DE - ArtÃculos de Revistas
http://hdl.handle.net/10016/713
2015-03-04T15:17:19ZTests for m-dependence based on Sample Splitting Methods
http://hdl.handle.net/10016/20111
Tests for m-dependence based on Sample Splitting Methods
Moon, Seongman; Velasco, Carlos
This paper develops new test methods for m-dependent data. Our approach is based on sample splitting by regular sampling of the original data at lower frequencies, so that standard techniques for testing independence can be used for each individual subsample. We then propose several alternative statistics that aggregate information across subsamples and investigate their asymptotic and nite sample properties. We apply our methods to test the predictability of excess returns in foreign exchange markets. We also illustrate how our serial dependence tests can provide useful information for identifying particular economic alternatives when testing the expectations hypothesis in foreign exchange markets.
2013-04-01T00:00:00ZA joint portmanteau test for conditional mean and variance time-series models
http://hdl.handle.net/10016/20096
A joint portmanteau test for conditional mean and variance time-series models
Velasco, Carlos; Xuexin, Wang
In this article, we propose a new joint portmanteau test for checking the specification of parametric conditional mean and variance functions of linear and nonlinear time-series models. The use of a joint test is motivated for complete control of the asymptotic size since marginal tests for the conditional variance may lead to misleading conclusions when the conditional mean is misspecified. The new test is based on an asymptotically distribution-free transformation on the sample autocorrelations of both normalized residuals and squared normalized residuals. This makes it unnecessary to full detail the asymptotic properties of the estimates used to obtain residuals, which could be inefficient two-step ones, avoiding also choices of maximum lag parameters increasing with sample length to control asymptotic size. The robust versions of the new test also properly account for higher-order moment dependence at a reduced cost. The finitesample performance of the new test is compared with that of well-known tests through simulations.
2015-01-01T00:00:00ZFractional cointegration rank estimation
http://hdl.handle.net/10016/20094
Fractional cointegration rank estimation
Lasak, Katarzyna Aleksandra; Velasco, Carlos
We consider cointegration rank estimation for a p-dimensional Fractional Vector Error Correction Model. We propose a new two-step procedure which allows testing for furtherlong-run equilibrium relations with possibly different persistence levels. The first step consists in estimating the parameters of the model under the none hypothesis of the cointegration rank r = 1; 2,... p - 1: This step provides consistent estimates of the order of fractional cointegration, the cointegration vectors, the speed of adjustment to the equilibrium parameters and the common trends. In the second step we carry out a sup-likelihood ratio test of no-cointegration on the estimated p - r common trends that are not cointegrated under the none. The order of fractional cointegration is re-estimatedin the second step to allow for new cointegration relationships with different memory. We augment the error correction model in the second step to adapt to the representation of the common trends estimated in the first step. The critical values of the proposed tests depend only on the number of common trends under the none, p-r; and on the interval of the orders of fractional cointegration b allowed in the estimation, but not on the order of fractional cointegration of already identied relationships. Hence this reduces the set of simulations required to approximate the critical values, making this procedure convenient for practical purposes. In a Monte Carlo study we analyze the finite sample properties of our procedure and compare with alternative methods. We finally apply these methods to study the term structure of interest rates.
2014-05-28T00:00:00ZEfficient inference on fractionally integrated panel data models with fixed effects
http://hdl.handle.net/10016/20093
Efficient inference on fractionally integrated panel data models with fixed effects
Robinson, Peter Michael; Velasco, Carlos
A dynamic panel data model is considered that contains possibly stochastic individual components and a common stochastic time trend that allows for stationary and nonstationary long memory and general parametric short memory. We propose four different ways of coping withthe individual effects so as to estimate the parameters. Like models with autoregressive dynamics, ours nests I(1) behaviour, but unlike the nonstandard asymptotics in the autoregressive case, estimates of the fractional parameter can be asymptotically normal. For three of the estimates, establishing this property is made difficult due to bias caused by the individual effects, or by the consequences of eliminating them, which appears in the central limit theorem except under stringent conditions on the growth of the cross-sectional size N relative to the time series length T; though in case of two estimates these can be relaxed by bias correction, where the biases depend only on the parameters describing autocorrelation. For the fourth estimate, there is no bias problem, and no restrictions on N: Implications for hypothesis testing and interval estimation are discussed, with central limit theorems for feasibly bias-corrected estimates included. A Monte Carlo study of nite-sample performance is included.
2015-04-01T00:00:00Z