http://repub.eur.nl/res/concept/jel-C12/ Recent publications classified by JEL Code C12 en http://repub.eur.nl/static-eur/img/logo.png http://repub.eur.nl http://repub.eur.nl/res/pub/39848/ 2013-04-19T00:00:00Z This paper develops a testing framework for comparing the predictive accuracy of copula-based multivariate density forecasts, focusing on a specific part of the joint distribution. The test is framed in the context of the Kullback-Leibler Information Criterion, but using (out-of-sample) conditional likelihood and censored likelihood in order to focus the evaluation on the region of interest. Monte Carlo simulations document that the resulting test statistics have satisfactory size and power properties in small samples. In an empirical application to daily exchange rate returns we find evidence that the dependence structure varies with the sign and magnitude of returns, such that different parametric copula models achieve superior forecasting performance in different regions of the support. Our analysis highlights the importance of allowing for lower and upper tail dependence for accurate forecasting of common extreme appreciation and depreciation of different currencies. Diks, C.G.H. Panchenko, V. Sokolinskiy, O. Dijk, D.J.C. van http://repub.eur.nl/res/pub/26505/ 2011-09-01T00:00:00Z Patton and Timmermann (2011, 'Forecast Rationality Tests Based on Multi-Horizon Bounds', Journal of Business & Economic Statistics, forthcoming) propose a set of useful tests for forecast rationality or optimality under squared error loss, including an easily implemented test based on a regression that only involves (long-horizon and short-horizon) forecasts and no observations on the target variable. We propose an extension, a simulation-based procedure that takes into account the presence of errors in parameter estimates. This procedure can also be applied in the field of 'backtesting' models for Value-at-Risk. Applications to simple AR and ARCH time series models show that its power in detecting certain misspecifications is larger than the power of well-known tests for correct Unconditional Coverage and Conditional Coverage. Hoogerheide, L.F. Ravazzolo, F. Dijk, H.K. van http://repub.eur.nl/res/pub/25640/ 2011-08-01T00:00:00Z We consider the problem of testing for seasonal unit roots in monthly panel data. To this aim, we generalize the quarterly cross-sectionally augmented Hylleberg-Engle-Granger-Yoo (CHEGY) test to the monthly case. This parametric test is contrasted with a new non-parametric test, which is the panel counterpart to the univariate record unit-root seasonal (RURS) test that relies on counting extrema in time series. All methods are applied to an empirical data set on tourism in Austrian provinces. The power properties of the tests are evaluated in simulation experiments that are tuned to the tourism data. Kunst, R.M. Franses, Ph.H.B.F. http://repub.eur.nl/res/pub/21723/ 2010-12-07T00:00:00Z This paper develops a new test, the trinomial test, for pairwise ordinal data samples to improve the power of the sign test by modifying its treatment of zero di®erences between observations, thereby increasing the use of sample information. Simulations demonstrate the power superiority of the proposed trinomial test statis- tic over the sign test in small samples in the presence of tie observations. We also show that the proposed trinomial test has substantially higher power than the sign test in large samples and also in the presence of tie observations, as the sign test ignores information from observations resulting in ties. Bian, G. McAleer, M.J. Wong, W-K. http://repub.eur.nl/res/pub/21727/ 2010-12-07T00:00:00Z This paper develops a new test, the trinomial test, for pairwise ordinal data samples to improve the power of the sign test by modifying its treatment of zero differences between observations, thereby increasing the use of sample information. Simulations demonstrate the power superiority of the proposed trinomial test statistic over the sign test in small samples in the presence of tie observations. We also show that the proposed trinomial test has substantially higher power than the sign test in large samples and also in the presence of tie observations, as the sign test ignores information from observations resulting in ties. Bian, G. McAleer, M.J. Wong, W-K. http://repub.eur.nl/res/pub/16317/ 2009-08-01T00:00:00Z In applied economic research computable general equilibrium (CGE) models in which the behavior of economic agents are modeled, are widely used. In many CGE models, the linear expenditure system (LES) is used to model the behavior of the household sector. The disadvantage of LES is that the Engel curves, describing the relationship between expenditure on a certain commodity and total expenditure, are straight lines. Moreover, the LES does not allow for the existence of inferior commodities, elastic demand and gross substitution. An alternative model for the household block is the indirect addilog system (IAS), which is as simple to implement as LES, but which does not suffer from these theoretical deficiencies. In this paper, we test the LES specification against the IAS specification in case one disposes of a budget survey. Consequently, IAS provides a theoretically richer description of household behavior than LES, while it is also easy to implement. It is not possible to use a standard likelihood ratio test as both models are not nested. We propose to use the likelihood ratio test for non-nested hypotheses due to Vuong [(1989), Likelihood ratio tests for model selection and non-nested hypotheses, Econometrica 57, 307–333.] or, alternatively, the distribution-free test due to Clarke [(2007), A simple distribution-free test for nonnested model selection, Political Analysis 15, 347–363.]. We apply both tests to the Palestinian Expenditure and Consumption Survey [PECS (2005), Palestinian Central Bureau of Statistics, Ramallah, Palestine.] and find that there is overwhelming evidence that the IAS specification is to be preferred to the LES specification. Boer, P.M.C. de http://repub.eur.nl/res/pub/15564/ 2009-04-21T00:00:00Z In applied economic research computable general equilibrium [CGE] models in which the behavior of economic agents are modeled, are widely used. In many CGE models, the Linear Expenditure System [LES] is used to model behavior of the household sector. The disadvantage of LES is that the Engel curves, describing the relationship between expenditure on a certain commodity and total expenditure, are straight lines. Moreover, the LES does not allow for the existence of inferior commodities, elastic demand and gross substitution. An alternative model for the household block is the Indirect Addilog System [IAS] which is as simple to implement as LES, but which does not suffer from these theoretical deficiencies. Consequently, IAS provides a theoretically richer description of household behavior than LES, while it is as easy to implement. In this paper we test the LES specification against the IAS specification in case one disposes of a budget survey. It is not possible to use a standard likelihood ratio test as both models are not nested. We propose to use the likelihood ratio test for non-nested hypotheses due to Vuong (1989), or, alternatively, the distribution-free test due to Clarke (2007). We apply both tests to the Palestinian Expenditure and Consumption Survey (PECS, 2005) and find that there is overwhelming evidence that the IAS specification is to be preferred to the LES specification. Boer, P.M.C. de Paap, R. http://repub.eur.nl/res/pub/14861/ 2009-02-19T00:00:00Z We consider the problem of testing for seasonal unit roots in monthly panel data. To this aim, we generalize the quarterly CHEGY test to the monthly case. This parametric test is contrasted with a new nonparametric test, which is the panel counterpart to the univariate RURS test that relies on counting extrema in time series. All methods are applied to an empirical data set on tourism in Austrian provinces. The power properties of the tests are evaluated in simulation experiments that are tuned to the tourism data. Kunst, R.M. Franses, Ph.H.B.F. http://repub.eur.nl/res/pub/14052/ 2008-11-01T00:00:00Z We introduce a statistical test for comparing the predictive accuracy of competing copula specifications in multivariate density forecasts, based on the Kullback-Leibler Information Criterion (KLIC). The test is valid under general conditions: in particular it allows for parameter estimation uncertainty and for the copulas to be nested or non-nested. Monte Carlo simulations demonstrate that the proposed test has satisfactory size and power properties in finite samples. Applying the test to daily exchange rate returns of several major currencies against the US dollar we find that the Student's t copula is favored over Gaussian, Gumbel and Clayton copulas. This suggests that these exchange rate returns are characterized by symmetric tail dependence. Diks, C.G.H. Panchenko, V. Dijk, D.J.C. van http://repub.eur.nl/res/pub/14051/ 2008-10-20T00:00:00Z We propose a quantification of the p-p plot that assigns equal weight to all distances between the respective distributions: the surface between the p-p plot and the diagonal. This surface is labelled the Harmonic Weighted Mass (HWM) index. We introduce the diagonal-deviation (d-d) plot that allows the index to be computed exactly under all circumstances. For two balanced samples absent ties the finite sample distribution of the HWM index is derived. Simulations show that in most cases unbalanced samples and ties have little effect on this distribution. The d-d plot allows for a straightforward extension to the K-sample HWM index. As we have not been able to derive the distribution of the index for K>2, we simulate significance tables for K=3,...,15. An example involving economic growth rates of the G7 countries illustrates that the HWM test can have better power than alternative Empirical Distribution Function tests. Hinloopen, J. Wagenvoort, R. Marrewijk, J.G.M. van http://repub.eur.nl/res/pub/13975/ 2008-05-19T00:00:00Z We propose new scoring rules based on partial likelihood for assessing the relative out-of-sample predictive accuracy of competing density forecasts over a specific region of interest, such as the left tail in financial risk management. By construction, existing scoring rules based on weighted likelihood or censored normal likelihood favor density forecasts with more probability mass in the given region, rendering predictive accuracy tests biased towards such densities. Our novel partial likelihood-based scoring rules do not suffer from this problem, as illustrated by means of Monte Carlo simulations and an empirical application to daily S&P 500 index returns. Diks, C.G.H. Panchenko, V. Dijk, D.J.C. van http://repub.eur.nl/res/pub/12677/ 2007-08-01T00:00:00Z Copulas offer financial risk managers a powerful tool to model the dependence between the different elements of a portfolio and are preferable to the traditional, correlation-based approach. In this paper, we show the importance of selecting an accurate copula for risk management. We extend standard goodness-of-fit tests to copulas. Contrary to existing, indirect tests, these tests can be applied to any copula of any dimension and are based on a direct comparison of a given copula with observed data. For a portfolio consisting of stocks, bonds and real estate, these tests provide clear evidence in favor of the Student’s t copula, and reject both the correlation-based Gaussian copula and the extreme value-based Gumbel copula. In comparison with the Student’s t copula, we find that the Gaussian copula underestimates the probability of joint extreme downward movements, while the Gumbel copula overestimates this risk. Similarly we establish that the Gaussian copula is too optimistic on diversification benefits, while the Gumbel copula is too pessimistic. Moreover, these differences are significant. Kole, H.J.W.G. Koedijk, C.G. Verbeek, M.J.C.M. http://repub.eur.nl/res/pub/14062/ 2007-01-01T00:00:00Z We develop a non-parametric test of productive efficiency that accounts for errors-in-variables, following the approach of Varian. [1985. Nonparametric analysis of optimizing behavior with measurement error. Journal of Econometrics 30(1/2), 445–458]. The test is based on the general Pareto–Koopmans notion of efficiency, and does not require price data. Statistical inference is based on the sampling distribution of the L∞ norm of errors. The test statistic can be computed using a simple enumeration algorithm. The finite sample properties of the test are analyzed by means of a Monte Carlo simulation using real-world data of large EU commercial banks. Kuosmanen, T. Post, G.T. Scholtes, S. http://repub.eur.nl/res/pub/8001/ 2006-09-29T00:00:00Z We propose a discussion index model (Stock and Watson, 2002) to fore- cast electricity demand for one hour to one week ahead. The model is particularly useful as it captures complicated seasonal patterns in the data. The forecast performance of the proposed method is illustrated with a simulated real-time experiment for data from the Pennsylvania- New Jersey-Maryland Interchange. Rotger, G.P. Franses, Ph.H.B.F. http://repub.eur.nl/res/pub/12668/ 2006-09-11T00:00:00Z Copulas offer financial risk managers a powerful tool to model the dependence between the different elements of a portfolio and are preferable to the traditional, correlation-based approach. In this paper we show the importance of selecting an accurate copula for risk management. We extend standard goodness-of-fit tests to copulas. Contrary to existing, indirect tests, these tests can be applied to any copula of any dimension and are based on a direct comparison of a given copula with observed data. For a portfolio consisting of stocks, bonds and real estate, these tests provide clear evidence in favor of the \\studt copula, and reject both the correlation-based Gaussian copula and the extreme value-based Gumbel copula. In comparison with the \\studt copula, we find that the Gaussian copula underestimates the probability of joint extreme downward movements, while the Gumbel copula overestimates this risk. Similarly we establish that the Gaussian copula is too optimistic on diversification benefits, while the Gumbel copula is too pessimistic. Moreover, these differences are significant. Kole, H.J.W.G. Koedijk, C.G. Verbeek, M.J.C.M. http://repub.eur.nl/res/pub/13216/ 2006-07-01T00:00:00Z We propose a novel statistic to test the rank of a matrix. The rank statistic overcomes deficiencies of existing rank statistics, like: a Kronecker covariance matrix for the canonical correlation rank statistic of Anderson [Annals of Mathematical Statistics (1951), 22, 327–351] sensitivity to the ordering of the variables for the LDU rank statistic of Cragg and Donald [Journal of the American Statistical Association (1996), 91, 1301–1309] and Gill and Lewbel [Journal of the American Statistical Association (1992), 87, 766–776] a limiting distribution that is not a standard chi-squared distribution for the rank statistic of Robin and Smith [Econometric Theory (2000), 16, 151–175] usage of numerical optimization for the objective function statistic of Cragg and Donald [Journal of Econometrics (1997), 76, 223–250] and ignoring the non-negativity restriction on the singular values in Ratsimalahelo [2002, Rank test based on matrix perturbation theory. Unpublished working paper, U.F.R. Science Economique, University de Franche-Comté]. In the non-stationary cointegration case, the limiting distribution of the new rank statistic is identical to that of the Johansen trace statistic. Kleibergen, F.R. Paap, R. http://repub.eur.nl/res/pub/12993/ 2005-12-30T00:00:00Z The information contained in PP-plots is transformed into a single number. The resulting Harmonic Mass (HM) index is distribution free and its sample counterpart is shown to be consistent. For a wide class of CDFs the exact analytical expression of the distribution of the sample HM index is derived, assuming the two underlying samples to be drawn from the same distribution. The robustness of the concomitant test statistic is assessed, and four different methods are discussed for applying the HM test in case of asymmetric samples. Hinloopen, J. Marrewijk, J.G.M. van http://repub.eur.nl/res/pub/11131/ 2005-11-01T00:00:00Z Causality-in-variance tests suffer from severe size distortions in the presence of structural breaks in volatility, when such breaks are not taken into account. Pre-testing the series for structural changes in volatility largely remedies the problem. Dijk, D.J.C. van Osborn, D.R. Sensier, M. http://repub.eur.nl/res/pub/13342/ 2005-05-01T00:00:00Z We propose tests for hypotheses on the parameters for deterministic trends. The model framework assumes a multivariate structure for trend-stationary time series variables. We derive the asymptotic theory and provide some relevant critical values. Monte Carlo simulations suggest which tests are more useful in practice than others. We apply our tests to examine real GDP convergence for a sample of seven European countries. Vogelsang, T.J. Franses, Ph.H.B.F. http://repub.eur.nl/res/pub/1801/ 2004-11-05T00:00:00Z We examine the size properties of tests for causality in variance in the presence of structural breaks in volatility. Extensive Monte Carlo simulations demonstrate that these tests suffer from severe size distortions when such breaks are not taken into account. Pre-testing the series for structural changes in volatility is shown to largely remedy the problem. Dijk, D.J.C. van Osborn, D.R. Sensier, M.