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Traffic Crash Analysis And Prevention
Published in Dušan Teodorović, The Routledge Handbook of Transportation, 2015
If the size of the road segment varies, it is important to include a variable to control for this size. Of vital importance in traffic crash models is the inclusion of a measure of traffic crash risk exposure, such as traffic volume for automobile crashes. Without a measure of exposure the model is likely to lead to biased results, since other variables, correlated with exposure, will suffer from omitted variables bias. Typically, the time period remains fixed for all observations and does not need to be controlled for. If this time period is too short, it can lead to a problem due to a preponderance of zero counts, which violates the Poisson or negative binomial model distributions. There are various ways to handle this issue if it occurs, although some are debated (Lord et al., 2007).
Violations of Regression Assumptions
Published in Simon Washington, Matthew Karlaftis, Fred Mannering, Panagiotis Anastasopoulos, Statistical and Econometric Methods for Transportation Data Analysis, 2020
Simon Washington, Matthew Karlaftis, Fred Mannering, Panagiotis Anastasopoulos
The first is when a relevant variable is omitted from the specified model (underfitting a model). Excluding an important variable from a model is frequently called omitted variable bias. To illustrate, suppose the correct model is () Yi=β0+β1X1i+β2X2i+εi
Big Entropy and the Generalized Linear Model
Published in Richard McElreath, Statistical Rethinking, 2020
Back in Chapters 5 and 6, you saw some examples of omitted variable bias, where leaving a causally important variable out of a model leads to biased inference. The same thing can of course happen in GLMs. But it can be worse in GLMs, because even a variable that isn’t technically a confounder can bias inference, once we have a link function. The reason is that the ceiling and floor effects described above can distort estimates by suppressing the causal influence of a variable.
Local empowerment and irrigation devolution in Ethiopia
Published in International Journal of Water Resources Development, 2022
Rahel Deribe Bekele, Dawit Mekonnen
The analysis was implemented at the plot level to capture more spatial heterogeneity and minimize omitted variable bias. Due to a collinearity problem between water management systems and interaction terms, a separate effect of a water management system on empowerment indicators was omitted. Thus, only interactions of the various irrigation water management systems and complementary irrigation technologies are captured. We tested whether there is a problem of multicollinearity among explanatory variables, but it was found only among the climate variables as one would expect. The correlation between these variables was leading to high variance inflation factors (VIFs) of between 3.83 and 69.71. However, all the variables in the models are included since they are statistically significant coefficients. Moreover, omitting one of the variables would result in omitted variables bias. The other variables had a variance inflation factor of < 2.08, indicating that multicollinearity was not a major concern for these variables (Gujarati, 1995). The White heteroscedasticity-robust covariance matrix (White, 1980), which is robust to heteroskedasticity of unknown form, was used. It was also tested if there is a problem of incorrect functional form. The result demonstrated that there was no evidence of functional form misspecification. Furthermore, the Pearson goodness-of-fit test was performed; all four probit models fit reasonably well.
Responding to epidemic-driven demand: the role of supply channels
Published in International Journal of Production Research, 2022
Jaeseok Lee, Min Kyung Lee, Seongkyoon Jeong, Brandon Lee, Minseok Park
Note that in our empirical models, we address endogeneity issues by utilising three different major approaches, as suggested by Lu et al. (2018): the treatment effect model, adding control variables, and fixed effect models. First of all, our models operationalise an exogenous shock as a treatment variable (Post MERS), which is not based on inherent decisions made by individual manufacturers. In addition, to address omitted variable bias, as shown in Section 3.3, we added control variables based on the relevant literature. Admittedly, fully addressing omitted variable bias is a challenge, in that one cannot include all possible control variables in a model (Lu et al. 2018). Therefore, to further capture individual heterogeneity, we used fixed effect models by including time-specific fixed effects, as well as firm fixed effects.
The best of times and the worst of times: empirical operations and supply chain management research
Published in International Journal of Production Research, 2018
Steven A. Melnyk, Barbara B. Flynn, Amrou Awaysheh
Omitted variable bias usually arises because an important independent variable was omitted because empirical data was not available to measure it (Hamilton and Nickerson 2003). For example, assume that the model for y is: