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This study provides an econometric methodology to test a linear structural relationship among economic variables. We propose the so-called distance-difference (DD) test and show that it has omnibus power against arbitrary nonlinear structural relationships. If the DD-test rejects the linear model hypothesis, a sequential testing procedure assisted by the DD-test can consistently estimate the degree of a polynomial function that arbitrarily approximates the nonlinear structural equation. Using extensive Monte Carlo simulations, we confirm the DD-test's finite sample properties and compare its performance with the sequential testing procedure assisted by the J-test and moment selection criteria. Finally, through investigation, we empirically illustrate the relationship between the value-added and its production factors using firm-level data from the United States. We demonstrate that the production function has exhibited a factor-biased technological change instead of Hicks-neutral technology presumed by the Cobb–Douglas production function. [ABSTRACT FROM AUTHOR]

We review the literature on the autoregressive distributed lag (ARDL) model, from its origins in the analysis of autocorrelated trend stationary processes to its subsequent applications in the analysis of cointegrated non‐stationary time series. We then survey several recent extensions of the ARDL model, including asymmetric and non‐linear generalisations of the ARDL model, the quantile ARDL model, the pooled mean group dynamic panel data model and the spatio‐temporal ARDL model. [ABSTRACT FROM AUTHOR]

To purchase or authenticate to the full-text of this article, please visit this link: http://dx.doi.org/10.1111/j.1468-0262.2007.00809.x Byline: Jin Seo Cho (*), Halbert White (1) Keywords: Markov regime switching; mixture model; likelihood ratio statistic; null distribution; bounds for critical values; cartel stability Abstract: We analyze use of a quasi-likelihood ratio statistic for a mixture model to test the null hypothesis of one regime versus the alternative of two regimes in a Markov regime-switching context. This test exploits mixture properties implied by the regime-switching process, but ignores certain implied serial correlation properties. When formulated in the natural way, the setting is nonstandard, involving nuisance parameters on the boundary of the parameter space, nuisance parameters identified only under the alternative, or approximations using derivatives higher than second order. We exploit recent advances by Andrews (2001) and contribute to the literature by extending the scope of mixture models, obtaining asymptotic null distributions different from those in the literature. We further provide critical values for popular models or bounds for tail probabilities that are useful in constructing conservative critical values for regime-switching tests. We compare the size and power of our statistics to other useful tests for regime switching via Monte Carlo methods and find relatively good performance. We apply our methods to reexamine the classic cartel study of Porter (1983) and reaffirm Porter's findings. Author Affiliation: (*)School of Economics and Finance, Victoria University of Wellington, P.O. Box 600, Wellington, 6001, New Zealand;JinSeo.Cho@vuw.ac.nz (**)Dep. of Economics, University of California San Diego, 9500 Gilman Dr., La Jolla, CA 92093-0508, U.S.A.;hwhite@weber.ucsd.edu Article History: Manuscript received April, 2003; final revision received April, 2007.

We propose a framework for estimation of the conditional mean function in a parametric model with function space covariates. The approach employs a functional mean squared error objective criterion. Under regularity conditions, consistency and asymptotic normality are established. The analysis extends to situations where the asymptotic properties are influenced by estimation errors arising from the presence of nuisance parameters. Wald, Lagrange multiplier, and quasi‐likelihood ratio statistics are studied. An empirical application conducts lifetime income path comparisons across different demographic groups according to years of work experience. [ABSTRACT FROM AUTHOR]