Browsing by Subject "Regression analysis"

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Forecast of Revenue Freight Carried by Rail in Texas to 1990
(Council for Advanced Transportation Studies, 1974-04) Williams, David L.
Revenue tons of freight carried by rail in Texas have been forecast to 1990 using multiple regression analysis and trend analysis. Data were gathered on the dependent variable (revenue tons of freight carried by rail in Texas) and on twenty-nine independent variables (economic indicators of the Texas economy) for the base period 1950 to 1972. Missing values from the time series data were estimated with the aid of the OMNITAB computer program POLYFIT. Multiple regression analyses were used to measure the linear relationship between the dependent variable and a set of independent variables, taking into consideration the interrelationships between the independent variables. From these analyses, a set of ten independent variables was selected as providing the best predictor regression equation. Forecasts for each of the ten selected variables were computed for 1975, 1980, 1985, and 1990 by extrapolating a chosen trend curve. These forecasted values were then substituted into the regression equation to yield forecasts for the tons of revenue freight carried by rail in Texas.
The life insurer Risk-Based Capital ratio : panel data analysis
(2013-05) Beisenov, Aidyn; Sager, Thomas W.
Many studies suggest the ability of the NAIC Risk-Based Capital ratio (RBC ratio) to predict insurer insolvency. Based on the US life insurer (insurer) data for the period of 2005 to 2008, this study finds explanatory variables that have a statistically significant relationship with the RBC ratio. Advantages of panel data over cross-sectional and time series data analysis are exploited to make valid inference on coefficients of the explanatory variables. Testing for unobserved insurer and time effects and for dependence between these effects and the explanatory variables indicates the appropriateness of the fixed insurer and time effects model. Based on the ordinary least squares estimates, it is found that insurers' size, capital-to-asset ratio, and return on capital have a statistically significant relationship with the RBC ratio. Additionally, health product, annuity product, opportunity, and regulatory risks of insurers are related to the RBC ratio. Accounting for heteroscedasticity and autocorrelation for a given insurer yields the same coefficient estimates, but increased standard errors.
Three essays in econometrics
(2004) Lin, Shih-Chang; Donald, Stephen G.
In this dissertation, I would like to consider the efficient estimation of various models in the presence of heteroskedasticity of unknown form. The first essay focuses on mean sqaure errors comparison of linear regression model of hetetroskedasticity with unknown form. I compare higher order properties of the efficient estimators which include the GMM-type Cragg estimator, FGLS based on series and kernel estimations. The comparison is to calculate the approximate mean square errors of estimators using the Nagar type stochastic expansion. In the second essay, I consider the efficient estimation of partial linear regression model under heteroskedastictiy with unknown form. I propose an efficient estimator and prove it achieves Chamberlain’s (1992) semi-parametric efficiency bound. The new estimator I propose has the same first order asymptotic properties as Li’s (2000) estimator. My estimator has the potential advantage of analyzing the higher order asymptotics. The third essay considers the two-step series estimation method for generated regressors problem in context of semiparametric regression model under heteroskedastictiy of unknown form. I establish the root-n consistency and asymptotic normality results of the two-step series estimators. Compared to the double kernel estimator introduced by Stengos and Yan (2001), my estimator has some computational advantage and is more accurate in the sense of the asymptotic variance. Simulation results show that the two-step series estimator outperforms the double kernel estimator in terms of mean absolute bias and mean square error. The estimators considered in three essay involve the problem of choosing smoothing parameters. Therefore, I also demonstrate how to pick optimal smoothing parameters in each essay.