Thesis (M.S.)--University of Nevada, Reno, 2006. / "December, 2006." Includes bibliographical references (leaves 38-40). Online version available on the World Wide Web.

This work was undertaken with a view to construct a macroeconometric model for the Indian economy for purposes of forecasting and policy simulation. As a prelude to this exercise, we have surveyed available forecasting techniques, techniques of evaluation of forecasts and forecasting models and the issues concerning the use of macroeconometric models in the context of policy analysis. We have also considered specific issues and considerations relevant in the context of developing countries. As a second step towards providing a proper perspective to our model, and to derive useful guidelines, we have surveyed and reviewed existing macro-econometric models of the Indian economy. This survey concerns the models built by Narasimham, Choudhry, Krishnamurty, Krishnamurty-Choudhry, Marwah, Mammen, Agarwala, Pandit, Gupta, Bhattacharya and UNCTAD. We notice that an interesting variety of sectoral emphasis is offered in these models although in general they are all based on the IS-LM framework. As a part of the review of the existing models, we have re-estimated three models, viz., models by Choudhry, Marwah and Bhattacharya under common sample conditions and estimation techniques and have compared their forecasting performance against alternative autoregressive benchmark models. In general, the benchmark models do better but the performance of Bhattacharya and Marwah models, in their adapted versions, seems to be satisfactory. A common shortcoming of all these models is an underexploration of the fiscal sector of the economy. Generally, the government budget restraint has been ignored, the treatment of tax functions is highly aggregated and all government expenditure variables are treated as exogenous. Furthermore, the estimates in these models have become dated because of major data revisions. On these grounds and also as a part of continuing efforts towards building macroeconometric models for the Indian economy, we have specified, estimated and analysed a new model containing thirty four equations out of which eighteen are stochastic. Its special features are an endogenous treatment of government consumption expenditure, a disaggregated treatment of tax-revenue functions, an endogenous money-supply function and a distinction between the agricultural and non-agricultural sectors in terms of prices, outputs and investments. The model is estimated by mixed estimation procedures. In particular, two stage least squares with subsets of predetermined variables in the first stage and with first order autoregressive corrections in a few cases have been used. The model is used for forecasting and policy simulation. Its forecasting performance, within the sample period, and in a 'pseudo' forecast period is found to be satisfactory compared against 'naive' and 'not-so-naive' extrapolative benchmark models. Various policy simulations have been done and subsequently the model is used for conditional forecasting. We find that increases in government consumption expenditure have detrimental effects on real output, that changes in tax-rates and discount rates have very marginal impact on the system and that important policy changes relate to expenditure variables and government deficit financing.

Project (M.U.A.)--Virginia Polytechnic Institute and State University, 1991. / Includes bibliographical references (leaf 27). Also available via the Internet.

Transportation funding is critical to maintaining the assets that provide mobility for the movement of Georgia's people and goods. Currently, most of Georgia's transportation revenue is provided by the motor fuel tax. Inflation and recent increases in fuel economy have decreased fuel tax revenue in Georgia and weakened the Georgia Department of Transportation's (GDOT)'s ability to maintain and expand its transportation network. This thesis synthesizes factors from literature that affect motor fuel tax revenue. These include demographic, economic, technological, and environmental forces that influence travel behavior and vehicle fuel economy. A model was then created that incorporated these factors to model GDOT's 2009 fuel tax revenue and then project revenue in 2020 and 2030. The model uses an input/output structure that segments the fleet into personal, freight, and transit categories. User inputs, historical data, and projections are linked via relationships and feedback loops to project travel and fuel tax revenue forward. Because a near-infinite number of scenarios exist, conservative and aggressive scenarios were created for 2020 and 2030 scenarios that output revenue on an absolute, per-mile, and per-capita basis for comparison with more recent revenues. The model outputs predict marginal declines in revenue by 2020 and significant declines by 2030. In response to these declines, the thesis evaluates methods of increasing transportation revenue. These methods include increasing the fuel tax, incorporating a VMT-fee, and widespread tolling measures. After evaluation, a policy recommendation is provided for how to best implement revenue strategies.

The purpose of this study is to determine which of three forecasting methods provides the most accurate short-term forecasts, in terms of absolute and mean absolute percentage error, for a unique set of data. The study applies three forecasting techniques--the Box-Jenkins or ARIMA method, cycle regression analysis, and multiple regression analysis--to quarterly sales tax revenue data. The final results show that, with varying success, each model identifies the direction of change in the future, but does not closely identify the period to period fluctuations. Indeed, each model overestimated revenues for every period forecasted. Cycle regression analysis, with a mean absolute percentage error of 7.21, is the most accurate model. Multiple regression analysis has the smallest absolute percentage error of 3.13.

Thesis (M.S.)--University of Texas at El Paso, 2008. / Title from title screen. Vita. CD-ROM. Includes bibliographical references. Also available online.

This study seeks a method of evaluating the local impact of changing economic conditions (such as employment, per capita income, et cetera). Specifically, measures of the impact of changing economic conditions on the services of local governments will be formulated. These measures provide a means for comparing the impact of a project (or of alternative projects) to conditions that would prevail in its (their) absence. The data used is drawn from the Virginia Impact Projection (VIP) Model in the form of statistically derived equations representing the relationships for eleven different categories of public services. The analytical framework was developed based on theories of public decision-making, public finance, and local government expenditures and services. This framework was used to analyze the per capita expenditure relationships (functions) to obtain the information required in formulating the impact measures. For individual categories of services, a SERVICE VALUE INDEX was devised to indicate changes (including shifts) in the demand for the services, and the level of provision thereof. The NET PUBLIC SERVICE BENEFIT is a measure of the overall increase (decline) in public services provision, and balances the change in levels of service on the individual categories against each other. This measure reflects the net benefits derived by the consumer-voter following her/his reallocation of expenditures given the new cost conditions, budget constraints, et cetera, as a consequence of the project. / Master of Urban Affairs

Statistics / M. Sc. (Statistics)