Momento de extraer conclusiones cualitativas basadas en una inferencia estándarĭe mínimos cuadrados ordinarios realizada en el contexto de un análisis de Medida en que compartimos completamente nuestros códigos y utilizamos la base deĭatos de acceso abierto del documento seminal escrito por Nelson y PlosserĮs simple: los investigadores empíricos deben ser absolutamente cautelosos al Nuestros ejercicios cuantitativos son replicables en la Granger, que destacamos con originalidad utilizando una perspectiva desde la Abordamos gráficamente la distribuciónĮmpírica del sesgo del estimador y el inconveniente del uso de errores estándar Nuestros resultados de Monte Carlo muestran que en presencia de seriesĭe tiempo estacionarias y no estacionarias, la inferencia basada en los mínimosĬuadrados ordinarios puede ser engañosa.
Nuestro propósito es ilustrar este tipo de análisis utilizando algunas ideas de Juego y de los supuestos probabilísticos subyacentes del modelo aplicado.
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Reconocimiento de las características estadísticas de las series de tiempo en La realización de un análisis estadístico confiable se fundamenta en el Keywords: reliable statistical analysis standard OLS inference empirical bias replicability measurement in economics Qualitative findings based on a standard ordinary least squares inferenceĬarried out in the context of a regression analysis. Our quantitative exercises are replicable to the extent that we fully shared ourĬodes in addition to using an open-access database of the seminal paper writtenĬonclusion is simple: empirical researchers should be cautious when drawing We highlight with originality using a “measurement in economics” perspective. We recommend following Granger’s suggestions, which Graphically address the empirical distribution of the estimator’s bias as wellĪs the inconvenience of using standard errors to illustrate how the true The standard ordinary least squares inference could be misleading.
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Results show that in the presence of stationary and non-stationary time series, This kind of analysis using Granger’s groundbreaking ideas. Probabilistic assumptions of the applied model. The statistical features of the time series at stake and on the underlying
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