Local Asymptotic Specification Error Analysis

Kiefer, Nicholas M.; Skoog, Gary R.

doi:10.2307/1911189

Cited by 28 publications

(17 citation statements)

References 10 publications

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“…The asymptotic bias of the non-linear FI estimator is calculated as in Kiefer and Skoog (1984) as the derivative of this bias with respect to cZ,. See the appendix for details.…”

Section: Asymptotic Bias and Power Of Testsmentioning

confidence: 99%

Full Versus Limited Information Estimation of a Rational Expectations Model: Some Numerical Comparisons

West

1986

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This paper compares numerically the asymptotic distributions of parameter estimates and test statistics associated with two estimation techniques: (a) a limited-information one, which uses instrumental variables to estimate a single equation [Hansen and Singleton (1982)], and (b) a full-information one, which uses a procedure asymptotically equivalent to maximum likelihood to simultaneously estimate multiple equations [Hansen and Sargent (1980)]. The paper compares the two with respect to both (1) asymptotic efficiency under the null hypothesis of no misspecification, and (2) asymptotic bias and power in the presence of certain local alternatives. It is found that (1) full-information standard errors are only moderately smaller than limited-information standard errors, and (2) when the model is misspecified, full-information tests tend to be more powerful, and its parameter estimates tend to be more biased. This suggests that at least in the model considered here, the gains from the use of the less robust and computationally more complex full-information technique are not particularly large.

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“…The asymptotic bias of the non-linear FI estimator is calculated as in Kiefer and Skoog (1984) as the derivative of this bias with respect to cZ,. See the appendix for details.…”

Section: Asymptotic Bias and Power Of Testsmentioning

confidence: 99%

Full Versus Limited Information Estimation of a Rational Expectations Model: Some Numerical Comparisons

West

1986

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“…The model has a high explanatory power with a high and highly significant 2 . Tests on ordered logit models provide evidence that omitted uncorrelated explanatory variables and neglected heteroscedasticity may generate inconsistent parameter estimates in this type of models (Kiefer & Skoog, 1984). To test for these misspecifications, an artificial regression based on LM (Lagrange Multiplier) test was used.…”

Section: Resultsmentioning

confidence: 99%

The Coordination Imperative for MNE: Empirical Evidence from Foreign Operations in Greece

Manolopoulos

2007

CMR

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Globally, Multinational Enterprises (MNEs) are increasingly focusing on devising strategies in order to balance between the local responsiveness at the subsidiary level and the international integration of operations for global competitiveness. It is against this background, the research studies focusing on the need for evolving effective coordination functions within the multinational network are gaining importance. The present research study makes an attempt to investigate the prevalence of the specific coordination mechanisms in the management of MNEs' activities in Greece and relates them to the underlying corporate culture, relevant information technology mechanisms and the intrinsic organizational architectures so as to serve as valuable tool for managing the interdependencies among the MNE subunits. A survey instrument (questionnaire) was administered to a sample size of 317 foreign firms in Greece to gain insights in to the various factors that determined the use of the appropriate coordination mechanisms so as to manage the interdependencies between HQs and subsidiaries. Multiple regression method was used for our empirical analysis and the results supported the hypotheses put forward in the research design. Our findings further demonstrated that MNEs in Greece manage coordination by tailoring the appropriate instruments to the specific mandates assigned to their subsidiaries thereof.

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“…In particular this concerns the identification of the variance of the latent variable, which becomes a central problem when one wants to compare or pool different data sets. Moreover, failure to correct for true underlying heteroskedasticity implies inconsistent parameter estimates, contrary to conventional continuous regression models; see for example [12,17]. In this section we first replicate the baseline CEHM results (based on their data).…”

Section: Referenda and Between-sample Heteroskedasticity: The Cehm Datamentioning

confidence: 98%

Scale factors and hypothetical referenda: A clarifying note

Carlsson

Johansson‐Stenman

2010

Journal of Environmental Economics and Management

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Local Asymptotic Specification Error Analysis

Cited by 28 publications

References 10 publications

Full Versus Limited Information Estimation of a Rational Expectations Model: Some Numerical Comparisons

Full Versus Limited Information Estimation of a Rational Expectations Model: Some Numerical Comparisons

The Coordination Imperative for MNE: Empirical Evidence from Foreign Operations in Greece

Scale factors and hypothetical referenda: A clarifying note

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