Predicting the long-term citation impact of recent publications

A fundamental problem in citation analysis is the prediction of the long-term citation impact of recent publications. We propose a model to predict a probability distribution for the future number of citations of a publication. Two predictors are used: The impact factor of the journal in which a publication has appeared and the number of citations a publication has received one year after its appearance. The proposed model is based on quantile regression. We employ the model to predict the future number of citations of a large set of publications in the field of physics. Our analysis shows that both predictors (i.e., impact factor and early citations) contribute to the accurate prediction of long-term citation impact. We also analytically study the behavior of the quantile regression coefficients for high quantiles of the distribution of citations. This is done by linking the quantile regression approach to a quantile estimation technique from extreme value theory. Our work provides insight into the influence of the impact factor and early citations on the long-term citation impact of a publication, and it takes a step toward a methodology that can be used to assess research institutions based on their most recently published work.Comment: 17 pages, 17 figure

Volatility forecasting

Volatility has been one of the most active and successful areas of research in time series econometrics and economic forecasting in recent decades. This chapter provides a selective survey of the most important theoretical developments and empirical insights to emerge from this burgeoning literature, with a distinct focus on forecasting applications. Volatility is inherently latent, and Section 1 begins with a brief intuitive account of various key volatility concepts. Section 2 then discusses a series of different economic situations in which volatility plays a crucial role, ranging from the use of volatility forecasts in portfolio allocation to density forecasting in risk management. Sections 3, 4 and 5 present a variety of alternative procedures for univariate volatility modeling and forecasting based on the GARCH, stochastic volatility and realized volatility paradigms, respectively. Section 6 extends the discussion to the multivariate problem of forecasting conditional covariances and correlations, and Section 7 discusses volatility forecast evaluation methods in both univariate and multivariate cases. Section 8 concludes briefly. JEL Klassifikation: C10, C53, G1

Macroeconomic Sources of Foreign Exchange Risk in New EU Members

We address the issue of foreign exchange risk and its macroeconomic determinants in several new EU members. The joint distribution of excess returns in the foreign exchange market and the observable macroeconomic factors is modeled using the stochastic discount factor (SDF) approach and a multivariate GARCH-in-mean model. We find that in post-transition economies real factors play a small role in determining foreign exchange risk, while nominal and monetary factors have a significant impact. Therefore, to contribute to the further stability of their domestic currencies, the central banks in the new EU member countries should continue stabilization policies aimed at achieving nominal convergence with the core EU members, as nominal factors play a crucial role in explaining the variability of the risk premium.http://deepblue.lib.umich.edu/bitstream/2027.42/64391/1/wp898.pd