A panel cointegration study of the Euro effect on trade

Bun and Klaassen (2007) investigate the impact of the introduction of Euro on bilateral trade. Accounting for deterministic trends in the residuals of the gravity equation they estimate an Euro effect of about 3%, smaller than previous estimates in the range of 5% to 40%. In this paper we revisit their data using methods recently advanced in the analysis of non-stationary panel data with cross-sectional dependence. Using several panel unit root tests we find strong evidence that (the log of) bilateral trade, as well as the product of GDP and GDP per capita have unit roots. However, we find cointegration between these variables using the cointegration test of Gengenbach, Palm and Urbain (2006) and the error correction tests proposed by Gengenbach, Westerlund and Urbain (2008). Employing the common correlated effects (CCEP) estimator of Pesaran (2006) and the continuously updated (CUP) estimator of Bai, Kao and Ng (2009), we obtain estimates of the cointegrating vector and estimates of the Euro effect on bilateral trade. Our estimates vary between models and estimators but seem to support the findings of Bun and Klaassen (2007)

Stock Markets, Banks and Long Run Economic Growth: A Panel Cointegration-Based Analysis

The aim of this paper is to investigate the long run relationship between the development of banks and stock markets and economic growth. We make use of the Groen and Kleibergen (2003) panel cointegration methodology to test the number of cointegrating vectors among these three variables for 5 developing countries. In addition, we test the direction of potential causality between financial and economic development. Our results conclude to the existence of a single cointegrating vector between financial development and growth and of causality going from financial development to economic growth. We find little evidence of reverse causation as well as bi-directional causality.

Panel Error Correction Testing with Global Stochastic Trends

This paper considers a cointegrated panel data model with common factors. Starting from the triangular representation of the model as used by Bai et al. (2008) a Granger type representation theorem is derived. The conditional error correction representation is obtained, which is used as a basis for developing two new tests for the null hypothesis of noerror correction. The asymptotic distributions of the tests are shown to be free of nuisanceparameters, depending only on the number of non-stationary variables. However, the tests are not cross-sectionally independent, which makes pooling difficult. Nevertheless, the averages of the tests converge in distribution. This makes pooling possible in spite of the cross-sectional dependence. We investigate the nite sample performance of the proposed tests in a Monte Carlo experiment and compare them to the tests proposed by Westerlund (2007). We also present two empirical applications of the new tests.econometrics;

Panel Unit Root Tests in the Presence of Cross-Sectional Dependencies: Comparison and Implications for Modelling

Several panel unit root tests that account for cross section dependence using a common factor structure have been proposed in the literature recently, notably Pesaran (2003), Moon and Perron (2004) and Bai and Ng (2004). This paper is aimed at comparing these three proposed unit root tests for panels with dynamic factors. It makes fourcontributions: (1) it compares the three testing procedures in terms of similarities and difference in the data generation process, tests, null and alternative hypotheses considered,(2) it compares the small sample properties of the tests usingMonte Carlo results in models with up to two common factors, (3) it provides an application which illustrates the use of the tests, and (4) finally it discusses the use of the tests in modelling in general. The main conclusions are: Pesaran’s (2003) cross-sectionally augmented (CA)DF tests are designed for cases where cross-sectional dependence is due to a single factor. The Moon and Perron (2004) tests which use defactored data is similar in spirit but can account for mutiple common factors. The Bai and Ng (2004) tests allow to tests for unit roots in the common factors and/or the idiosyncratic factors. It would therefore be natural to use the Pesaran (2003) or Moon and Perron tests in a first step to find out whether there are unit roots in the data. Then in a second step of modelling, the Bai and Ng (2004) tests could be used to determine whether the unit roots arise in the common factors or in the idiosyncratic components. It is also found that the latter behave well when the observed nonstationarity in the data series comes exclusively from nonstationary common factors, e.g. when the series cointegrate along the cross sectional dimension of the panel.econometrics;

Are Panel Unit Root Tests Useful for Real-Time Data?

With the development of real-time databases, N vintages are available for T observations instead of a single realization of the time series process. Although the use of panel unit root tests with the aim to gain in efficiency seems obvious, empirical and simulation results shown in this paper heavily mitigate the intuitive perspective.macroeconomics ;

Comparing Nonlinear and Nonparametric Modeling Techniques for Mapping and Stratification in Forest Inventories of the Interior Western USA

Recent emphasis has been placed on merging regional forest inventory data with satellite-based information both to improve the efficiency of estimates of population totals, and to produce regional maps of forest variables. There are numerous ways in which forest class and structure variables may be modeled as functions of remotely sensed variables, yet surprisingly little work has been directed at surveying modem statistical techniques to determine which tools are best suited to the tasks given multiple objectives and logistical constraints. Here, a series of analyses to compare nonlinear and nonparametric modeling techniques for mapping a variety of forest variables, and for stratification of field plots, was conducted using data in the Interior Western United States. The analyses compared four statistical modeling techniques for predicting two discrete and four continuous forest inventory variables. The modeling techniques include generalized additive models (GAMs), classification and regression trees (CARTs), multivariate adaptive regression splines (MARS), and artificial neural networks (ANNs). Alternative stratification schemes were also compared for estimating population totals. The analyses were conducted within six ecologically different regions using a variety of satellite-based predictor variables. The work resulted in the development of an objective modeling box that automatically models spatial response variables as functions of any assortment of predictor variables through the four nonlinear or nonparametric modeling techniques. In comparing the different modeling techniques, all proved themselves workable in an automated environment, though ANNs were more problematic. When their potential mapping ability was explored through a simple simulation, tremendous advantages were seen in use of MARS and ANN for prediction over GAMs, CART, and a simple linear model. However, much smaller differences were seen when using real data. In some instances, a simple linear approach worked virtually as well as the more complex models, while small gains were seen using more complex models in other instances. In real data runs, MARS performed (marginally) best most often for binary variables, while GAMs performed (marginally) best most often for continuous variables. After considering a subjective ease of use measure, computing time and other predictive performance measures, it was determined that MARS had many advantages over other modeling techniques. In addition, stratification tests illustrated cost-effective means to improve precision of estimates of forest population totals. Finally, the general effect of map accuracy on the relative precision of estimates of population totals obtained under simple random sampling compared to that obtained under stratified random sampling was established and graphically illustrated as a tool for management decisions

Panel Cointegration Testing in the Presence of Common Factors

Panel unit root and no-cointegration tests that rely on cross-sectional independence of the panel unit experience severe size distortions when this assumption is violated, as has e.g. been shown by Banerjee, Marcellino and Osbat (2004, 2005) via Monte Carlo simulations. Several studies have recently addressed this issue for panel unit root test using a common factor structure to model the cross-sectional dependence, but not much work has been done yet for panel no-cointegration tests. This paper proposes a model for panel no-cointegration using an unobserved common factor structure, following the work on Bai and Ng (2004) for panel unit roots. The model enables us to distinguish two important cases: (i) the case when the non-stationarity in the data is driven by a reduced number of common stochastic trends, and (ii) the case where we have common and idiosyncratic stochastic trends present in the data. We study the asymptotic behavior of some existing, residual-based panel no-cointegration, as suggested by Kao (1999) and Pedroni (1999, 2004). Under the DGP used, the test statistics are no longer asymptotically normal, and convergence occurs at rate T rather than sqrt(N)T as for independent panels. We then examine the properties of residual-based tests for no-cointegration applied to defactored data from which the common factors and individual components have been extracted.econometrics;

Strategien zur Erzeugung von Qualitätsgetreide in viehlosen/-armen Marktfruchtbetrieben

In diesem Workshop sollen sowohl bereits bewährte als auch innovative Anbaustrategien auf ihre Eignung für nachhaltige Getreideerzeugung in ökologischen viehlosen Marktfruchtbetrieben untersucht werden