risk shocks to business cycle volatility increase. However, the recalibrated model does worse in capturing the business cycle properties of net exports once an additional error in the computation of net exports is corrected. (JEL E13, E20, E32, E43, F32, F43, O11)

We ask what specification of preferences can account for the changes in the expenditure shares of broad sectors that are associated with the process of structural transformation in the U.S. since 1947. Following the tradition of the expenditure systems literature, we first calibrate utility function parameters using NIPA data on final consumption expenditure. We find that a Stone-Geary specification fits the data well. While useful, this exercise does not tell the researcher what utility function to use in a model that posits sectoral production functions in value added form. We therefore develop a method to calculate the value added components of consumption categories that are consistent with value added production functions, and use these data to calibrate a utility function over sectoral consumption value added. We find that a Leontief specification fits the data well. Interestingly, the two specifications display very different properties: for final consumption expenditure income effects are the dominant force behind changes in expenditure shares whereas for consumption value added relative price effects are dominant.

This paper estimates the dynamic effects of changes in taxes in the United States. We distinguish between changes in personal and corporate income taxes and develop a new narrative account of federal tax liability changes in these two tax components. We develop an estimator which uses narratively identified tax changes as proxies for structural tax shocks and apply it to quarterly post-WWII data. We find that short run output effects of tax shocks are large and that it is important to distinguish between different types of taxes when considering their impact on the labor market and on expenditure components. (JEL E23, E62, H24, H25, H31, H32)

An earlier study (Barro 2006) applied the Thomas A. Rietz (1988) insight on rare eco? nomic disasters to explain the equity premium and related asset-pricing puzzles. Key param? eters were the probability, p, of disaster and the distribution of disaster sizes, b. In the main analysis, p and the ?-distribution were assumed to be time invariant. An extension to time-vary? ing p is in Xavier Gabaix (2008). Because large macroeconomic disasters are rare, pinning down p and the ?-distribution from historical data requires long time series for many countries, along with the assumption of rough parameter stability over time and across countries. Barro (2006) relied on the long-term international GDP data for 35 countries from Angus Maddison (2003). Using the definition of an economic disaster as a peak-to-trough fall in per capita GDP by at least 15 percent, 60 disas? ters were found, corresponding to p ?* 0.017 per year. The average disaster size was 29 percent, and the empirical size distribution was used to calibrate a model of asset pricing. The underlying asset-pricing theory relates to consumption, C, rather than GDP. This distinc? tion is especially important for wars. For exam? ple, in the United Kingdom during the two world wars, GDP increased while C fell sharply?the difference representing mostly added military spending. Maddison (2003) provides national-accounts information only for GDP. Our initial idea was to add consumption, C, which we measure by personal consumer expenditure because of diffi? culties in separating durables from nondurables in the long-term data. We have not assembled data on government consumption, some of which may substitute for C and, thereby, affect asset pricing. However, this substitution is prob? ably unimportant for military outlays, which are the type of government spending that moves a lot during some disaster events. Maddison (2003) represents a monumental contribution for international studies using long term GDP data. However, although much of the information is sound, close examination revealed many problems. Specifically, Maddison tends to fill in missing data with doubtful assumptions, and this practice is often significant for major crises. As examples, Maddison assumed that Belgium's GDP during WWI and WWII moved with France's; that Mexico's GDP between 1910 and 1920, including the Revolution and Civil War, followed a smooth trend (with no crisis); that GDP for Colombia and Peru over more than a decade moved with the average of Brazil and Chile; and that GDP in Germany for the crucial years 1944-1946 followed a linear trend. There are also some mismatches between original works and published series for GDP in Japan at the end of WWII and Greece during WWII and its Civil War. Given these difficulties, our project expanded to estimating long-term GDP for many countries. The Maddison informa? tion was often usable, but superior estimates can be constructed in many cases. Also, results from recent major long-term national-accounts projects for some countries are now available, including Argentina, Brazil, Chile, Colombia, Greece, Norway, Spain, Sweden, and Taiwan. We are dealing with long-term national accounts data for 41 countries, but the current study applies to the 21 for which we have, thus far, assembled annual data on C and GDP from before WWI to 2006. (See Table 1 for a list of included countries and starting years.) We begin + Discussant: John Campbell, Harvard University.