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Empirical Accuracy of Immigration Fo...

Teke, John Nde,

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Empirical Accuracy of Immigration Forecasts in the United States: A Comparative Algorithmic Analysis /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Empirical Accuracy of Immigration Forecasts in the United States: A Comparative Algorithmic Analysis // John Nde Teke.
Author:	Teke, John Nde,
Description:	1 electronic resource (145 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-02, Section: A.
Contained By:	Dissertations Abstracts International86-02A.
Subject:	Statistics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31329704
ISBN:	9798383580691

Empirical Accuracy of Immigration Forecasts in the United States: A Comparative Algorithmic Analysis /
Teke, John Nde,

Empirical Accuracy of Immigration Forecasts in the United States: A Comparative Algorithmic Analysis /John Nde Teke. - 1 electronic resource (145 pages)

Source: Dissertations Abstracts International, Volume: 86-02, Section: A.

This study investigated the persistent differences between forecast and actual immigration classes of admission since 1980. The study sought to understand the performance of the United States immigration forecasting system and its ability to set targets since 1980. Forecast errors indicate the combined impacts of systematic and unsystematic errors, making understanding the composition of forecast errors crucial. The research employed quantitative research methods, specifically correlational research design, to analyze numerical data and identify patterns. The empirical analysis involved regression analysis of actual values on forecasts based on the Prediction - Realization diagram, a standard research methodology used in immigration forecasting. The study focused on evaluating forecasting algorithms to identify the error distribution, which differs from standard evaluations that assess the accuracy and reliability of prediction algorithms. The study analyzed immigration forecast errors related to noncitizen apprehensions and refugee arrivals, identifying the predominant error type as residual covariance. This predominant error type indicated the difficulty in accurately tracking nuanced trend variations. The finding suggested that the immigration forecasting system's ability to accurately reflect dynamic changes in noncitizen apprehensions and refugee arrivals is limited. Understanding residual covariance helps develop robust planning strategies and contingency plans. Theil's inequality coefficients were low across all immigration forecasts, indicating a commendable performance in capturing underlying patterns and trends. However, unsystematic error in immigration forecasting exists and is tolerable due to challenges in perfect predictions. Recognizing these limitations is crucial for improving accuracy and reliability. The awareness promotes a proactive approach to warranted adjustments that enhance forecasting system performance.

English

ISBN: 9798383580691Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Actual immigration classes

Empirical Accuracy of Immigration Forecasts in the United States: A Comparative Algorithmic Analysis /
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520 $a This study investigated the persistent differences between forecast and actual immigration classes of admission since 1980. The study sought to understand the performance of the United States immigration forecasting system and its ability to set targets since 1980. Forecast errors indicate the combined impacts of systematic and unsystematic errors, making understanding the composition of forecast errors crucial. The research employed quantitative research methods, specifically correlational research design, to analyze numerical data and identify patterns. The empirical analysis involved regression analysis of actual values on forecasts based on the Prediction - Realization diagram, a standard research methodology used in immigration forecasting. The study focused on evaluating forecasting algorithms to identify the error distribution, which differs from standard evaluations that assess the accuracy and reliability of prediction algorithms. The study analyzed immigration forecast errors related to noncitizen apprehensions and refugee arrivals, identifying the predominant error type as residual covariance. This predominant error type indicated the difficulty in accurately tracking nuanced trend variations. The finding suggested that the immigration forecasting system's ability to accurately reflect dynamic changes in noncitizen apprehensions and refugee arrivals is limited. Understanding residual covariance helps develop robust planning strategies and contingency plans. Theil's inequality coefficients were low across all immigration forecasts, indicating a commendable performance in capturing underlying patterns and trends. However, unsystematic error in immigration forecasting exists and is tolerable due to challenges in perfect predictions. Recognizing these limitations is crucial for improving accuracy and reliability. The awareness promotes a proactive approach to warranted adjustments that enhance forecasting system performance.
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