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Books on the topic 'Regression with ARIMA Errors'

Author: Grafiati

Published: 9 September 2021

Last updated: 29 July 2025

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1

Bagchi, Parthasarathy. Bayesian regression analysis under non-normal errors. University of Toronto, Dept. of Statistics, 1987.

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2

Hella, Heikki. On robust ESACF indentification [sic] of mixed ARIMA models. Bank of Finland, 2003.

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3

Robinson, P. M. Large-sample inference for nonparametric regression with dependent errors. Suntory and Toyota International Centres for Economics and Related Disciplines, 1997.

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4

Stock, James H. Heteroskedasticity-robust standard errors for fixed effects panel data regression. National Bureau of Economic Research, 2006.

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5

Kurumai, Hiroko. MSE performance of some shrinkage estimators in a regression model with non-normal errors. Institute of Economic Research, Kobe University of Commerce, 2001.

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6

Rahiala, Markku. On the identification and estimation of multiple input transfer function models with autocorrelated errors. Research Institute of the Finnish Economy, 1985.

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7

Edronova, Valentina. Statistics. INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1577822.

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In this textbook, the methodology of statistics is presented in the unity of quantitative assessment and meaningful analysis of the state, development, forecast and connections of socio-economic phenomena. The evolution of statistics as a science and its subject, methods of calculating absolute, relative, average indicators in the assessment of socio-economic phenomena are considered. The content and types of statistical observation and summary are described. The characteristic of selective observation and methods of calculating its errors are given. The methods of research of developmen

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8

Khan, Aman, and Kenneth A. Scorgie. Forecasting Government Budgets. The Rowman & Littlefield Publishing Group, 2022. https://doi.org/10.5040/9781666990355.

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Forecasting is integral to all governmental activities, especially budgetary activities. Without good and accurate forecasts, a government will not only find it difficult to carry out its everyday operations but will also find it difficult to cope with the increasingly complex environment in which it has to operate. This book presents, in a simple and easy to understand manner, some of the commonly used methods in budget forecasting, simple as well as advanced. The book is divided into three parts: It begins with an overview of forecasting background, forecasting process, and forecasting metho

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9

Pevehouse, Jon, and Jason D. Brozek. Time‐Series Analysis. Edited by Janet M. Box-Steffensmeier, Henry E. Brady, and David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0019.

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This article discusses time-series methods such as simple time-series regressions, ARIMA models, vector autoregression (VAR) models, and unit root and error correction models (ECM). It specifically presents a brief history of time-series analysis before moving to a review of the basic time-series model. It then describes the stationary models in univariate and multivariate analyses. The nonstationary models of each type are addressed. In addition, various issues regarding the analysis of time series including data aggregation and temporal stability are considered. Before concluding, the articl

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10

Delongchamp, Robert. Analysis of epidemiological data with covariate errors. 1993.

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11

Arashi, Mohammad, A. K. Ehsanes Saleh, and S. M. M. Tabatabaey. Statistical Inference for Models with Multivariate T-Distributed Errors. Wiley & Sons, Incorporated, John, 2014.

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12

Arashi, Mohammad, A. K. Ehsanes Saleh, and S. M. M. Tabatabaey. Statistical Inference for Models with Multivariate T-Distributed Errors. Wiley & Sons, Limited, John, 2014.

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13

A. K. Md. Ehsanes Saleh, Mohammad Arashi, and S. M. M. Tabatabaey. Statistical Inference for Models with Multivariate t-Distributed Errors. Wiley, 2014.

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14

Arashi, Mohammad, A. K. Ehsanes Saleh, and S. M. M. Tabatabaey. Statistical Inference for Models with Multivariate T-Distributed Errors. Wiley & Sons, Incorporated, John, 2014.

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15

Saleh, A. K., S. M. Tabatabaey, and Mohammad Arashi. Statistical Inference for Models with Multivariate T-Distributed Errors. Wiley & Sons, Incorporated, John, 2014.

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16

Arashi, Mohammad, A. K. Ehsanes Saleh, and S. M. M. Tabatabaey. Statistical Inference for Models with Multivariate T-Distributed Errors. Wiley & Sons, Incorporated, John, 2014.

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17

Random regression models with autocorrelated errors: Investigating drug plasma levels and clinical response. 1989.

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