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on Econometrics |
| By: | Pedro Galeano |
| Abstract: | This paper studies the problem of multiple changepoints in rate parameter of a Poisson process. We propose a binary segmentation algorithm in conjunction with a cumulative sums statistic for detection of changepoints such that in each step we need only to test the presence of a simple changepoint. We derive the asymptotic distribution of the proposed statistic, prove its consistency and obtain the limiting distribution of the estimate of the changepoint. A Monte Carlo analysis shows the good performance of the proposed procedure, which is illustrated with a real data example. |
| Date: | 2004–12 |
| URL: | http://d.repec.org/n?u=RePEc:cte:wsrepe:ws046816&r=ecm |
| By: | Gary M. Koop; Simon M. Potter |
| Abstract: | This paper develops a new approach to change-point modeling that allows the number of change-points in the observed sample to be unknown. The model we develop assumes regime durations have a Poisson distribution. It approximately nests the two most common approaches: the time varying parameter model with a change-point every period and the change-point model with a small number of regimes. We focus considerable attention on the construction of reasonable hierarchical priors both for regime durations and for the parameters which characterize each regime. A Markov Chain Monte Carlo posterior sampler is constructed to estimate a change-point model for conditional means and variances. Our techniques are found to work well in an empirical exercise involving US GDP growth and inflation. Empirical results suggest that the number of change-points is larger than previously estimated in these series and the implied model is similar to a time varying parameter (with stochastic volatility) model. |
| Keywords: | Bayesian; structural break; Markov Chain Monte Carlo; hierarchical prior |
| JEL: | C11 C22 E17 |
| Date: | 2004–11 |
| URL: | http://d.repec.org/n?u=RePEc:lec:leecon:04/31&r=ecm |
| By: | Denis Larocque; Michel Normandin |
| Abstract: | This paper presents and assesses a procedure to estimate conventional parameters characterizing fluctuations at the business cycle frequency, when the economic agents' information set is superior to the econometrician's one. Specifically, we first generalize the conditions under which the econometrician can estimate these 'cyclical fluctuation' parameters from augmented laws of motion for forcing variables that fully recover the agents' superior information. Second, we document the econometric properties of the estimates when the augmented laws of motion are possibly misspecified. Third, we assess the ability of certain information criteria to detect the presence of superior information. |
| Keywords: | Block bootstrap, Hidden variables, Laws of motion for forcing variables, Monte Carlo simulations |
| JEL: | C14 C15 C32 E32 |
| Date: | 2004 |
| URL: | http://d.repec.org/n?u=RePEc:lvl:lacicr:0434&r=ecm |
| By: | Gultekin Isiklar (State University of New York at Albany) |
| Abstract: | We impose a structure on the short-run market inefficiencies in the asset markets and use this structure to identify a structural vector autoregressive model. This novel identification method is based on more reasonable assumptions than the standard approaches and also gives estimates for inefficiency measures in the markets, which are important on their own. Applying our method on the major European stock markets, we find that while the UK shocks were dominant in Europe until 1999, German innovations have been more important since 1999. We also find that the pattern of inefficiencies are consistent with the rational inattention model of Sims (2003). |
| Keywords: | Structural VAR; Overreaction and Underreaction; Stock Market |
| JEL: | C32 G15 D84 |
| Date: | 2005–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:wpa:wuwpem:0501001&r=ecm |
| By: | Ethan Cohen-Cole (University of Wisconsin - Madison) |
| Abstract: | The linear-in-means model has been a theoretical and empirical workhorse of the social interactions field. As was noted by Manski (1993), the collinearity between group-level 'contextual' and 'endogenous' effects leads to an inability to identify the structural parameters of this model. Manski called this the 'reflection' problem. This paper suggests that Manksi’s reflection problem is unique to a special case of a more general context in which agents care about multiple reference groups. Specifically, the identification problem is resolved through a model generalization to include between-group and within-group effects. |
| Keywords: | Social Interactions, Identification, Linear-in- Means Model |
| JEL: | C31 D10 |
| Date: | 2005–01–03 |
| URL: | http://d.repec.org/n?u=RePEc:wpa:wuwpot:0501001&r=ecm |
| By: | Xiaohong Chen (Department of Economics, New York University); Yanqin Fan (Department of Economics, Vanderbilt University) |
| Abstract: | In this paper, we address two important issues in survival model selection for censored data generated by the Archimedean copula family; method of estimating the parametric copulas and data reuse. We demonstrate that for model selection, estimators of the parametric copulas based on minimizing the selection criterion function may be preferred to other estimators. To handle the issue of data reuse, we put model selection in the context of hypothesis testing and propose a simple test for model selection from a finite number of parametric copulas. Results from a simulation study and two empirical applications provide strong support to our theoretical findings. |
| Keywords: | Archimedean copula, bivariate survival function, data reuse, miimum-distance estimation, model selection |
| JEL: | C14 C34 C52 |
| Date: | 2004–08 |
| URL: | http://d.repec.org/n?u=RePEc:van:wpaper:0421&r=ecm |
| By: | Reinhold Kosfeld (Author-Workplace-Name: Department of Economics, University of Kassel); Jorgen Lauridsen (Author-Workplace-Name: University of Southern Denmark, Department of Economics) |
| Abstract: | In presence of multicollinearity principal component regression (PCR) is sometimes suggested for the estimation of the regression coefficients of a multiple regression model. Due to ambiguities in the interpretation involved by the orthogonal transformation of the set of explanatory variables the method could not yet gain wide acceptance. Factor analysis regression (FAR) provides a model-based estimation method which is particular tailored to overcome multicollinearity in an errors in variables setting. In this paper we present a new FAR estimator that proves to be unbiased and consistent for the coefficient vector of a multiple regression model given the parameters of the measurement model. The behaviour of feasible FAR estimators in the general case of completely unknown model parameters is studied in comparison with the OLS estimator by means of Monte Carlo simulation. |
| Keywords: | Factor Analysis Regression, Multicollinearity, Factor model, Errors in Variables |
| JEL: | C13 C20 C51 |
| Date: | 2004–05 |
| URL: | http://d.repec.org/n?u=RePEc:kas:wpaper:57/04&r=ecm |