In this paper, we identify the technology shock at business cycle frequencies to improve the performance of structural vector autoregression models in small samples. To this end, we propose a new identification method based on the spectral decomposition of the variance, which targets the contributions of the shock in theoretical models. Results from a Monte-Carlo assessment show that the proposed method can deliver a precise estimate of the response of hours in small samples. We illustrate the application of our methodology using US data and a standard Real Business Cycle model. We find a positive response of hours in the short run following a non-significant, near-zero impact. This result is robust to a large set of credible parameterizations of the theoretical model.

I summarise new facts on hours worked differences across countries and their driving forces. The facts are derived from a comprehensive analysis of micro data sets. First, hours worked are substantially higher in poor than in rich countries. Second, lower hours worked in Europe than in the US can partly be explained by differences in vacation weeks and partly by differences in the demographic structure. Moreover, employment rates tend to be higher and weekly hours worked lower in Western Europe and Scandinavia than in the US, with the opposite being true in Eastern and Southern Europe. Last, among core-aged individuals, married women form the group that exhibits the largest differences in hours worked across countries. International differences in taxation, and especially in the tax treatment of married couples, are an important driver of these differences.

The performance of unit root tests on simulated series is compared, using the business-cycle model of Chang et al. [Journal of Money, Credit and Banking 39(6), 1357–1373 (2007)] as a data-generating process. Overall, Monte Carlo simulations show that the efficient unit root tests of Ng and Perron (NP) [Econometrica 69(6), 1519–1554 (2001)] are more powerful than the standard unit root tests. These efficient tests are frequently able (i) to reject the unit-root hypothesis on simulated series, using the best specification of the business-cycle model found by Chang et al., in which hours worked are stationary with adjustment costs, and (ii) to reduce the gap between the theoretical impulse response functions and those estimated with a Structural VAR model. The results of Monte Carlo simulations show that the hump-shaped behavior of data can explain the divergence between unit root tests.

Previous research has found that the dynamic response of hours worked to a technology shock crucially depends on whether the hours variable is assumed to be an I(0) or an I(1) variable ex ante. In this paper we employ a multivariate fractionally integrated model that allows us to simultaneously estimate the order of integration of hours worked and its dynamic response to a technology shock. Our evidence lends support to the hypothesis that hours fall in response to a positive technology shock.

This paper analyzes the robustness of the estimate of a positive productivity shock on hours to the presence of a possible unit root in hours. Estimations in levels or in first differences provide opposite conclusions. We rely on an agnostic procedure in which the researcher does not have to choose between a specification in levels or in first differences. We find that a positive productivity shock has a negative impact effect on hours, but the effect is much shorter lived, and disappears after two quarters. The effect becomes positive at business-cycle frequencies, although it is not significant.