Business cycle synchronization and the Euro: A wavelet analysis

We use wavelet analysis to study business cycle synchronization across the EU-15 and the Euro-12 countries. Based on the wavelet transform, we propose a metric to measure and test for business cycles synchronization. Several conclusions emerge. France and Germany form the core of the Euro land, being the most synchronized countries with the rest of Europe. Portugal, Greece, Ireland and Finland do not show statistically relevant degrees of synchronization with Europe. We also show that some countries (like Spain) have a French accent, while others have a German accent (e.g., Austria). Perhaps surprisingly, we find that the French business cycle has been leading the German business cycle as well as the rest of Europe. Among the countries that may, in the future, join the Euro, the Czech Republic seems the most promising candidate.     

Financial variables as leading indicators in Greece

This paper presents business cycle stylized facts for the Greek economy and extends the relevant Greek literature in the following directions. First, the index of industrial production (IOP) is used to represent real economic activity and business cycle conditions. Second, the behavior of certain financial variables throughout the various phases of the business cycle is analyzed in order to assess their leading indicator properties. Third, possible non-linearities in these variables are investigated and tested for their relation to the business cycle states. The results imply that the most reliable leading indicators are real Treasury bill rates. Volatilities of real short-term interest rates may also contain useful predictive information for IOP volatility. Finally, mean non-linearities seem to be associated with business cycle asymmetries in the mean. The author wishes to express his gratitude to conference participants at the 55th International Atlantic Economic Conference held in Vienna, Austria, March 12–16, 2003, and an anonymous referee for their valuable comments and suggestions. The author is responsible for any shortcomings in the paper.     

Leading indicators of non-performing loans in Greece: the information content of macro-, micro- and bank-specific variables

We examine the information content of a unique set of macroeconomic, bank-specific, market and credit registry variables as regards their ability to forecast non-performing loans using a panel data set of nine Greek banks. We distinguish between business, consumer and mortgage loans and investigate their differences with respect to their optimal predictors. The quasi-AIM approach (Carson et al. in Int J Forecast 27:923–941, 2010) is utilized in order to take into account heterogeneity across banks and minimize estimation uncertainty. In addition, we calculate a number of forecasting measures in order to take into account the policy makers’ preferences. We find that market variables, specifically the supermarket sales, confidence indices for the services and construction sector and the business sentiment index represent good forecasting variables for most categories of NPLs. In addition, industrial production is the optimal predictor for consumer NPLs and imports for business NPLs. Finally, bank-specific variables represent top-performing leading indicators for business NPLs. Our results have significant implications for stress-testing credit risk in a top-down manner and for supervisory and macro-prudential policy design.     

On business cycle fluctuations in USA macroeconomic time series

This study employs eighteen USA macroeconomic time series variables to investigate possible existence of asymmetries in business cycle fluctuations in the series. Detection of asymmetric fluctuations in economic activity is important for policymakers since effective monetary policy relies on asymmetric business cycle fluctuations in all the series. The asymmetric deviations from the long-term growth trend in each of the series are modeled using regime switching models and artificial neural networks. The results based on nonlinear switching time series models reveal strong evidence of business cycle asymmetries in most of the series. The results based on in-sample approximations from artificial neural networks show statistically significant evidence of asymmetries in all the series. Similar results are obtained when jackknife out-of-sample approximations from artificial neural networks are used. Thus, the study results show statistically significant evidence of asymmetries in all the series which indicates that business cycle fluctuations in the series are asymmetric, thus alike. Therefore, the impact of monetary policy shocks on the output and the other macroeconomic variables can be anticipated using nonlinear models only. The results on asymmetric business cycle fluctuations in real GDP are in line with recent studies but in sharp contrast with Balke and Fomby (1994).     

Hedge fund return higher moments over the business cycle

We investigate how macroeconomic and financial uncertainty impacts the behavior of hedge fund strategy higher moments—i.e., co-skewness and co-kurtosis—and their respective cross-sectional dispersions. Consistent with theoretical models, we find that strategy managers trade off these two higher moments when building optimal portfolios. Moreover, these trade-offs depend on the kind of strategy. Our experiments show that the VIX and its conditional variance are the most important factors affecting higher moment risk in the hedge fund industry. They also reveal that the behavior of hedge fund strategies is very asymmetric depending on the phase of the business cycle. In contrast to studies which rely on the mean-variance setting, we find that systemic risk—as measured by the cross-sectional dispersions of higher moments—tends to decrease in the low regime. The indicators of market volatility play a decisive role to explain this decline in systemic risk.     

The nonlinear multidimensional relationship between stock returns and the macroeconomy

We use nonparametric dimension-reduction methods to extract from a set of 15 macroeconomic variables the risk factors that are priced in the stock market. The dominant factor moves with the business cycle but, because it is a nonlinear function of observed macroeconomic variables, it captures a rich set of interactions. Low-credit risk and low-inflationary expectations have a greater positive effect on stock returns when leading macroeconomic indicators are high relative to current economic activity, i.e. early in the business cycle as the economy emerges from recession. High-stock returns also arise in periods when the economy is booming relative to its leading indicators, but such periods tend to portend crashes.     

Bayesian forecasting of federal funds target rate decisions

In this paper we examine which macroeconomic and financial variables have most predictive ability for the federal funds target rate decisions made by the Federal Open Market Committee (FOMC). We conduct the analysis for the 157 FOMC decisions during the period January 1990–June 2008, using dynamic ordered probit models with a Bayesian endogenous variable selection methodology and real-time data for a set of 33 candidate predictor variables. We find that indicators of economic activity and forward-looking term structure variables, as well as survey measures are most informative from a forecasting perspective. For the full sample period, in-sample probability forecasts achieve a hit rate of 90%. Based on out-of-sample forecasts for the period January 2001–June 2008, 82% of the FOMC decisions are predicted correctly.     

The equity premium in a small open economy and an application to Israel

We show that the preferences suggested by Greenwood, Hercowitz and Huffman (GHH), which are quite common in real business cycle (RBC) models of small open economies, are not suited for reproducing both the business cycle and the equity premium facts of a small open economy. We show that by assuming a moderate degree of a wealth effect on labor supply, together with some limitations on labor supply (in the form of real wage rigidity), we can increase the volatility of the stochastic discount factor (SDF), thereby increasing the equity premium and improving the fit of the business cycle moments. We also find that under the aforementioned assumptions, a shock to the realized return on foreign bonds can help in reproducing the equity premium.     

Forecasting UK commercial real estate cycle phases with leading indicators: a probit approach

This paper examines the significance of widely used leading indicators of the UK economy for predicting the cyclical pattern of commercial real estate performance. The analysis uses monthly capital value data for UK industrials, offices and retail from the Investment Property Databank (IPD). Prospective economic indicators are drawn from three sources namely, the series used by the US Conference Board to construct their UK leading indicator and the series deployed by two private organisations, Lombard Street Research and NTC Research, to predict UK economic activity. We first identify turning points in the capital value series adopting techniques employed in the classical business cycle literature. Probit models are then estimated using the leading economic indicators as independent variables and forecast the probability of different phases of capital values, that is, periods of declining and rising capital values. The forecast performance of the models is tested and found to be satisfactory. The predictability of lasting directional changes in property performance represents a useful tool for real estate investment decision-making.     

Determinants of business cycles in small scale macroeconomic models: the German case

We identify measures of shocks to total factor productivity and preferences from two real business cycle models and subject them to Granger causality tests to see whether they can be considered exogenous to other plausible sources of the German business cycle. For West German data from 1960.i to 1989.iv we conclude that our measures of shocks are indeed exogenous. This contrasts with similar studies for other countries that question the exogeneity of either productivity or preference shocks. For the period 70.i to 01.iv we find that M3 Granger causes all of our shock measures. We attribute this to the breaks in our time series associated with the German reunification in 1990 and the European Monetary Union in 1999.Earlier versions of this paper circulate as University of Augsburg economics discussion paper no. 213 and Kiel Institute for World Economics working paper no. 1158, respectively.     

Nowcasting the French index of industrial production: A comparison from bridge and factor models

Governments and central banks need to have an accurate and timely assessment of indicators for the current month, as this is essential for providing a reliable and early analysis of the current economic situation. The index of industrial production (IIP) is probably the most important and widely analyzed monthly indicator, given the relevance of the manufacturing activity as a driver of the whole business cycle. This paper presents a series of models conceived to forecast the current French monthly IIP, based on regression models and dynamic factor models. The combination of these two approaches allows selecting economically relevant explanatory variables among a large data set. In addition, a rolling forecast study is carried out to assess the forecasting performance of the estimated models, using predictive ability and model confidence set tests. This latter allows getting several models displaying equivalent forecasting performance and therefore gives robustness to the forecasting exercise rather than to base the forecasting analysis only on one model.     

The World Is Not Enough! Small Open Economies and Regional Dependence

In this paper, we explicitly introduce regional factors into a global dynamic factor model. We combine new open economy factor models (emphasizing global shocks) with the recent findings of regional importance in the business cycle synchronization literature. The analysis is applied to a large panel of domestic data for four small open economies. We find that global and regional shocks explain roughly 30 and 20 percent, respectively, of the business cycle variation in all countries. While global shocks have most impact on trade variables, regional shocks explain a relatively large share of the variation in cost variables.     

Managing TFT-LCDs under uncertainty: When crystal cycles meet business cycles

Understanding the correlation between the crystal cycle and the business cycle is important, because it can help managers to anticipate change, reduce environmental uncertainty, and formulate operational objectives. To this end, we focused on China and the U.S. in our analysis. We found that the economic indicators that were the most relevant in the characterization of China's huge and burgeoning TFT-LCD market are gross domestic production and industrial production. We complemented this finding by conducting similar analyses in the U.S. market using a more comprehensive list of economic indicators.     

Think national,forecast local: a case study of 71 German urban housing markets

In this article, we examine whether the local indicators are able to predict the city-level housing prices and rents better than national indicators. For this purpose, we assess the forecasting ability of 126 indicators and 21 types of forecast combinations using a sample of 71 large German cities. There are several predictors that are especially useful, namely price-to-rent ratios, national-level business confidence, and consumer surveys. We also find that combinations of individual forecasts are among the top forecasting models. On average, the forecast improvements attain about 20%, measured by a reduction in root mean square error, compared to the naive models.     

Predicting UK Business Cycle Regimes

This paper uses logistic regression to construct a one‐quarter ahead prediction model for classical business cycle regimes in the UK. The binary dependent variable is obtained by applying simple mechanical rules to date turning points in quarterly real GDP data from 1963 to 1999. Using a range of real and financial leading indicators, several parsimonious one‐quarter‐ahead models are developed for the GDP regimes, with model selection based on the SIC criterion. A real M4 variable is consistently found to have predictive content. One model that performs well combines this with nominal UK and German short‐term interest rates. The role of the latter emphasises the open nature of the UK economy.     

Measuring the cyclicality of labour market flows using individual transitions

We investigate whether the choice of a business cycle measure affects estimates of the cyclicality of labour market flows. We exploit precise administrative data on individual labour market transitions and study the association of alternative business cycle measures with individual transitions between employment and unemployment. We find indeed substantial heterogeneities across business cycle indicators that may have affected the results of prior studies on labour market transitions.     

Threshold cointegration between inflation and US capacity utilization

An analogue to the Phillips curve shows a positive relationship between inflation and capacity utilization. Some recent empirical work has shown that this relationship has broken down when using data after the mid-1980s. We empirically investigate this issue using several threshold error correction models. We find, in the long run, a 1% increase in the rate of inflation leads to approximately a 0.0046% increase in capacity utilization. The asymmetric error correction structure shows that changes in capacity utilization show significant corrective measures only during booms while changes in inflation correct during both phases of the business cycle with the corrections being stronger during recessions. We also find that, in the short run, changes in the inflation rate do Granger cause capacity utilization while changes in capacity utilization do not Granger cause inflation. The Granger causality from inflation to capacity utilization can be interpreted as supporting recent calls made in the popular press by some economists that it may be desirable for the Federal Reserve Bank to try to induce some inflation. However, it is also possible to interpret these Granger causality results as arising because both variables respond to some more fundamental set of variables with the inflation rate simply responding sooner. The lack of Granger causality from capacity utilization to inflation casts doubt on the older view that capacity utilization could be a leading indicator for future inflation.     

Pervasive inattentiveness

In this paper we analyze how inattentiveness in capital investment decisions shapes business cycle dynamics in a dynamic stochastic general equilibrium (DSGE) model with inattentiveness. We find that the model with pervasive inattentiveness matches several business cycle moments much better than an otherwise identical model without informational frictions in investment. These findings reinforce the need for pervasive stickiness to mimic the inertia found in macroeconomic data.     

Forecasting monthly industrial production in real-time: from single equations to factor-based models

The aim of this paper is to analyze the performance of alternative forecasting methods to predict the index of industrial production in Italy from 1 to 3 months ahead. We use twelve different models, from simple ARIMA to dynamic factor models exploiting the timely information of up to 110 short-term indicators, both qualitative and quantitative. This allows to assess the relevance for the forecasting practice of alternative combinations of types of data (real-time and latest available), estimation methods and periods. Out-of-sample predictive ability tests stress the relevance of more indicators in disaggregate models over sample periods covering a complete business cycle (about 7 years in Italy). Our findings downgrade the emphasis on both the estimation method and data revision issues. In line with the classical “average puzzle”, the use of simple averages of alternative forecasts often improves the predictive ability of their single components, mainly over short horizons. Finally, selected indicators and factor-based models always perform significantly better than ARIMA models, suggesting that the short-run indicator signal always dominates the noise component. On this regard, selected indicators models can further increase the amount of signal extracted to improve up to 30–40% the short-run predictive ability of factor-based models and to forecast-encompass them.     

Forecasting macro variables with a Qual VAR business cycle turning point index

One criticism of Vector Autoregression (VAR) forecasting is that macroeconomic variables tend not to behave as linear functions of their own past around business cycle turning points. A large amount of literature therefore focuses on nonlinear forecasting models, such as Markov switching models, which only indirectly capture the relation with turning points. This article investigates a direct approach to using information on turning points from the National Bureau of Economic Research (NBER) chronology to model and forecast macroeconomic data. Our Qual VAR model includes a truncated normal latent business cycle index that is negative during NBER recessions and positive during expansions. We motivate our forecasting exercise by demonstrating that if starting from a linear specification, a truncated normal variable is an omitted variable, then forecasts of the remaining variables will become nonlinear functions of their own past. We apply the Qual VAR model to recursive out-of-sample forecasting and find that the Qual VAR improves on out-of-sample forecasts from a standard VAR.