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on Econometric Time Series |
| By: | Badi H. Baltagi (Center for Policy Research, Maxwell School, Syracuse University, Syracuse, NY 13244-1020); Chihwa Kao (Center for Policy Research, Maxwell School, Syracuse University, Syracuse, NY 13244-1020); Sanggon Na |
| Abstract: | This paper considers the problem of hypotheses testing in a simple panel data regression model with random individual effects and serially correlated disturbances. Following Baltagi, Kao and Liu (2008), we allow for the possibility of non-stationarity in the regressor and/or the disturbance term. While Baltagi et al. (2008) focus on the asymptotic properties and distributions of the standard panel data estimators, this paper focuses on test of hypotheses in this setting. One important finding is that unlike the time series case, one does not necessarily need to rely on the “super-efficient” type AR estimator by Perron and Yabu (2009) to make inference in panel data. In fact, we show that the simple t-ratio always converges to the standard normal distribution regardless of whether the disturbances and/or the regressor are stationary. |
| Keywords: | Panel Data, OLS, Fixed-Effects, First-Difference, GLS, t-ratio. |
| JEL: | C12 C33 |
| Date: | 2011–02 |
| URL: | http://d.repec.org/n?u=RePEc:max:cprwps:128&r=ets |
| By: | Chihwa Kao (Center for Policy Research, Maxwell School, Syracuse University, Syracuse, NY 13244-1020); Lorenzo Trapani; Giovanni Urga |
| Abstract: | In this paper, we develop tests for structural change in cointegrated panel regressions with common and idiosyncratic trends. We consider both the cases of observable and nonobservable common trends, deriving a Functional Central Limit Theorem for the partial sample estimators under the null of no break. We show that tests based on sup-Wald statistics are powerful versus breaks of size , also proving that power is present when the time of change differs across units and when only some units have a break. Our framework is extended to the case of cross correlated regressors and endogeneity. Monte Carlo evidence shows that the tests have the correct size and good power properties. |
| Keywords: | Structural change, Panel cointegration, Common stochastic trends, Functional Central Limit Theorem. |
| JEL: | C23 |
| Date: | 2011–02 |
| URL: | http://d.repec.org/n?u=RePEc:max:cprwps:129&r=ets |
| By: | Ghassen El Montasser |
| Abstract: | Few authors have studied, either asymptotically or in finite samples, the size and power of seasonal unit root tests when the data generating process [DGP] is a non-stationary alternative aside from the seasonal random walk. In this respect, Ghysels, lee and Noh (1994) conducted a simulation study by considering the alternative of a non-seasonal random walk to analyze the size and power properties of some seasonal unit root tests. Analogously, Taylor (2005) completed this analysis by developing the limit theory of statistics of Dickey and Fuller Hasza [DHF] (1984) when the data are generated by a non-seasonal random walk. del Barrio Castro (2007) extended the set of non-stationary alternatives and established, for each one, the asymptotic theory of the statistics subsumed in the HEGY procedure. In this paper, I show that establishing the limit theory of F-type statistics for seasonal unit roots can be debatable in such alternatives. The problem lies in the nature of the regressors that these overall F-type tests specify. |
| Keywords: | Fisher test, seasonal integration, non-stationary alternatives, Brownian motion, Monte Carlo Simulation. |
| JEL: | C22 |
| Date: | 2011–04–06 |
| URL: | http://d.repec.org/n?u=RePEc:eei:rpaper:eeri_rp_2011_06&r=ets |
| By: | Carlos León Rincón; Alejandro Reveiz |
| Abstract: | As a natural extension to León and Vivas (2010) and León and Reveiz (2010) this paper briefly describes the Cholesky method for simulating Geometric Brownian Motion processes with long-term dependence, also referred as Fractional Geometric Brownian Motion (FBM). Results show that this method generates random numbers capable of replicating independent, persistent or antipersistent time-series depending on the value of the chosen Hurst exponent. Simulating FBM via the Cholesky method is (i) convenient since it grants the ability to replicate intense and enduring returns, which allows for reproducing well-documented financial returns’ slow convergence in distribution to a Gaussian law, and (ii) straightforward since it takes advantage of the Gaussian distribution ability to express a broad type of stochastic processes by changing how volatility behaves with respect to the time horizon. However, Cholesky method is computationally demanding, which may be its main drawback. Potential applications of FBM simulation include market, credit and liquidity risk models, option valuation techniques, portfolio optimization models and payments systems dynamics. All can benefit from the availability of a stochastic process that provides the ability to explicitly model how volatility behaves with respect to the time horizon in order to simulate severe and sustained price and quantity changes. These applications are more pertinent than ever because of the consensus regarding the limitations of customary models for valuation, risk and asset allocation after the most recent episode of global financial crisis. |
| Date: | 2011–04–03 |
| URL: | http://d.repec.org/n?u=RePEc:col:000094:008277&r=ets |