|
on Econometric Time Series |
| By: | Siem Jan Koopman (Vrije Universiteit Amsterdam); André Lucas (Vrije Universiteit Amsterdam); Marius Ooms (Vrije Universiteit Amsterdam); Kees van Montfort (Vrije Universiteit Amsterdam); Victor van der Geest (Netherlands Institute for the Study of Crime and Law Enforcement (NCSR), Leiden) |
| Abstract: | We model panel data of crime careers of juveniles from a Dutch Judicial Juvenile Institution. The data are decomposed into a systematic and an individual-specific component, of which the systematic component reflects the general time-varying conditions including the criminological climate. Within a model-based analysis, we treat (1) shared effects of each group with the same systematic conditions, (2) strongly non-Gaussian features of the individual time series, (3) unobserved common systematic conditions, (4) changing recidivism probabilities in continuous time, (5) missing observations. We adopt a non-Gaussian multivariate state space model that deals with all of these issues simultaneously. The parameters of the model are estimated by Monte Carlo maximum likelihood methods. This paper illustrates the methods empirically. We compare continuous-time trends and standard discrete-time stochastic trend specifications. We find interesting common time-variation in the recidivism behavior of the juveniles during a period of 13 years, while taking account of significant heterogeneity determined by personality characteristics and initial crime records. |
| Keywords: | non-Gaussian state space modeling; nonlinear panel data model; binomial time series; recidivism behavior; continuous time modelling |
| JEL: | C15 C32 C33 D63 |
| Date: | 2007–03–08 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:uvatin:20070027&r=ets |
| By: | Michael J. Dueker; Zacharias Psaradakis; Martin Sola; Fabio Spagnolo |
| Abstract: | In this paper we propose a contemporaneous threshold multivariate smooth transition autoregressive (C-MSTAR) model in which the regime weights depend on the ex ante probabilities that latent regime-specific variables exceed certain threshold values. The model is a multivariate generalization of the contemporaneous threshold autoregressive model introduced by Dueker et al. (2007). A key feature of the model is that the transition function depends on all the parameters of the model as well as on the data. The stability and distributional properties of the proposed model are investigated. The C-MSTAR model is also used to examine the relationship between US stock prices and interest rates. |
| Date: | 2007 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedlwp:2007-19&r=ets |
| By: | Antonio E. Noriega (School of Economics, Universidad de Guanajuato); Daniel Ventosa-Santaularia (School of Economics, Universidad de Guanajuato) |
| Abstract: | This paper analyses the asymptotic and finite sample implications of different types of nonstationary behavior among the dependent and explanatory variables in a linear spurious regression model. We study cases when the nonstationarity in the dependent and explanatory variables is deterministic as well as stochastic. In particular, we derive the order in probability of the t-statistic in a linear regression equation under a variety of empirically relevant data generation processes, and show that he spurious regression phenomenon is present in all cases considered, when at least one of the variables behaves in a nonstationary way. Simulation experiments confirm our asymptotic results. |
| Keywords: | Trend Stationarity, Structural Breaks, Spurious Regression, Unit Roots, Trends. |
| JEL: | C12 C13 C22 |
| URL: | http://d.repec.org/n?u=RePEc:gua:wpaper:em200701&r=ets |
| By: | Schlicht, Ekkehart |
| Abstract: | Trend extraction from time series is often performed by using the filter proposed by Leser (1961), also known as the Hodrick-Prescott filter. A practical problem arises, however, when the time series contains structural breaks (such as produced by German unification for German time series, for instance). This note proposes a method for coping with this problem. |
| Keywords: | Trend extraction; structural break; Hodrick-Prescott filter; Leser filter; spline; time-series; smoothing; interpolation. |
| JEL: | C22 C32 C63 C14 |
| Date: | 2007–05 |
| URL: | http://d.repec.org/n?u=RePEc:lmu:muenec:1926&r=ets |
| By: | Schlicht, Ekkehart |
| Abstract: | Trend extraction from time series is often performed by using the filter proposed by Leser (1961), also known as the Hodrick-Prescott filter. A practical problem arises, however, when some data points are missing. This note proposes a method for coping with this problem. |
| Keywords: | Trend extraction; missing observations; gaps; Hodrick-Prescott filter; Leser filter; spline; time-series; smoothing; interpolation. |
| JEL: | C22 C32 C63 C14 |
| Date: | 2007–05 |
| URL: | http://d.repec.org/n?u=RePEc:lmu:muenec:1927&r=ets |
| By: | Wolfgang Härdle; Julius Mungo |
| Abstract: | The volatility implied by observed market prices as a function of the strike and time to maturity form an Implied Volatility Surface (IV S). Practical applications require reducing the dimension and characterize its dynamics through a small number of factors. Such dimension reduction is summarized by a Dynamic Semiparametric Factor Model (DSFM) that characterizes the IV S itself and their movements across time by a multivariate time series of factor loadings. This paper focuses on investigating long range dependence in the factor loadings series. Our result reveals that shocks to volatility persist for a very long time, affecting significantly stock prices. For appropriate representation of the series dynamics and the possibility of improved forecasting, we model the long memory in levels and absolute returns using the class of fractional integrated volatility models that provide flexible structure to capture the slow decaying autocorrelation function reasonably well. |
| Keywords: | Implied Volatility, Dynamic Semiparametric Factor Modeling, Long Memory, Fractional Integrated Volatility Models. |
| JEL: | C14 C32 C52 C53 G12 |
| Date: | 2007–05 |
| URL: | http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2007-027&r=ets |
| By: | Deniz Dilan Karaman Örsal |
| Abstract: | The main aim of this paper is to compare the size and size-adjusted power properties of four residual-based and one maximum-likelihood-based panel cointegration tests with the help of Monte Carlo simulations. In this study the panel-p, the group-p, the panel-t, the group-t statistics of Pedroni (1999) and the standardized LR-bar statistic of Larsson et al. (2001) are considered. The simulation results indicate that the panel-t and standardized LR-bar statistic have the best size and power properties a mong the five panel cointegration test statistics evaluated. Finally, the Fisher Hypothesis is tested with two different data sets for OECD countries. The results point out the existence of the Fisher relation. |
| Keywords: | Panel Cointegration tests, Monte Carlo Study, Fisher Hypothesis. |
| JEL: | C23 C33 C15 |
| Date: | 2007–05 |
| URL: | http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2007-029&r=ets |
| By: | Di Iorio, Francesca; Fachin, Stefano |
| Abstract: | Stability tests for cointegrating coefficients are known to have very low power with small to medium sample sizes. In this paper we propose to solve this problem by extending the tests to dependent cointegrated panels through the stationary bootstrap. Simulation evidence shows that the proposed panel tests improve considerably on asymptotic tests applied to individual series. As an empirical illustration we examined investment and saving for a panel of 14 European countries over the 1960-2002 period. While the individual stability tests, contrary to expectations and graphical evidence, in almost all cases do not reject the null of stability, the bootstrap panel tests lead to the more plausible conclusion that the long-run relationship between these two variables is likely to have undergone a break. |
| Keywords: | Panel cointegration; stationary bootstrap; parameter stability tests |
| JEL: | C23 |
| Date: | 2006–07 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:3280&r=ets |