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Analysis of the causes of unemployment in DKI Jakarta using panel data regression
Published in Yuli Rahmawati, Peter Charles Taylor, Empowering Science and Mathematics for Global Competitiveness, 2019
W. Rahayu, V. Maya Santi, D. Siregar
The Chow test is a hypothesis testing between the combined model and the fixed influence model. The testing procedure is as follows: Hypotheses:H0: β01 = … = β0 N = 0 (Combined Model)H1: There is at least one β0i ≠ 0 (Fixed Influence Model)
Vehicle-following behaviour in mixed traffic – role of lane position and adjacent vehicle
Published in Transportation Letters, 2023
Kavitha Madhu, Karthik K. Srinivasan, R. Sivanandan
The models are statistically compared in addition to the analysis of signs, magnitudes, and significance of various model coefficients (given in Table 3). The Chow test can be used to test whether the true coefficients in two linear regressions on different data sets are equal or not (Gujarati 2004). In the present study, Chow test provides evidence that the acceleration model for adjacent vehicle present and absent are structurally different from each other and from the combined unsegmented model (at alpha = 5%). This test indicates that the following behavior varies based on the presence or absence of adjacent vehicle. To investigate how the coefficients vary across different models under consideration, the regression coefficients of the three variables (absolute speed, longitudinal gap, relative speed) are compared in Table 3.
Calibration and validation of person-based trip production models of optional trip purposes
Published in Transportation Planning and Technology, 2021
Jihye Kim, Ikki Kim, Jaeyeob Shim
Furthermore, it was statistically tested whether all independent variables (including constants) had the same coefficients in the models calibrated with two-year data using Chow testing. If the null hypothesis is accepted based on comparing the F value (as expressed in Equation (2)) with the critical value, then the two models are regarded as having the same variable and constant coefficients, as the two models are the same. The Chow test affirms the null hypothesis only when all coefficients of each variable in the two models are equal, whereas it rejects the null hypothesis if any one of the coefficients is different. Therefore, the Chow test verifies the prediction stability of all models, not by each coefficient. where are the coefficient of independent variables i in year 2006 or year 2010 model ( linear regression model); is the error sum of squares of the combined model; are the error sum of squares of year 2006 or year 2010 model; P is the number of parameters in the model including the constant and is the number of observation data.