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on Econometrics |
| By: | Gluschenko, Konstantin |
| Abstract: | This paper deals with testing a time series with a structural break in its mean for a unit root when the break date is known. A nonlinear (with respect to coefficients) test equation is used, providing asymptotically efficient estimates. Finite-sample and quasi-asymptotic empirical distributions of the unit root test statistics are estimated, comparing them with those associated with the Perron-type equations. Asymptotic distributions of the nonlinear test statistics are found to be the Dickey-Fuller distributions. The nonlinear test proves to have more power than the test based on the linear model. |
| Keywords: | structural break; nonlinear regression; nonstandard distribution |
| JEL: | C22 C16 C15 C12 |
| Date: | 2004–08 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:678&r=ecm |
| By: | Wagner, Martin (Department of Economics and Finance, Institute for Advanced Studies, Vienna, Austria) |
| Abstract: | In recent years many empirical studies of environmental Kuznets curves employing unit root and cointegration techniques have been conducted for both time series and panel data. When using such methods several issues arise: the effects of a short time dimension, in a panel context the effects of cross-sectional dependence, and the presence of nonlinear transformations of integrated variables. We discuss and illustrate how ignoring these problems and applying standard methods leads to questionable results. Using an estimation approach that addresses the second and third problem we find no evidence for an inverse U-shaped relationship between GDP and CO2 emissions. |
| Keywords: | Carbon Kuznets Curve, Panel data, Unit roots, Cointegration, Cross-sectional dependence, Nonlinear transformations of regressors |
| JEL: | C12 C13 Q20 |
| Date: | 2006–11 |
| URL: | http://d.repec.org/n?u=RePEc:ihs:ihsesp:197&r=ecm |
| By: | Juarez, Miguel A.; Steel, Mark F. J. |
| Abstract: | A first order autoregressive non-Gaussian model for analysing panel data is proposed. The main feature is that the model is able to accommodate fat tails and also skewness, thus allowing for outliers and asymmetries. The modelling approach is to gain sufficient flexibility, without sacrificing interpretability and computational ease. The model incorporates individual effects and we pay specific attention to the elicitation of the prior. As the prior structure chosen is not proper, we derive conditions for the existence of the posterior. By considering a model with individual dynamic parameters we are also able to formally test whether the dynamic behaviour is common to all units in the panel. The methodology is illustrated with two applications involving earnings data and one on growth of countries. |
| Keywords: | autoregressive modelling; growth convergence; individual effects; labour earnings; prior elicitation; posterior existence; skewed distributions |
| JEL: | C11 C23 |
| Date: | 2006–07 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:450&r=ecm |
| By: | Liew, Venus Khim-Sen; Lau, Sie-Hoe; Ling, Siew-Eng |
| Abstract: | This study shows that augmented Dickey-Fuller (ADF) test failed to detect covariance nonstationary series. Supportive of Ahamada (2004), this study finds that the cumulative sums of squares procedure in Inclán and Tiao (1994) is useful to complement the ADF test. As illustration, the ADF test indicates that there is no unit root in the returns of Japanese yen/US dollar, British pound/ US dollar and Swiss franc/US. However, the complementary test reveals that each of these returns contains heterogeneous variance. To sum, it can be concluded that these exchange rate returns are covariance nonstationary although there is no unit root. |
| Keywords: | cumulative sums of squares; covariance nonstationary; exchange rate returns |
| JEL: | C22 F31 C12 |
| Date: | 2005 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:518&r=ecm |
| By: | Gundersen, Craig; Kreider, Brent |
| Abstract: | The Food Stamp Program, designed to alleviate food insecurity and hunger, takes a central role in the U.S. social safety net. Given this role, policymakers have been puzzled to observe that food stamp households appear more likely to be food insecure than observationally similar eligible nonparticipating households. We reexamine this issue given the potential for classification errors in program participation and food insecurity. Extending the emerging literature on corrupt samples, we introduce a nonparametric framework for assessing what can be inferred about conditional probabilities when a binary outcome and a binary conditioning variable are both subject to non-classical measurement error. We derive easy-to-compute sharp bounds under various assumptions about the nature and degree of classification errors. Surprisingly small degrees of misreporting are sufficient to overturn the prevailing conclusion that food stamp participation is associated with greater food insecurity. |
| Keywords: | Food Stamp Program, food insecurity, measurement error, nonparametric bounds, corrupt sampling |
| JEL: | I3 H5 I1 |
| Date: | 2006–10–30 |
| URL: | http://d.repec.org/n?u=RePEc:isu:genres:12690&r=ecm |
| By: | Mishra, SK |
| Abstract: | In this paper an attempt has been made to fit the Gielis curves (modified by various functions) to simulated data. The estimation has been done by two methods - the Classical Simulated Annealing (CSA) and the Particle Swarm (PS) methods - of global optimization. The Repulsive Particle Swarm (RPS) optimization algorithm has been used. It has been found that both methods are quite successful in fitting the modified Gielis curves to the data. However, the lack of uniqueness of Gielis parameters to data (from which they are estimated) is corroborated. From a technical viewpoint, this exercise may be considered as an application of CSA and RPS to extremely nonlinear least-squares curve-fitting to data that may exhibit a large number of local optima. |
| Keywords: | Gielis curves; superformula; nonlinear curve-fitting; Least squares; multi-modal; local optima; global optimization; simulated annealing; particle swarm; parameters estimation |
| JEL: | C13 C15 C63 C61 |
| Date: | 2006–07–02 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:465&r=ecm |