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Testing for the Sandwich-Form Covariance Matrix Applied to Quasi-Maximum Likelihood Estimation Using Economic and Energy Price Growth Rates

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Listed:
  • Lijuan Huo

    (Beijing Institute of Technology)

  • Jin Seo Cho

    (Yonsei Univ)

Abstract

This study aims to directly test for the sandwich-form asymptotic covariance matrix entailed by conditional heteroskedasticity and autocorrelation in the regression error. Given that none of the conditional heteroskedastic or autocorrelated regression errors yield the sandwich-form asymptotic covariance matrix for the least squares estimator, it is not necessary to estimate the asymptotic covariance matrix using the heteroskedasticity-consistent (HC) or heteroskedasticity and autocorrelation-consistent (HAC) covariance matrix estimator. Because of this fact, we first examine testing for the sandwich-form asymptotic covariance matrix before applying the HC or HAC covariance matrix estimator. For this goal, we apply the testing methodologies proposed by Cho and White (2015) and Cho and Phillips (2018) to fit the context of this study by extending the scope of their maximum test statistic to have greater power and further establishing a methodology to sequentially detect the influence of heteroskedastic and autocorrelated regression errors on the asymptotic covariance matrix. We affirm the theory on the test statistics of this study through a simulation and further apply our test statistics to economic and energy price growth rate data for illustrative purposes.

Suggested Citation

  • Lijuan Huo & Jin Seo Cho, 2019. "Testing for the Sandwich-Form Covariance Matrix Applied to Quasi-Maximum Likelihood Estimation Using Economic and Energy Price Growth Rates," Working papers 2019rwp-152, Yonsei University, Yonsei Economics Research Institute.
    • Handle: RePEc:yon:wpaper:2019rwp-152
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    References listed on IDEAS

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    More about this item

    Keywords

    Information matrix equality; sandwich-form covariance matrix; heteroskedasticity-consistent covariance matrix estimator; heteroskedasticity and autocorrelation-consistent covariance matrix estimator; economic growth rate; energy price growth rate.;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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