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1 – 10 of over 48000We provide a new characterization of the equality of two positive-definite matrices A and B, and we use this to propose several new computationally convenient statistical tests…
Abstract
We provide a new characterization of the equality of two positive-definite matrices A and B, and we use this to propose several new computationally convenient statistical tests for the equality of two unknown positive-definite matrices. Our primary focus is on testing the information matrix equality (e.g. White, 1982, 1994). We characterize the asymptotic behavior of our new trace-determinant information matrix test statistics under the null and the alternative and investigate their finite-sample performance for a variety of models: linear regression, exponential duration, probit, and Tobit. The parametric bootstrap suggested by Horowitz (1994) delivers critical values that provide admirable level behavior, even in samples as small as n = 50. Our new tests often have better power than the parametric-bootstrap version of the traditional IMT; when they do not, they nevertheless perform respectably.
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Dante Amengual, Gabriele Fiorentini and Enrique Sentana
The authors propose the information matrix test to assess the constancy of mean and variance parameters in vector autoregressions (VAR). They additively decompose it into several…
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The authors propose the information matrix test to assess the constancy of mean and variance parameters in vector autoregressions (VAR). They additively decompose it into several orthogonal components: conditional heteroskedasticity and asymmetry of the innovations, and their unconditional skewness and kurtosis. Their Monte Carlo simulations explore both its finite size properties and its power against i.i.d. coefficients, persistent but stationary ones, and regime switching. Their procedures detect variation in the autoregressive coefficients and residual covariance matrix of a VAR for the US GDP growth rate and the statistical discrepancy, but they fail to detect any covariation between those two sets of coefficients.
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Tae-Hwan Kim and Halbert White
To date, the literature on quantile regression and least absolute deviation regression has assumed either explicitly or implicitly that the conditional quantile regression model…
Abstract
To date, the literature on quantile regression and least absolute deviation regression has assumed either explicitly or implicitly that the conditional quantile regression model is correctly specified. When the model is misspecified, confidence intervals and hypothesis tests based on the conventional covariance matrix are invalid. Although misspecification is a generic phenomenon and correct specification is rare in reality, there has to date been no theory proposed for inference when a conditional quantile model may be misspecified. In this paper, we allow for possible misspecification of a linear conditional quantile regression model. We obtain consistency of the quantile estimator for certain “pseudo-true” parameter values and asymptotic normality of the quantile estimator when the model is misspecified. In this case, the asymptotic covariance matrix has a novel form, not seen in earlier work, and we provide a consistent estimator of the asymptotic covariance matrix. We also propose a quick and simple test for conditional quantile misspecification based on the quantile residuals.
Monalisa Sen, Anil K. Bera and Yu-Hsien Kao
In this chapter we investigate the finite sample properties of a Hausman test for the spatial error model (SEM) proposed by Pace and LeSage (2008). In particular, we demonstrate…
Abstract
In this chapter we investigate the finite sample properties of a Hausman test for the spatial error model (SEM) proposed by Pace and LeSage (2008). In particular, we demonstrate that the power of their test could be very low against a natural alternative like the spatial autoregressive (SAR) model.
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We investigate whether or not the effects of the subprime financial crisis on 12 Asian economies are similar to those of the Asian financial crisis by examining volatility…
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We investigate whether or not the effects of the subprime financial crisis on 12 Asian economies are similar to those of the Asian financial crisis by examining volatility spillovers and time-varying correlation between the US and Asian stock markets. After pretesting volatility causality and constancy of correlation, we estimate an appropriate smooth-transition correlation VAR-GARCH model for each Asian stock market. First, the empirical evidence indicates stark differences in stock market linkages between the two crises. The volatility causality comes from the crises-originating country. Volatility in Asian stock markets Granger-caused volatility in the US market during the Asian crisis, whereas volatility in the US stock market Granger-caused volatility in Asian stock markets during the subprime crisis. Second, decreased correlations during the period of financial turmoil were observed, especially during the Asian financial crisis. Third, the estimated points of transition in the correlation are indicative of market participants’ awareness of the ensuing stock market crashes in July 1997 and in September 2008.
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This paper proposes an efficient test designed to have power against alternatives where the error correction term follows a Markov switching dynamics. The adjustment to long run…
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This paper proposes an efficient test designed to have power against alternatives where the error correction term follows a Markov switching dynamics. The adjustment to long run equilibrium is different in different regimes characterised by the hidden state Markov chain process. Using a general nonlinear MS-ECM framework, we propose an optimal test for the null of no cointegration against an alternative of a globally stationary MS cointegration. The Monte Carlo studies demonstrate that our proposed tests display superior powers compared to the linear tests. In an application to price-dividend relationships, our test is able to find cointegration while linear based tests fail to do so.
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