An improved portmanteau test for autocorrelated errors in interrupted time-series regression models

In 2012, Washington State legalized the production, sale, and possession of marijuana through Initiative 502. Advocates of legalization argued that it would decrease the jail population and reduce the disproportionate incarceration of minorities, reasoning that the police would refocus their resources on other matters. In order to evaluate this assumption, we examined jail booking data using a set of interrupted time-series regression models. Our findings indicate that jail population trends differ among counties across time and with respect to impacts on minorities and women. With regard to ethnic and racial disproportionate impact, there appears to be little positive change.

Download Full-text

The Runs Test for Autocorrelated Errors: Unacceptable Properties

Journal of Educational and Behavioral Statistics ◽

10.3102/10769986021004390 ◽

1996 ◽

Vol 21 (4) ◽

pp. 390-404 ◽

Cited By ~ 10

Author(s):

Bradley E. Huitema ◽

Joseph W. McKean ◽

Jinsheng Zhao

Keyword(s):

Time Series ◽

Regression Models ◽

Degrees Of Freedom ◽

Type I Error ◽

Error Rates ◽

Type I ◽

Time Series Regression ◽

Monte Carlo Investigation ◽

Autocorrelated Errors ◽

Asymmetrical Error

The runs test is frequently recommended as a method of testing for nonindependent errors in time-series regression models. A Monte Carlo investigation was carried out to evaluate the empirical properties of this test using (a) several intervention and nonintervention regression models, (b) sample sizes ranging from 12 to 100, (c) three levels of α, (d) directional and nondirectional tests, and (e) 19 levels of autocorrelation among the errors. The results indicate that the runs test yields markedly asymmetrical error rates in the two tails and that neither directional nor nondirectional tests are satisfactory with respect to Type I error, even when the ratio of degrees of freedom to sample size is as high as .98. It is recommended that the test generally not be employed in evaluating the independence of the errors in time-series regression models.

Download Full-text

Real-Time Prediction of the COVID-19 Epidemic in Thailand using Simple Model-Free Method and Time Series Regression Model

Walailak Journal of Science and Technology (WJST) ◽

10.48048/wjst.2021.10028 ◽

2021 ◽

Vol 18 (14) ◽

Author(s):

Rati WONGSATHAN

Keyword(s):

Genetic Algorithm ◽

Time Series ◽

Regression Model ◽

Regression Models ◽

Gaussian Function ◽

Logistic Function ◽

Hyperbolic Tangent ◽

Time Series Regression ◽

Model Free ◽

Tangent Function

The novel coronavirus 2019 (COVID-19) pandemic was declared a global health crisis. The real-time accurate and predictive model of the number of infected cases could help inform the government of providing medical assistance and public health decision-making. This work is to model the ongoing COVID-19 spread in Thailand during the 1st and 2nd phases of the pandemic using the simple but powerful method based on the model-free and time series regression models. By employing the curve fitting, the model-free method using the logistic function, hyperbolic tangent function, and Gaussian function was applied to predict the number of newly infected patients and accumulate the total number of cases, including peak and viral cessation (ending) date. Alternatively, with a significant time-lag of historical data input, the regression model predicts those parameters from 1-day-ahead to 1-month-ahead. To obtain optimal prediction models, the parameters of the model-free method are fine-tuned through the genetic algorithm, whereas the generalized least squares update the parameters of the regression model. Assuming the future trend continues to follow the past pattern, the expected total number of patients is approximately 2,689 - 3,000 cases. The estimated viral cessation dates are May 2, 2020 (using Gaussian function), May 4, 2020 (using a hyperbolic function), and June 5, 2020 (using a logistic function), whereas the peak time occurred on April 5, 2020. Moreover, the model-free method performs well for long-term prediction, whereas the regression model is suitable for short-term prediction. Furthermore, the performances of the regression models yield a highly accurate forecast with lower RMSE and higher R2 up to 1-week-ahead. HIGHLIGHTS COVID-19 model for Thailand during the first and second phases of the epidemic The model-free method using the logistic function, hyperbolic tangent function, and Gaussian function applied to predict the basic measures of the outbreak Regression model predicts those measures from one-day-ahead to one-month-ahead The parameters of the model-free method are fine-tuned through the genetic algorithm GRAPHICAL ABSTRACT

Download Full-text