What does a Monetary Policy Shock Do? An International Analysis with Multiple Filters*

What does a monetary policy shock do? We answer this question by estimating a new‐Keynesian monetary policy dynamic stochastic general equilibrium model for a number of economies with a variety of empirical proxies of the business cycle. The effects of two different policy shocks, an unexpected interest rate hike conditional on a constant inflation target and an unpredicted drift in the inflation target, are scrutinized. Filter‐specific Bayesian impulse responses are contrasted with those obtained by combining multiple business cycle indicators. Our results document the substantial uncertainty surrounding the estimated effects of these two policy shocks across a number of countries.     

Globalization vs. Europeanization: A Business Cycles Race*

This paper examines the relative cyclical affiliation in six industrialized economies. While the broad conclusions are consistent with the existing literature, the proposed empirical framework is not based on correlations and permits the analysis of relative groupwise synchronization under very few assumptions. The results allow us to conclude that an English‐speaking club (Canada, UK, USA) is emerging in the last decades, whereas explicit and formal commitments seem to have had a relatively weaker power in determining Euro‐zone (France, Germany, Italy) business cycle comovements. Altogether, the business cycle race is a cause for concern as regards the entry of the UK into the Euro‐zone.     

Measuring Synchronization and Convergence of Business Cycles for the Euro area,UK and US*

This paper investigates business cycle relations among different economies in the Euro area. Cyclical dynamics are explicitly modelled as part of a time series model. We introduce mechanisms that allow for increasing or diminishing phase shifts and for time‐varying association patterns in different cycles. Standard Kalman filter techniques are used to estimate the parameters simultaneously by maximum likelihood. The empirical illustrations are based on gross domestic product (GDP) series of seven European countries that are compared with the GDP series of the Euro area and that of the US. The original integrated time series are band‐pass filtered. We find that there is an increasing resemblance between the business cycle fluctuations of the European countries analysed and those of the Euro area, although with varying patterns.     

Business Cycles and the Role of Confidence: Evidence for Europe*

This paper examines whether indicators of consumer and business confidence can predict movements in GDP over the business cycle for four European economies. The empirical methodology used to investigate the properties of the data comprises cross‐correlation statistics, implementing an approach developed by den Haan [Journal of Monetary Economics (2000) , Vol. 46, pp. 3–30]. The predictive power of confidence indicators is also examined, investigating whether they can predict discrete events, namely economic downturns, and whether they can quantitatively forecast point estimates of economic activity. The results indicate that both consumer and business confidence indicators are procyclical and generally play a significant role in predicting downturns.     

The Relative Importance of Symmetric and Asymmetric Shocks: The Case of United Kingdom and Euro Area*

We show how a simple model with sign restrictions can be used to identify symmetric and asymmetric supply, demand and monetary policy shocks in an estimated two‐country structural VAR for the UK and Euro area. The results can be used to deal with several issues that are relevant in the optimal currency area literature. We find an important role for symmetric shocks in explaining the variability of the business cycle in both economies. However, the relative importance of asymmetric shocks, being around 20% in the long run, cannot be ignored. Moreover, when we estimate the model for the UK and US, the degree of business cycle synchronization seems to be higher. Finally, we confirm existing evidence of the exchange rate being an independent source of shocks in the economy.     

Merger Cycles: A Frequency Domain Approach*

Using frequency domain techniques, a cycle of 6‐year duration at the aggregate level and coherent sectoral cycles of average 5‐year duration are found in UK merger activity between 1969 and 2005. It is shown that business and capital market cycles jointly are causal for the merger cycle but the capital market cycle alone is not, suggesting that merger cycles may reflect disequilibria and/or market mis‐valuation. Although the possibility of disequilibrium or strong behavioural influences will complicate social evaluation, no reason is found to advise against the current UK policy stance upon mergers.     

Nonlinearities and Fractional Integration in the US Unemployment Rate*

This paper proposes a model of the US unemployment rate which accounts for both its asymmetry and its long memory. Our approach introduces fractional integration and nonlinearities simultaneously into the same framework, using a Lagrange multiplier procedure with a standard null‐limit distribution. The empirical results suggest that the US unemployment rate can be specified in terms of a fractionally integrated process, which interacts with some nonlinear functions of labour‐demand variables such as real oil prices and real interest rates. We also find evidence of a long‐memory component. Our results are consistent with a hysteresis model with path dependency rather than a non‐accelerating inflation rate of unemployment (NAIRU) model with an underlying unemployment equilibrium rate, thereby giving support to more activist stabilization policies. However, any suitable model should also include business cycle asymmetries, with implications for both forecasting and policy‐making.     

Economic Integration and Regional Business Cycles: Evidence from the Iberian Regions*

This paper provides evidence of the positive impact of economic integration on EU regions’ business cycles convergence by focusing on two neighbouring countries: Spain and Portugal. We show that while a rise in cross‐country business cycle correlation has also been experienced by other European countries, it has been relatively more pronounced for Iberian regions. Econometric evidence suggests that the existence of an administrative border, the economic size of regions and their industrial structures can explain a substantial proportion of regional cycles.     

Identification of Slowdowns and Accelerations for the Euro Area Economy*

Business cycle analyses have proved to be helpful to practitioners in assessing current economic conditions and anticipating upcoming fluctuations. In this article, we focus on the acceleration cycle in the euro area, namely the peaks and troughs of the growth rate which delimit the slowdown and acceleration phases of the economy. Our aim is twofold: first, we put forward a reference turning point chronology of this cycle on a monthly basis, based on gross domestic product and industrial production indices. We consider both euro area aggregate level and country‐specific cycles for the six main countries of the zone. Second, we come up with a new turning point indicator, based on business surveys carefully watched by central banks and short‐term analysts, to follow in real‐time the fluctuations of the acceleration cycle.     

Capital Services Growth in the UK: 1950 to 2006*

This paper describes a capital services dataset for the United Kingdom developed for use in empirical work, and some of its key features. The estimates are consistent with National Accounts output estimates, making them ideal for use in growth‐accounting or business‐cycle analysis. The divergence between the volume of capital services and the volume of the capital stock after 1980 is highlighted. This divergence is driven by a shift in investment towards short‐lived and more productive information and communication technology assets for which the flow of capital services is high. Standard capital stock measures understate growth in the productive input of capital, especially after 1990.     

Growth Trends and Systematic Patterns of Booms and Busts‐Testing 200 Years of Business Cycle Dynamics

We study the dynamic pattern of business cycles using US GDP data between 1790 and 2015. To address difficulties in trend and cycle decomposition, we introduce a semiparametric estimation approach with an iterative plug‐in (IPI) algorithm for endogenous bandwidth selection. This algorithm identifies continuously moving growth trends with trend‐supporting growth periods. A simulation study demonstrates the value‐added of our trend identification. Afterwards, nonlinear SETAR models are fitted parametrically. Further, we test the trend using a recently developed test and the estimated SETAR models against their linear alternatives. The results indicate asymmetric characteristics during booms and busts.     

News,disaster risk,and time-varying uncertainty

Standard real business cycle models are often unable to replicate three empirical facts: positive output in response to good news, stochastic volatility of macro variables, and asymmetric business cycles. This paper proposes a unified basis for understanding these facts in a tractable dynamic stochastic general equilibrium (DSGE) model, in which the key is the interaction of information flows and disaster risk. Information flows fluctuate between two regimes with different precision levels for signals regarding future economic fundamentals. A shift in forecast precision changes the probability of entering an economic disaster. High disaster risk leads to low expected capital returns and a decline in hours, investment, and output. Changing information structures results in different volatility and skewness over the business cycle. Simple theory makes the two expectation effects through information flows and disaster risk transparent. Quantitatively, the model suggests that the interaction of the two expectation effects plays a significant role in accounting for the higher-order moments of the business cycle.     

How Do Alphas and Betas Move? Uncertainty,Learning and Time Variation in Risk Loadings*

I employ a parsimonious model with learning, but without conditioning information, to extract time‐varying measures of market‐risk sensitivities, pricing errors and pricing uncertainty. The evolution of these quantities has interesting implications for macroeconomic dynamics. Parameters estimated for US equity portfolios display significant low‐frequency fluctuations, along patterns that change across size and book‐to‐market stocks. Time‐varying betas display superior predictive accuracy for returns against constant and rolling‐window OLS estimates. As to the relationship of betas with business‐cycle variables, value stocks’ betas move pro‐cyclically, unlike those of growth stocks. Investment growth, rather than consumption, predicts the betas of value and small‐firm portfolios.     

Estimating contract indexation in a Financial Accelerator Model

This paper addresses the positive implications of indexing risky debt to observable aggregate conditions. These issues are pursued within the context of the celebrated financial accelerator model of Bernanke et al. (1999). The principal conclusions include: (1) the estimated level of indexation is significant, (2) the business cycle properties of the model are significantly affected by this degree of indexation, (3) the importance of investment shocks in the business cycle depends upon the estimated level of indexation, and (4) although the data prefers the financial model with indexation over the frictionless model, they have remarkably similar business cycle properties for non-financial exogenous shocks.     

Calling recessions in real time

This paper surveys efforts to automate the dating of business cycle turning points. Doing this on a real time, out-of-sample basis is a bigger challenge than many academics might assume, due to factors such as data revisions and changes in economic relationships over time. The paper stresses the value of both simulated real-time analysis — looking at what the inference of a proposed model would have been using data as they were actually released at the time — and actual real-time analysis, in which a researcher stakes his or her reputation on publicly using the model to generate out-of-sample, real-time predictions. The immediate publication capabilities of the internet make the latter a realistic option for researchers today, and many are taking advantage of it. The paper reviews a number of approaches to dating business cycle turning points and emphasizes the fundamental trade-off between parsimony — trying to keep the model as simple and robust as possible — and making full use of the available information. Different approaches have different advantages, and the paper concludes that there may be gains from combining the best features of several different approaches.     

Factor MIDAS for Nowcasting and Forecasting with Ragged‐Edge Data: A Model Comparison for German GDP*

In this article, we merge two strands from the recent econometric literature. First, factor models based on large sets of macroeconomic variables for forecasting, which have generally proven useful for forecasting. However, there is some disagreement in the literature as to the appropriate method. Second, forecast methods based on mixed‐frequency data sampling (MIDAS). This regression technique can take into account unbalanced datasets that emerge from publication lags of high‐ and low‐frequency indicators, a problem practitioner have to cope with in real time. In this article, we introduce Factor MIDAS, an approach for nowcasting and forecasting low‐frequency variables like gross domestic product (GDP) exploiting information in a large set of higher‐frequency indicators. We consider three alternative MIDAS approaches (basic, smoothed and unrestricted) that provide harmonized projection methods that allow for a comparison of the alternative factor estimation methods with respect to nowcasting and forecasting. Common to all the factor estimation methods employed here is that they can handle unbalanced datasets, as typically faced in real‐time forecast applications owing to publication lags. In particular, we focus on variants of static and dynamic principal components as well as Kalman filter estimates in state‐space factor models. As an empirical illustration of the technique, we use a large monthly dataset of the German economy to nowcast and forecast quarterly GDP growth. We find that the factor estimation methods do not differ substantially, whereas the most parsimonious MIDAS projection performs best overall. Finally, quarterly models are in general outperformed by the Factor MIDAS models, which confirms the usefulness of the mixed‐frequency techniques that can exploit timely information from business cycle indicators.     

Regional output growth in the United Kingdom: More timely and higher frequency estimates from 1970

Output growth estimates for regions of the UK are currently published at an annual frequency only, released with a long delay, and offer limited historical coverage. To improve the regional database this paper develops a mixed-frequency multivariate model and uses it to produce consistent estimates of quarterly regional output growth dating back to 1970. We describe how these estimates are updated and evaluated on an ongoing, quarterly basis to publish online (at www.escoe.ac.uk/regionalnowcasting ) more timely regional growth estimates. We illustrate how the new quarterly data can contribute to our historical understanding of business cycle dynamics and connectedness between regions.     

VARs,common factors and the empirical validation of equilibrium business cycle models

Equilibrium business cycle models have typically less shocks than variables. As pointed out by Altug (1989) International Economic Review 30 (4) 889–920 and Sargent (1989) The Journal of Political Economy 97 (2) 251–287, if variables are measured with error, this characteristic implies that the model solution for measured variables has a factor structure. This paper compares estimation performance for the impulse response coefficients based on a VAR approximation to this class of models and an estimation method that explicitly takes into account the restrictions implied by the factor structure. Bias and mean-squared error for both factor- and VAR-based estimates of impulse response functions are quantified using, as data-generating process, a calibrated standard equilibrium business cycle model. We show that, at short horizons, VAR estimates of impulse response functions are less accurate than factor estimates while the two methods perform similarly at medium and long run horizons.     

Inflation Uncertainty, Output Growth Uncertainty and Macroeconomic Performance

We use a bivariate generalized autoregressive conditionally heteroskedastic (GARCH) model of inflation and output growth to examine the causality relationship among nominal uncertainty, real uncertainty and macroeconomic performance measured by the inflation and output growth rates. The application of the constant conditional correlation GARCH(1,1) model leads to a number of interesting conclusions. First, inflation does cause negative welfare effects, both directly and indirectly, i.e. via the inflation uncertainty channel. Secondly, in some countries, more inflation uncertainty provides an incentive to Central Banks to surprise the public by raising inflation unexpectedly. Thirdly, in contrast to the assumptions of some macroeconomic models, business cycle variability and the rate of economic growth are related. More variability in the business cycle leads to more output growth.     

Towards Unrestricted Public Use Business Microdata: The Synthetic Longitudinal Business Database

In most countries, national statistical agencies do not release establishment‐level business microdata, because doing so represents too large a risk to establishments’ confidentiality. One approach with the potential for overcoming these risks is to release synthetic data; that is, the released establishment data are simulated from statistical models designed to mimic the distributions of the underlying real microdata. In this article, we describe an application of this strategy to create a public use file for the Longitudinal Business Database, an annual economic census of establishments in the United States comprising more than 20 million records dating back to 1976. The U.S. Bureau of the Census and the Internal Revenue Service recently approved the release of these synthetic microdata for public use, making the synthetic Longitudinal Business Database the first‐ever business microdata set publicly released in the United States. We describe how we created the synthetic data, evaluated analytical validity, and assessed disclosure risk.