257,074 research outputs found
Optimal Conditionally Unbiased Bounded-Influence Inference in Dynamic Location and Scale Models
This paper studies the local robustness of estimators and tests for the conditional location and scale parameters in a strictly stationary time series model. We first derive optimal bounded-influence estimators for such settings under a conditionally Gaussian reference model. Based on these results, optimal bounded-influence versions of the classical likelihood-based tests for parametric hypotheses are obtained. We propose a feasible and efficient algorithm for the computation of our robust estimators, which makes use of analytical Laplace approximations to estimate the auxiliary recentering vectors ensuring Fisher consistency in robust estimation. This strongly reduces the necessary computation time by avoiding the simulation of multidimensional integrals, a task that has typically to be addressed in the robust estimation of nonlinear models for time series. In some Monte Carlo simulations of an AR(1)-ARCH(1) process we show that our robust procedures maintain a very high efficiency under ideal model conditions and at the same time perform very satisfactorily under several forms of departure from conditional normality. On the contrary, classical Pseudo Maximum Likelihood inference procedures are found to be highly inefficient under such local model misspecifications. These patterns are confirmed by an application to robust testing for ARCH.Time series models, M-estimators, influence function, robust estimation and testing
Robustness of power properties of non-linearity tests
The paper examines the robustness of the size and power properties of the standard non-linearity tests under different conditions such as moment failure and asymmetry of innovations. Our results reveal the following. First, there seems not to be a direct link between moment condition failure and the power variation of non-linearity tests. Second, the power of the tests is very sensitive to asymmetry of innovations compared to moment condition failure. Third, although we evaluate 9 non-linear time series models using 8 standard non-linearity tests, some non-linear models remain completely undetected
Asymptotically Optimal Tests when Parameters are Estimated
The main purpose of this paper is to provide an asymptotically optimal test.
The proposed statistic is of Neyman-Pearson-type when the parameters are
estimated with a particular kind of estimators. It is shown that the proposed
estimators enable us to achieve this end. Two particular cases, AR(1) and ARCH
models were studied and the asymptotic power function was derived
Sequential Specification Tests to Choose a Model: A Change-Point Approach
Researchers faced with a sequence of candidate model specifications must
often choose the best specification that does not violate a testable
identification assumption. One option in this scenario is sequential
specification tests: hypothesis tests of the identification assumption over the
sequence. Borrowing an idea from the change-point literature, this paper shows
how to use the distribution of p-values from sequential specification tests to
estimate the point in the sequence where the identification assumption ceases
to hold. Unlike current approaches, this method is robust to individual errant
p-values and does not require choosing a test level or tuning parameter. This
paper demonstrates the method's properties with a simulation study, and
illustrates it by application to the problems of choosing a bandwidth in a
regression discontinuity design while maintaining covariate balance and of
choosing a lag order for a time series model
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The weekly structure of US stock prices
In this paper we use fractional integration techniques to examine the degree of integration of four US stock market indices, namely the Standard and Poor, Dow Jones, Nasdaq and NYSE, at a daily frequency from January 2005 till December 2009. We analyse the weekly structure of the series and investigate their characteristics depending on the specific day of the week. The results indicate that the four series are highly persistent; a small degree of mean reversion (i.e., orders of integration strictly smaller than 1) is found in some cases for
S&P and the Dow Jones indices. The most interesting findings are the differences in the degree of dependence for different days of the week. Specifically, lower orders of
integration are systematically observed for Mondays and Fridays, consistently with the “day of the week” effect frequently found in financial data.The second-named author gratefully acknowledges financial support from the the
Ministerio de Ciencia y TecnologĂa (ECO2008-03035 ECON Y FINANZAS, Spain) and from a PIUNA Project from the University of Navarra
A Test of the Adaptive Market Hypothesis using a Time-Varying AR Model in Japan
This study examines the adaptive market hypothesis (AMH) in Japanese stock
markets (TOPIX and TSE2). In particular, we measure the degree of market
efficiency by using a time-varying model approach. The empirical results show
that (1) the degree of market efficiency changes over time in the two markets,
(2) the level of market efficiency of the TSE2 is lower than that of the TOPIX
in most periods, and (3) the market efficiency of the TOPIX has evolved, but
that of the TSE2 has not. We conclude that the results support the AMH for the
more qualified stock market in Japan.Comment: 10 pages, 2 figure, 2 table
A time series analysis of U.K. construction and real estate indices
This study assess the nonlinear behavior of U.K. Construction and Real Estate indices. Standard unit root tests show that both time series are I(1) processes. However, the empirical results show that the returns series for both indices deviate from the null hypothesis of white noise. Moreover, we have found evidence of nonlinearity but strong evidence against chaos for the returns series. Further tests show that the source of nonlinearity is rather different. Hence, the Construction index returns series displays weak nonlinear forecastability, typical of nonlinear deterministic processes, whereas the Real Estate index could be characterized as a stationary process about a nonlinear deterministic trend
A new class of distribution-free tests for time series models specification
The construction of asymptotically distribution free time series model specification tests using as
statistics the estimated residual autocorrelations is considered from a general view point. We
focus our attention on Box-Pierce type tests based on the sum of squares of a few estimated
residual autocorrelations. This type of tests belongs to the class defined by quadratic forms of
weighted residual autocorrelations, where weights are suitably transformed resulting in
asymptotically distribution free tests. The weights can be optimally chosen to maximize the
power function when testing in the direction of local alternatives. The optimal test in this class
against MA, AR or Bloomfield alternatives is a Box-Pierce type test based on the sum of
squares of a few transformed residual autocorrelations. Such transformations are, in fact, the
recursive residuals in the projection of the residual autocorrelations on a certain score function
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