Tracking Down the Business Cycle: A Dynamic Factor Model For Germany 1820-1913

We use a Bayesian dynamic factor model to measure Germany’s pre World War I economic activity. The procedure makes better use of existing time series data than historical national accounting. To investigate industrialization we propose to look at comovement between sectors. We find that Germany’s industrial sector developed earlier than stated in the literature, since after the 1860s agricultural time series do not comove with the business cycle anymore. Also, the bulk of comovement between 1820 and 1913 can be traced back to five out of 18 series representing industrial production, investment and demand for industrial inputs. Our factor is impressingly confirmed by a stock price index, leading the factor by 1-2 years. We also find evidence for early market integration in the 1820s and 1830s. Our business cycle dating aims to resolve the debate on German business cycle history. Given the often unsatisfactory quality of national accounting data for the 19th century we show the advantage of dynamic factor models in making efficient use of rare historical time series.Business Cycle Chronology; Imperial Germany; Dynamic Factor Models; Industrialization.

Bayesian Demographic Modeling and Forecasting: An Application to U.S. Mortality

We present a new way to model age-specific demographic variables with the example of age-specific mortality in the U.S., building on the Lee-Carter approach and extending it in several dimensions. We incorporate covariates and model their dynamics jointly with the latent variables underlying mortality of all age classes. In contrast to previous models, a similar development of adjacent age groups is assured allowing for consistent forecasts. We develop an appropriate Markov Chain Monte Carlo algorithm to estimate the parameters and the latent variables in an efficient one-step procedure. Via the Bayesian approach we are able to asses uncertainty intuitively by constructing error bands for the forecasts. We observe that in particular parameter uncertainty is important for long-run forecasts. This implies that hitherto existing forecasting methods, which ignore certain sources of uncertainty, may yield misleadingly sure predictions. To test the forecast ability of our model we perform in-sample and out-of-sample forecasts up to 2050, revealing that covariates can help to improve the forecasts for particular age classes. A structural analysis of the relationship between age-specific mortality and covariates is conducted in a companion paper.Demography, Age-specific, Mortality, Lee-Carter, Stochastic, Bayesian, State Space Models, Forecasts

The Influence of the Business Cycle on Mortality

We analyze the impact of short-run economic fluctuations on age-specific mortality using Bayesian time series econometrics and contribute to the debate on the procyclicality of mortality. For the first time, we examine the differing consequences of economic changes for all individual age classes. We employ a recently developed model to set up structural VARs of a latent mortality variable and of unemployment and GDP growth as main business cycle indicators. We find that young adults noticeably differ from the rest of the population. They exhibit increased mortality in a recession, whereas most of the other age classes between childhood and old age react with lower mortality to increased unemployment or decreased GDP growth. In order to avoid that opposed effects may cancel each other, our findings suggest to differentiate closely between particular age classes, especially in the age range of young adults. The results for the U.S. in the period 1956–2004 are confirmed by an international comparison with France and Japan. Long- term changes in the relationship between macroeconomic conditions and mortality are investigated with data since 1933.Age-specific Mortality, Business Cycle, Unemployment, Bayesian Econometrics, Health, Epidemiology

The U.S. Business Cycle, 1867-1995: Dynamic Factor Analysis vs. Reconstructed National Accounts

This paper presents insights on U.S. business cycle volatility since 1867 de- rived from diffusion indices. We employ a Bayesian dynamic factor model to obtain aggregate and sectoral economic activity indices. We find a remarkable increase in volatility across World War I, which is reversed after World War II. While we can generate evidence of postwar moderation relative to pre-1914, this evidence is not robust to structural change, implemented by time-varying factor loadings. We do find evidence of moderation in the nominal series, however, and reproduce the standard result of moderation since the 1980s. Our estimates broadly confirm the NBER historical business cycle chronology as well the National Income and Product Accounts, except for World War II where they support alternative estimates of Kuznets (1952).U.S. business cycle, volatility, dynamic factor analysis

The U.S. business cycle, 1867–2006: a dynamic factor approach

We estimate a Stock/Watson index of economic activity to assess U.S. business cycle volatility since 1867. We replicate the Great Moderation of the 1980s and 1990s and find exceptionally low volatility also in the Golden Age of the 1960s. Postwar moderation relative to pre-1914 occurs under constant but not time-varying factor loadings, suggesting structural change toward more volatile sectors. For comparable series, the U.S. postwar business cycle was as volatile overall as under the Classical Gold Standard, but much less so during the Great Moderation and the Golden Age

Can GDP measurement be further improved? Data revision and reconciliation

Recent years have seen many attempts to combine expenditure-side estimates of U.S. real output (GDE) growth with income-side estimates (GDI) to improve estimates of real GDP growth. We show how to incorporate information from multiple releases of noisy data to provide more precise estimates while avoiding some of the identifying assumptions required in earlier work. This relies on a new insight: using multiple data releases allows us to distinguish news and noise measurement errors in situations where a single vintage does not. Our new measure, GDP++, fits the data better than GDP+, the GDP growth measure of Aruoba et al. (2016) published by the Federal Reserve Bank of Philadephia. Historical decompositions show that GDE releases are more informative than GDI, while the use of multiple data releases is particularly important in the quarters leading up to the Great Recession

Can GDP Measurement Be Further Improved? Data Revision and Reconciliation

Recent years have seen many attempts to combine expenditure-side estimates of U.S. real output (GDE) growth with income-side estimates (GDI) to improve estimates of real GDP growth. We show how to incorporate information from multiple releases of noisy data to provide more precise estimates while avoiding some of the identifying assumptions required in earlier work. This relies on a new insight: using multiple data releases allows us to distinguish news and noise measurement errors in situations where a single vintage does not. We find that (a) the data prefer averaging across multiple releases instead of discarding early releases in favor of later ones, and (b) that initial estimates of GDI are quite informative. Our new measure, GDP(++), undergoes smaller revisions and tracks expenditure measures of GDP growth more closely than either the simple average of the expenditure and income measures published by the BEA or the GDP growth measure of Aruoba et al. published by the Federal Reserve Bank of Philadelphia