There is a need in the scientific, technology, and financial communities for economic forecast models that improve the ability to estimate new or immature technology developments. Engineering design or conceptual technical requirements with which to drive parametric estimates or translate analogous system costs are often unavailable in early life-cycle stages of technology development. The limited availability of comparable systems, design or performance parameters, and other objective bases makes it challenging to produce even rough-order-of-magnitude cost and schedule models. Often compounding the limited availability of information is the proprietary or protected nature of technology research and development efforts and related intellectual property. Consequently, executives, program managers, budget analysts, and other decision-makers must often rely on historical information from related yet often very dissimilar systems or the subjective opinion or best guess of subject-matter experts. This paper first investigates available industry modeling concepts, frameworks, models, and tools. A representative project data set is identified and selected for cost and schedule modeling, leveraging macro-parameters generally known or available in early technology development stages. Several model forms are then created and evaluated based on key performance criteria.