This thesis consists of three chapters. In the first two chapters, I study rebound effects in solar adoption and an energy efficiency program (air-conditioning units (AC) upgrading program). Both solar adoption and AC upgrades reduce households’ energy bills and lower their average electricity prices. Households might adjust their energy consumption behavior which results in the actual reduction in energy use lower than the anticipated reduction. In chapter 1, I use novel data which contain the detailed household-level hourly purchase, sale, and solar generation in the Sacramento area. Contrary to existing literature, results do not show significant rebound effects. Using fixed effects models, I find a statistically insignificant rebound effect of 0.96%, which translates to a 0.0096 kWh increase in solar homes’ total electricity consumption when the solar generation increases by 1 kWh. This effect is also economically negligible. Results from chapter 1 enrich the current literature on solar adoption rebound effects.In contrast to chapter 1, significant rebound effects from the AC Energy Efficiency rebate program in the Sacramento Municipal Utility District (SMUD) serving area are identified in chapter 2. Household-level daily electricity consumption data and daily temperature data in the Sacramento area are utilized in this analysis. Regression mixture models are estimated by an expectation-maximization algorithm to recover premise-level temperature response functions and AC usage behavior functions. These functions are then used to calculate direct savings, total savings, and rebound effects from AC upgrades through a difference-in-difference design. On average, the AC energy efficiency rebate program reduces energy uses in cooling by 347.10 kWh per household in one summer. The rebound effects are estimated at 20.61%. When a household saves 1 kWh in cooling from AC upgrading, its total daily electricity consumption will increase by around 0.21 kWh. The increases in consumption are mainly caused by turning on AC units more often after AC upgrades. In chapter 3, I and coauthors study R&D lag structure in agriculture. Quite diverse models of R&D lag structures have been used by economists studying economic growth com- pared with those estimating returns to investments in industrial R&D or agricultural R&D. In this paper, we and coauthors empirically compare and contrast these alternative models and their implications for R&D knowledge stocks using data on multifactor productivity (MFP) in U.S. agriculture and U.S. public agricultural R&D investments. We employ a model selection procedure based on a combination of time-series properties of data, econo- metric estimation performance, and consistency of estimates with strongly held economic priors. We reject the models used in studies of economic growth and industrial R&D both on prior grounds and using statistical tests. The preferred model is a 50-year gamma lag distribution model similar in shape to Huffman and Evenson’s (1993) trapezoidal lag model. In this gamma lag model the effects of an investment in agricultural R&D on the R&D knowledge stock rise to a peak after 13 years and are mostly dissipated after 35 years. The estimated elasticity of MFP with respect to the knowledge stock is 0.28 and the implied marginal benefit-cost ratio is 23:1.