On Periodicity in Series of Related Terms

Walker, G. O.

doi:10.1175/1520-0493(1931)59<277:opisor>2.0.co;2

Cited by 71 publications

(56 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Clearly, estimation based on solutions to linear equations, such as OLS or Yule-Walker equations (Yule 1927;Walker 1931;Brockwell and Davis 1991), can provide causal inference only if there is no directed cycle. This is the central reason why DAGs have been very useful in causal models.…”

Section: Assumptions and Theoretical Resultsmentioning

confidence: 99%

Causal ordering and inference on acyclic networks

2018

View full text Add to dashboard Cite

This paper develops a new identification result for the causal ordering of observation units in a recursive network or directed acyclic graph. Inferences are developed for an unknown spatial weights matrix in a spatial lag model under the assumption of recursive ordering. The performance of the methods in finite sample settings is very good. Application to data on portfolio returns produces interesting new evidences on the contemporaneous lead-lag relationships between the portfolios and generates superior predictions.Keywords Spatial weights matrix · Directed acyclic graphs · Recursive ordering · M-estimation · Portfolio returns · Partial identificationWe thank the Editors for their encouragement and invitation to contribute towards this Special Issue in honour of Ingmar Prucha. We are especially thankful to two anonymous reviewers for making many helpful suggestions that helped us revise and substantially improve upon this paper. Thanks also due to Nidhan Choudhuri, Atanas Christev and Rod McCrorie for helpful discussions. The usual disclaimer applies.

show abstract

Section: Assumptions and Theoretical Resultsmentioning

confidence: 99%

Causal ordering and inference on acyclic networks

2018

View full text Add to dashboard Cite

show abstract

“…If the stationary condition in variance is not met, Box and Cox [15] introduced the rank transformation, namely [16] and later developed by Walker in 1931 [17], while the Moving Average (MA) model was first used by Slutzky in 1937 [18]. And in 1938, then Wold produced the theoretical underpinnings of the ARMA combination process [19].…”

Section: Arima Modelmentioning

confidence: 99%

Forecasting of Primary Energy Consumption Data in the United State: a comparison between ARIMA and Holter Winters Models

Rahman¹,

Ahmar²

2017

Preprint

View full text Add to dashboard Cite

show abstract

“…Su fundamentación matemática fue originalmente desarrollada por Yule [1] y Walker [2] en el caso de los modelos AR y por Slutzky [3] en el caso de los modelos MA. Posteriormente, Wold [4] generalizó los trabajos anteriores al introducir el modelo ARMA.…”

Section: Introductionunclassified

Estimación de los parámetros del modelo no lineal de promedios móviles usando la metaheuristica de-PSO

Cogollo

Velásquez

Jaramillo

2013

rev.ing.univ.Medellin

View full text Add to dashboard Cite

RESUMENLa extensión teórica de los modelos lineales de promedios móviles al caso no lineal es directa y sencilla. Sin embargo, el uso práctico de los modelos no lineales de promedios móviles es limitado debido a la complejidad del espacio de parámetros y la imposibilidad de las establecer derivadas analíticas de la función de estimación. En este artículo, se evalúa el uso del algoritmo híbrido Evolución DiferencialOptimización del Enjambre de Partículas para calcular los parámetros óptimos del modelo no lineal de promedios móviles. Los resultados obtenidos muestran que la técnica meta-heurística utilizada es capaz de calcular con mayor precisión los valores de los parámetros del modelo en comparación con los algoritmos tradicionales. Este hallazgo nos alienta a explorar el uso de las meta-heurísticas en la estimación de los parámetros de otros modelos no lineales de promedios móviles.

show abstract

On Periodicity in Series of Related Terms

Cited by 71 publications

References 0 publications

Causal ordering and inference on acyclic networks

Causal ordering and inference on acyclic networks

Forecasting of Primary Energy Consumption Data in the United State: a comparison between ARIMA and Holter Winters Models

Estimación de los parámetros del modelo no lineal de promedios móviles usando la metaheuristica de-PSO

Contact Info

Product

Resources

About