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Size Distortions (size + distortion)
Selected AbstractsDifferent changes of body-images in patients with anorexia or bulimia nervosa during inpatient psychosomatic treatmentEUROPEAN EATING DISORDERS REVIEW, Issue 2 2006Dieter Benninghoven Abstract Background Changes of perceptual body size distortion and body dissatisfaction during inpatient psychosomatic treatment were assessed. Differences between patients with anorexia and bulimia nervosa were compared. Methods Forty-one female patients with anorexia and 37 with bulimia nervosa were examined at beginning and end of an inpatient psychosomatic treatment. Body images were assessed by the somatomorph matrix and by the Eating Disorder Inventory (EDI-2). Results Both groups showed a distorted body size perception at the beginning of treatment. This decreased with the bulimia patients, with anorexia patients it largely remained in spite of a successful increase in weight. With bulimia patients body satisfaction improved, whereas it hardly changed with anorexia patients. Conclusion Bulimia patients were able to positively modify their body images. Treatment might have enabled patients with anorexia to maintain their level of body satisfaction and to tolerate a bigger perceived body image while they significantly gained weight. Copyright © 2006 John Wiley & Sons, Ltd and Eating Disorders Association. [source] The Time Series Properties of Financial Ratios: Lev RevisitedJOURNAL OF BUSINESS FINANCE & ACCOUNTING, Issue 5-6 2003Christos Ioannidis This paper re-evaluates the time series properties of financial ratios. It presents new empirical analysis which explicitly allows for the possibility that financial ratios can be characterized as non-linear mean-reverting processes. Financial ratios are widely employed as explanatory variables in accounting and finance research with applications ranging from the determinants of auditors' compensation to explaining firms' investment decisions. An implicit assumption in this empirical work is that the ratios are stationary so that the postulated models can be estimated by classical regression methods. However, recent empirical work on the time series properties of corporate financial ratios has reported that the level of the majority of ratios is described by non-stationary, I(1), integrated processes and that the ratio differences are parsimoniously described by random walks. We hypothesize that financial ratios may follow a random walk near their target level, but that the more distant a ratio is from target, the more likely the firm is to take remedial action to bring it back towards target. This behavior will result in a significant size distortion of the conventional stationarity tests and lead to frequent non-rejection of the null hypothesis of non-stationarity, a finding which undermines the use of these ratios as reliable conditioning variables for the explanation of firms' decisions. [source] Heterogeneity and cross section dependence in panel data models: theory and applications introductionJOURNAL OF APPLIED ECONOMETRICS, Issue 2 2007Badi H. Baltagi The papers included in this special issue are primarily concerned with the problem of cross section dependence and heterogeneity in the analysis of panel data models and their relevance in applied econometric research. Cross section dependence can arise due to spatial or spill over effects, or could be due to unobserved (or unobservable) common factors. Much of the recent research on non-stationary panel data have focussed on this problem. It was clear that the first generation panel unit root and cointegration tests developed in the 1990's, which assumed cross-sectional independence, are inadequate and could lead to significant size distortions in the presence of neglected cross-section dependence. Second generation panel unit root and cointegration tests that take account of possible cross-section dependence in the data have been developed, see the recent surveys by Choi (2006) and Breitung and Pesaran (2007). The papers by Baltagi, Bresson and Pirotte, Choi and Chue, Kapetanios, and Pesaran in this special issue are further contributions to this literature. The papers by Fachin, and Moon and Perron are empirical studies in this area. Controlling for heterogeneity has also been an important concern for empirical researchers with panel data methods promising better handle on heterogeneity than cross-section data methods. The papers by Hsiao, Shen, Wang and Weeks, Pedroni and Serlenga and Shin are empirical contributions to this area. Copyright © 2007 John Wiley & Sons, Ltd. [source] Evaluating Specification Tests for Markov-Switching Time-Series ModelsJOURNAL OF TIME SERIES ANALYSIS, Issue 4 2008Daniel R. Smith C12; C15; C22 Abstract., We evaluate the performance of several specification tests for Markov regime-switching time-series models. We consider the Lagrange multiplier (LM) and dynamic specification tests of Hamilton (1996) and Ljung,Box tests based on both the generalized residual and a standard-normal residual constructed using the Rosenblatt transformation. The size and power of the tests are studied using Monte Carlo experiments. We find that the LM tests have the best size and power properties. The Ljung,Box tests exhibit slight size distortions, though tests based on the Rosenblatt transformation perform better than the generalized residual-based tests. The tests exhibit impressive power to detect both autocorrelation and autoregressive conditional heteroscedasticity (ARCH). The tests are illustrated with a Markov-switching generalized ARCH (GARCH) model fitted to the US dollar,British pound exchange rate, with the finding that both autocorrelation and GARCH effects are needed to adequately fit the data. [source] New Improved Tests for Cointegration with Structural BreaksJOURNAL OF TIME SERIES ANALYSIS, Issue 2 2007Joakim Westerlund C12; C32; C33 Abstract., This article proposes Lagrange multiplier-based tests for the null hypothesis of no cointegration. The tests are general enough to allow for heteroskedastic and serially correlated errors, deterministic trends, and a structural break of unknown timing in both the intercept and slope. The limiting distributions of the test statistics are derived, and are found to be invariant not only with respect to the trend and structural break, but also with respect to the regressors. A small Monte Carlo study is also conducted to investigate the small-sample properties of the tests. The results reveal that the tests have small size distortions and good power relative to other tests. [source] Seasonal Unit Root Tests Under Structural Breaks,JOURNAL OF TIME SERIES ANALYSIS, Issue 1 2004Uwe Hassler C12; C22 Abstract., In this paper, several seasonal unit root tests are analysed in the context of structural breaks at known time and a new break corrected test is suggested. We show that the widely used HEGY test, as well as an LM variant thereof, are asymptotically robust to seasonal mean shifts of finite magnitude. In finite samples, however, experiments reveal that such tests suffer from severe size distortions and power reductions when breaks are present. Hence, a new break corrected LM test is proposed to overcome this problem. Importantly, the correction for seasonal mean shifts bears no consequence on the limiting distributions, thereby maintaining the legitimacy of canonical critical values. Moreover, although this test assumes a breakpoint a priori, it is robust in terms of misspecification of the time of the break. This asymptotic property is well reproduced in finite samples. Based on a Monte-Carlo study, our new test is compared with other procedures suggested in the literature and shown to hold superior finite sample properties. [source] Reducing size distortions of parametric stationarity testsJOURNAL OF TIME SERIES ANALYSIS, Issue 4 2003MARKKU LANNE The use of asymptotic critical values in stationarity tests against the alternative of a unit root process is known to lead to over-rejections in finite samples when the considered process is stationary but highly persistent. We claim that, in recent parametric tests, this is caused by estimation errors which result when the autoregressive parameters used to describe the short-run dynamics of the process are replaced by estimators. We suggest a modification that corrects for these errors. Simulation results show that the modified test works reasonably well when the persistence is moderate and there is no time trend in the model but it is less effective when the model contains a time trend. An empirical illustration with inflation rate data is provided. [source] IS THERE UNIT ROOT IN THE NITROGEN OXIDES EMISSIONS: A MONTE CARLO INVESTIGATION?NATURAL RESOURCE MODELING, Issue 1 2010NINA S. JONES Abstract Use of the time-series econometric techniques to investigate issues about environmental regulation requires knowing whether air pollution emissions are trend stationary or difference stationary. It has been shown that results regarding trend stationarity of the pollution data are sensitive to the methods used. I conduct a Monte Carlo experiment to study the size and power of two unit root tests that allow for a structural change in the trend at a known time using the data-generating process calibrated to the actual pollution series. I find that finite sample properties of the Perron test are better than the Park and Sung Phillips-Perron (PP) type test. Severe size distortions in the Park and Sung PP type test can explain the rejection of a unit root in air pollution emissions reported in some environmental regulation analyses. [source] Testing for Error Correction in Panel Data,OXFORD BULLETIN OF ECONOMICS & STATISTICS, Issue 6 2007Joakim Westerlund Abstract This paper proposes new error correction-based cointegration tests for panel data. The limiting distributions of the tests are derived and critical values provided. Our simulation results suggest that the tests have good small-sample properties with small size distortions and high power relative to other popular residual-based panel cointegration tests. In our empirical application, we present evidence suggesting that international healthcare expenditures and GDP are cointegrated once the possibility of an invalid common factor restriction has been accounted for. [source] Cointegration Testing in Panels with Common Factors,OXFORD BULLETIN OF ECONOMICS & STATISTICS, Issue 2006Christian Gengenbach Abstract 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, for example, been shown by Banerjee, Marcellino and Osbat [Econometrics Journal (2004), Vol. 7, pp. 322,340; Empirical Economics (2005), Vol. 30, pp. 77,91] via Monte Carlo simulations. Several studies have recently addressed this issue for panel unit-root tests 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 study by Bai and Ng [Econometrica (2004), Vol. 72, pp. 1127,1177] for panel unit roots. We 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 discuss the homogeneity restrictions on the cointegrating vectors resulting from the presence of common factor cointegration. Furthermore, we study the asymptotic behaviour of some existing residual-based panel no-cointegration tests, as suggested by Kao [Journal of Econometrics (1999), Vol. 90, pp. 1,44] and Pedroni [Econometric Theory (2004a), Vol. 20, pp. 597,625]. Under the data-generating processes (DGP) used, the test statistics are no longer asymptotically normal, and convergence occurs at rate T rather than as for independent panels. We then examine the possibilities of testing for various forms of no-cointegration by extracting the common factors and individual components from the observed data directly and then testing for no-cointegration using residual-based panel tests applied to the defactored data. [source] Distinguishing between trend-break models: method and empirical evidenceTHE ECONOMETRICS JOURNAL, Issue 2 2001Chih-Chiang Hsu We demonstrate that in time trend models, the likelihood-based tests of partial parameter stability have size distortions and cannot be applied to detect the changing parameter. A two-step procedure is then proposed to distinguish between different trend-break models. This procedure involves consistent estimation of break dates and properly-sized tests for changing coefficient. In the empirical study of the Nelson-Plosser data set, we find that the estimated change points and trend-break specifications resulting from the proposed procedure are quite different from those of Perron (1989, 1997), Chu and White (1992), and Zivot and Andrews (1992). In another application, our procedure provides formal support for the conclusion of Ben-David and Papell (1995) that real per capita GDPs of most OECD countries exhibit a slope change in trend. [source] | ||||||||||