A simple panel-CADF test for unit roots

Copyright © Blackwell Publishing Ltd and the Department of Economics, University of Oxford 2012. This is the accepted version of the following article: Costantini, M. and Lupi, C. (2013), A Simple Panel-CADF Test for Unit Roots. Oxford Bulletin of Economics and Statistics, 75: 276–296, which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/j.1468-0084.2012.00690.x/abstract.In this paper, we propose a simple extension to the panel case of the covariate-augmented Dickey–Fuller (CADF) test for unit roots developed in Hansen (1995). The panel test we propose is based on a P values combination approach that takes into account cross-section dependence. We show that the test has good size properties and gives power gains with respect to other popular panel approaches. An empirical application is carried out for illustration purposes on international data to test the purchasing power parity (PPP) hypothesis

Capital mobility and global factor shocks

© 2013 Elsevier B.V. All rights reservedThis paper focuses on the effects of global factors on the saving–investment relationship. We prove that, if investments and savings are affected by idiosyncratic and global components, they must be cointegrated to obtain reliable estimates of the saving-retention coefficient. When global shocks are taken into account throughcommonfactors,wefind that the estimated saving-retention coefficient is close to zero for a panel of 21 OECD countrie

Combining Forecasts Based on Multiple Encompassing Tests in a Macroeconomic Core System

We investigate whether and to what extent multiple encompassing tests may help determine weights for forecast averaging in a standard vector autoregressive setting. To this end we consider a new test-based procedure, which assigns non-zero weights to candidate models that add information not covered by other models. The potential benefits of this procedure are explored in extensive Monte Carlo simulations using realistic designs that are adapted to U.K. and to French macroeconomic data. The real economic growth rates of these two countries serve as the target series to be predicted. Generally, we find that the test-based averaging of forecasts yields a performance that is comparable to a simple uniform weighting of individual models. In one of our role-model economies, test-based averaging achieves some advantages in small samples. In larger samples, pure prediction models outperform forecast averages.Combining forecasts, encompassing tests, model selection, time series

Forecast Combination Based on Multiple Encompassing Tests in a Macroeconomic DSGE System

We use data generated by a macroeconomic DSGE model to study the relative benefits of forecast combinations based on forecast-encompassing tests relative to simple uniformly weighted forecast averages across rival models. Assumed rival models are four linear autoregressive specifications, one of them a more sophisticated factor-augmented vector autoregression (FAVAR). The forecaster is assumed not to know the true data-generating DSGE model. The results critically depend on the prediction horizon. While one-step prediction hardly supports test-based combinations, the test-based procedure attains a clear lead at prediction horizons greater than two.Combining forecasts, encompassing tests, model selection, time series, DSGE model

Asymptotic solutions of a generalized eigenvalue problem

This paper provides a solution of a generalized eigenvalue problem for a fractional integrated processes. To this end two random matrices are constructed in order to take into account the stationarity properties of the differences of a fractional p-variate integrated process. The matrices are defined by some weight functions and the difference orders are assumed to vary in a continuous and discrete range. The asymptotic behavior of these matrices is obtained imposing some conditions on the weight functions. Using Bierens (1987) and Andersen et al. (1983) results, a generalized eigenvalues problem is solved

Some Nonparametric Asymptotic Results for a Class of Stochastic Processes

This paper provides a solution of a generalized eigenvalue problem for integrated processes of order 2 in a nonparametric framework. Our analysis focuses on a pair of random matrices related to such integrated process. The matrices are constructed considering some weight functions. Under asymptotic conditions on such weights, convergence results in distribution are obtained and the generalized eigenvalue problem is solved. Differential equations and stochastic calculus theory are used. This is an Accepted Manuscript of an article published by Taylor & Francis in Communications in Statistics - Theory and Methodson 13 July 2010 , available online: http://www.tandfonline.com/10.1080/0361092090306102

On the asymptotic behaviour of random matrices in a multivariate statistical model

This paper aims to provide a nonparametric analysis of the integrated processes of an integer order, via a theoretical solution of a generalized eigenvalue problem. To this end, we introduce a mean operator for the process, by using weights belonging to a Sobolev Space

New results on the convergence of random matrices

This paper extends the previous convergence results in Cerqueti and Costantini (2008) to a more general case using larger normed set of functions. In this regard, the weight-based convergence of the random matrices and their generalized eigenvalues is obtained under less restrictive requirements for the weights. This is an Accepted Manuscript of an article published by Taylor & Francis in Statistics on 10 January 2012, available online: http://www.tandfonline.com/10.1080/02331888.2011.64863