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on Econometric Time Series |
| By: | Pedro V. Amaral (GeoDa Center for Geospatial Analysis and Computation; Arizona State University); Luc Anselin (GeoDa Center for Geospatial Analysis and Computation; Arizona State University); Daniel Arribas-Bel |
| Abstract: | In this note, we compare three test statistics that have been suggested to assess the presence of spatial error autocorrelation in probit models. We highlight the formal differences between the tests proposed by Pinkse and Slade (1998), Pinkse (1999, 2004) and Kelejian and Prucha (2001), and compare their properties in a extensive set of Monte Carlo simulation experiments both under the null and under the alternative. We also assess the conjecture by Pinkse (1999) that the usefulness of these test statistics is limited when the explanatory variables are spatially correlated. The Kelejian and Prucha (2001) generalized Moran’s I statistic turns out to perform best, even in medium sized samples of several hundreds of obser- vations. The other two tests are acceptable in very large samples. |
| Date: | 2012 |
| URL: | http://d.repec.org/n?u=RePEc:asg:wpaper:1051&r=ets |
| By: | P. Amaral; L. Anselin |
| Abstract: | In this paper, we investigate the finite sample properties of Moran’s I test statistic for spatial autocorrelation in limited dependent variable models suggested by Kelejian and Prucha (2001). We analyze the socio- economic determinants of the availability of dialysis equipment in 5,507 Brazilian municipalities in 2009 by means of a probit and tobit specifica- tion. We assess the extent to which evidence of spatial autocorrelation can be remedied by the inclusion of spatial fixed effects. We find spa- tial autocorrelation in both model specifications. For the probit model, a spatial fixed effects approach removes evidence of spatial autocorrelation. However, this is not the case for the tobit specification. We further fill a void in the theoretical literature by investigating the finite sample prop- erties of these test statistics in a series of Monte Carlo simulations, using data sets ranging from 49 to 15,625 observations. We find that the tests are unbiased and have considerable power for even medium-sized sample sizes. Under the null hypothesis of no spatial autocorrelation, their em- pirical distribution cannot be distinguished from the asymptotic normal distribution, empirically confirming the theoretical results of Kelejian and Prucha (2001), although the sample size required to achieve this result is larger in the tobit case than in the probit case. |
| Date: | 2011 |
| URL: | http://d.repec.org/n?u=RePEc:asg:wpaper:1048&r=ets |
| By: | Fabio Canova; Fernando J. Pérez Forero |
| Abstract: | This paper provides a method to estimate time varying coefficients structural VARs which are non-recursive and potentially overidentified. The procedure allows for linear and non-linear restrictions on the parameters, maintains the multi-move structure of standard algorithms and can be used to estimate structural models with different identification restrictions. We study the transmission of monetary policy shocks and compare the results with those obtained with traditional methods. |
| Keywords: | Non-recursive overidentified SVARs, Time-varying coefficient models, Bayesian methods, Monetary transmission mechanism |
| JEL: | C11 E51 E52 |
| Date: | 2012–05 |
| URL: | http://d.repec.org/n?u=RePEc:bge:wpaper:637&r=ets |
| By: | Danilo Delpini; Giacomo Bormetti |
| Abstract: | Agents' heterogeneity has been recognized as a driver mechanism for the persistence of financial volatility. We focus on the multiplicity of investment strategies' horizons; we embed this concept in a continuous time stochastic volatility framework and prove that a parsimonious, two-scales version effectively capture the long memory as measured from the real data. Since estimating parameters in a stochastic volatility model is a challenging task, we introduce a robust, knowledge-driven methodology based on the Generalized Methods of Moments. In addition to volatility clustering, the estimated model also captures other relevant stylized facts, emerging as a minimal but realistic and complete framework for modeling financial time series. |
| Date: | 2012–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1206.0026&r=ets |
| By: | Dariusz Grech; Zygmunt Mazur |
| Abstract: | We examine the scaling regime for the detrended fluctuation analysis (DFA) - the most popular method used to detect the presence of long memory in data and the fractal structure of time series. First, the scaling range for DFA is studied for uncorrelated data as a function of length $L$ of time series and regression line coefficient $R^2$ at various confidence levels. Next, an analysis of artificial short series with long memory is performed. In both cases the scaling range $\lambda$ is found to change linearly -- both with $L$ and $R^2$. We show how this dependence can be generalized to a simple unified model describing the relation $\lambda=\lambda(L, R^2, H)$ where $H$ ($1/2\leq H \leq 1$) stands for the Hurst exponent of long range autocorrelated data. Our findings should be useful in all applications of DFA technique, particularly for instantaneous (local) DFA where enormous number of short time series has to be examined at once, without possibility for preliminary check of the scaling range of each series separately. |
| Date: | 2012–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1206.1007&r=ets |
| By: | Gao, Jiti |
| Abstract: | In this paper, we consider some identification, estimation and specification problems in a class of semi-linear time series models. Existing studies for the stationary time series case have been reviewed and discussed. We also establish some new results for the integrated time series case. In the meantime, we propose a new estimation method and establish a new theory for a class of semi-linear nonstationary autoregressive models. In addition, we discuss certain directions for further research. |
| Keywords: | Asymptotic theory; departure function; kernel method; nonlinearity; nonstationarity; semiparametric model; stationarity; time series |
| JEL: | C14 C22 |
| Date: | 2012–04–08 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:39256&r=ets |