Nonparametric and semiparametric modeling and estimation procedures are now widely applied in econometrics. Their popularity generally comes from the reduction of the probability of misspecification compared with their parametric counterpart. My research is composed of two parts: a theoretical part on semiparametric efficient estimation of partially linear model and an applied part in energy economics under different dynamic settings. The chapters are related in terms of their applications as well as the way in which models are constructed and estimated. In the second chapter, estimation of the partially linear model is studied under different stochastic restrictions of the residual term. We work out the efficient score functions and efficiency bounds under four stochastic assumptions — partially uncorrelated, independence, conditional symmetry, and conditional zero mean. A feasible efficient estimation method for the linear part of the model is also developed based on the efficient score function associated with each parametric submodel. A battery of specification test that allows for choosing between the alternative assumptions is provided. A Monte Carlo simulation is also conducted to contrast and compare the finite sample properties of the efficient estimator with those of Robinson’s estimator.
The third chapter presents a dynamic optimization model for a stylized oilfield resembling the largest developed light oil field in Saudi Arabia, Ghawar. We use data from different sources to estimate the oil production cost function and the revenue function that constitute part of our dynamic programming model. We pay particular attention to the dynamic aspect of oil production by employing a petroleum engineering software to simulate the interaction between production control variables and reservoir state variables. A nonparametric smoothing technique — tensor spline — is also employed to approximate the value function. Optimal solutions are studied under different scenarios to account for the possible changes in the exogenous variables and the uncertainty about the forecasts. The model is based on the profit maximization hypothesis. While Saudi oil policy is likely to reflect many political and strategic motives, our analysis is nevertheless instructive in that it enables one to quantify cost of pursuing these non-economic objectives.
The fourth chapter examines the effect of oil price volatility on the level of innovation displayed by the U.S. economy. A measure of innovation is calculated by decomposing an output-based Malmquist productivity index，which is a nonparametric index. We also construct a nonparametric measure for oil price volatility. Technical change and oil price volatility are then placed in a VAR framework with oil price and a variable indicative of monetary policy. The system is estimated and analyzed for significant relationships. We find that oil price volatility displays a significant negative effect on innovation. A key point of this analysis lies in the fact that we impose no functional forms for production technologies and the methods we employ keep technical assumptions to a minimum.
The fifth chapter contrasts these two alternatives in terms of cost effectiveness and social welfare in a nonparametric game theoretic framework, taking into account that electricity markets are not perfectly competitive. The search for economically efficient policy instruments designed to promote the diffusion of renewable energy technologies in liberalized markets has led to the introduction of quota-based tradable “green” certificate (TGC) schemes for renewable power. However, there is a debate about the pros and cons of TGC, a quantity control policy, compared to guaranteed feed-in tariffs (FIT), a price control policy. We find out that a mix of the two may prove to be more efficient, a possibility deserving further investigation.
作者简介：
章节目录：
1. Introduction 1.1. Parametric and Nonparametric Modeling and Estimation 1 1.2. Book Structure 7
2. Semiparametric Efficient Estimation of Partially Linear Model 2.1. Introduction 9 2.2. Model and Previous Results 13 2.3. Semiparametric Efficiency Bounds 17 2.4. Feasible and Efficient Estimation 28 2.5. Specification Test 41 2.6. Sampling Results 43 2.7. Conclusion 52
3. Optimal Dynamic Production Policy: The Case of a Large Oil Field in Saudi Arabia 3.1. Introduction 54 3.2. Dynamic Modeling of Oil Production Decisions 59 3.3. Data and Estimations 64 3.4. Theoretical Issues 82 3.5. Simulation Results 86 3.6. Conclusion 99
4. The Effects of Oil Price Volatility on Technical Change 4.1. Introduction 103 4.2. Channels of Transmission and Oil Price Volatility 106 4.3. A Measure of Volatility 112 4.4. A Measure of Innovation (Technical Change) 116 4.5. Vector Autoregression 124 4.6. Conclusion 135
5. Promoting Renewable Electricity Generation in Imperfect Markets: Price vs. Quantity Control 5.1. Introduction 138 5.2. Promoting Renewable Electricity in a Perfectly Competitive Market 141 5.3. Duopoly Market and Quasi-symmetric Costs 147 5.4. Welfare Comparison Between Subsidy and Quota-based Policies 162 5.5. Policy Implications 165 5.6. Conclusion 167
Appendix 169
References 185
精彩片段：
In this section, I would like to address, in general, the motivation for parametric and nonparametric (and semiparametric) modeling and estimation and issues regarding the applications. The specific motivation and background for each individual research project is left to the introduction of each of the respective chapters. The basic incentive for nonparametric models comes from the price that we might have to pay for using pure parametric models, i.e., the possible misspecification that could lead to highly biased results in estimation and prediction in terms of both finite sample and asymptotic properties. The nonparametric method, hence, provides an alternative estimation procedure in the absence of strong a priori restriction.
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