On consistent testing for serial correlation in seasonal time series models

Standard serial correlation tests are derived assuming that the disturbances are homoscedastic, but this study shows that asympotic critical values are not accurate when this assumption is violated. Asymptotic critical values for the ARCH(2)-corrected LM, BP and BL tests are valid only when the underlying ARCH process is strictly stationary, whereas Wooldridge's robust LM test has good properties overall. These tests exhibit similar bahaviour even when the underlying process is GARCH (1,1). When the regressors include lagged dependent variables, the rejection frequencies under both the null and alternative hypotheses depend on the coefficientsof the lagged dependent variables and the other model parameters. They appear to be robust across various disturbance distributions under the null hypothesis.

Hypothesis tests

As an extension of partially linear models and additive models, partially linear additive model is useful in statistical modelling. This paper proposes an empirical likelihood based approach for testing serial correlation in this semiparametric model. The proposed test method can test not only zero first-order serial correlation, but also higher-order serial correlation. Under the null hypothesis of no serial correlation, the test statistic is shown to follow asymptotically a chi-square distribution. Furthermore, a simulation study is conducted to illustrate the performance of the proposed method.

Specification Tests for Spatial Panel Data Models

Conditional absolute mean calibration for partial linear multiplicative distortion measurement errors models

The paper considers the problem of testing error serial correlation of partially linear additive measurement error model. We propose a test statistic and show that it converges to the standard chi-square distribution under the null hypothesis. Finally, a simulation study is conducted to illustrate the performance of the test approach.

Multi-scale tests for serial correlation

SUMMARY In order to test for serial correlation in residuals for static time series regression in the presence of missing data, the score principle is applied both to the likelihood conditional on the observation times, and to an unconditional form of likelihood. Asymptotic distributions of the test statistics are established, under both the null hypothesis of no serial correlation, and sequences of local, correlated, alternatives, enabling analytic comparison of efficiency.

This paper considers testing the null hypothesis that a times series is uncorrelated when the time series is uncorrelated but statistically dependent. This case is of interest in economic and finance applications. The GARCH(1, 1) model is a leading example of a model that generates serially uncorrelated but statistically dependent data. The tests of serial correlation introduced by Andrews and Ploberger (1996, hereafter AP) are generalized for the purpose of testing the null. The rationale for generalizing the AP tests is that they have attractive properties for cases for which they were originally designed: they are consistent against all nonwhite-noise alternatives and have good all-round power against nonseasonal alternatives compared to several widely used tests in the literature. These properties are inherited by the generalized AP tests.

By counting the “peaks” (relative maxima) of a time series, it is possible to estimate the serial correlation in the series, under the assumption that the series is a first-order autoregressive process. The estimator is inefficient, but it is effective nonetheless, and since all computations can be performed mentally and no tables are required, it is suitable for the quick assessment of computer-generated diagnostic plots. It also enjoys certain robustness properties. As a test for serial correlation, the procedure is essentially the same as one proposed by Bienayme in 1874 and by Wallis and Moore in 1941, which is itself equivalent to tests based on the counts of runs up and down.

We propose a general framework for the specification testing of continuous treatment effect models. We assume a general residual function, which includes the average and quantile treatment effect models as special cases. The null models are identified under the unconfoundedness condition and contain a nonparametric weighting function. We propose a test statistic for the null model in which the weighting function is estimated by solving an expanding set of moment equations. We establish the asymptotic distributions of our test statistic under the null hypothesis and under fixed and local alternatives. The proposed test statistic is shown to be more efficient than that constructed from the true weighting function and can detect local alternatives deviated from the null models at the rate of . A simulation method is provided to approximate the null distribution of the test statistic. Monte-Carlo simulations show that our test exhibits a satisfactory finite-sample performance, and an application shows its practical value.

In this paper we consider generalized chi-square goodness-of-fit tests based on increasingly finer partitions (as the sample size increases) for models with location-scale nuisance parameters. The asymptotic distributions are derived both under the null hypothesis and under local alternatives, obtained by taking contamination families of densities between the null hypothesis and fixed alternative hypotheses. If the number of random cells increases to infinity, the Rao-Robson-Nikulin test statistic is shown to be superior to the Watson-Roy and Dzhaparidze-Nikulin statistics. Conditions are derived under which it is optimal to let the number of classes tend to infinity.

Testing for random effects and serial correlation in spatial autoregressive models

Journal Article The Determinants of Equilibrium Unemployment in Britain: A Reply Get access S. J. Nickell S. J. Nickell Centre for Labour Economics, London Search for other works by this author on: Oxford Academic Google Scholar The Economic Journal, Volume 95, Issue 377, 1 March 1985, Pages 196–198, https://doi.org/10.2307/2233480 Published: 01 March 1985

Tests for serial correlation of unknown form in dynamic least squares regression with wavelets