Petre Caraiani

Using Complex Networks to Characterize International Business Cycles

Background There is a rapidly expanding literature on the application of complex networks in economics that focused mostly on stock markets. In this paper, we discuss an application of complex networks to study international business cycles. Methodology/Principal Findings We construct complex networks based on GDP data from two data sets on G7 and OECD economies. Besides the well-known correlation-based networks, we also use a specific tool for presenting causality in economics, the Granger causality. We consider different filtering methods to derive the stationary component of the GDP series for each of the countries in the samples. The networks were found to be sensitive to the detrending method. While the correlation networks provide information on comovement between the national economies, the Granger causality networks can better predict fluctuations in countries’ GDP. By using them, we can obtain directed networks allows us to determine the relative influence of different countries on the global economy network. The US appears as the key player for both the G7 and OECD samples. Conclusion The use of complex networks is valuable for understanding the business cycle comovements at an international level.

Evidence of Multifractality from Emerging European Stock Markets

We test for the presence of multifractality in the daily returns of the three most important stock market indices from Central and Eastern Europe, Czech PX, Hungarian BUX and Polish WIG using the Empirical Mode Decomposition based Multifractal Detrended Fluctuation Analysis. We found that the global Hurst coefficient varies with the q coefficient and that there is multifractality evidenced through the multifractal spectrum. The exercise is replicated for the sample around the high volatility period corresponding to the last global financial crisis. Although no direct link has been found between the crisis and the multifractal spectrum, the crisis was found to influence the overall shape as quantified through the norm of the multifractal spectrum.

The Role of Recurrence Plots in Characterizing the Output-Unemployment Relationship: An Analysis

We analyse the output-unemployment relationship using an approach based on cross-recurrence plots and quantitative recurrence analysis. We use post-war period quarterly U.S. data. The results obtained show the emergence of a complex and interesting relationship.

The uncertain unit root in GDP and CPI: a wavelet-based perspective

One of the controversial issues in macroeconomics consists in whether the macroeconomic time series are better characterized as being driven by permanent shocks or by temporary shocks. We provide here new evidence using a recent unit-root test based on the wavelets methodology.

Business Cycle Accounting for Peripheral European Economies

Using the Business Cycle Accounting methodology, this paper analyzes the dynamics of the EMU‐periphery economies before and during what was called the sovereign debt crisis. TFP dynamics and the labor wedge explain most of the dynamics before and during the crisis. The bond wedge, corresponding to the risk premium, made a low contribution, moving counter‐cyclically. The capital wedge made a modest contribution to the fall in output during the crisis. Additional evidence links the dynamics of the TFP and the labor wedge with changes in the interest rates and a spike in import prices at the onset of the crisis, corresponding to the general mechanisms of the large capital inflows, in the wake of the introduction of the Euro, followed by sudden stops.