Dissertations / Theses on the topic 'Welfare economics Econometric models'

In the second chapter, I analyze the effect of ambiguity on self-insurance and self-protection, that are tools used to deal with the uncertainty of facing a monetary loss when market insurance is not available (in the self-insurance model, the decision maker has the opportunity to furnish an effort to reduce the size of the loss occurring in the bad state of the world, while in the self-protection – or prevention – model, the effort reduces the probability of being in the bad state).

In a short note, in the context of a two-period model I first examine the links between risk-aversion, prudence and self-insurance/self-protection activities under risk. Contrary to the results obtained in the static one-period model, I show that the impacts of prudence and of risk-aversion go in the same direction and generate a higher level of prevention in the more usual situations. I also show that the results concerning self-insurance in a single period framework may be easily extended to a two-period context.

I then consider two-period self-insurance and self-protection models in the presence of ambiguity and analyze the effect of ambiguity aversion. I show that in most common situations, ambiguity prudence is a sufficient condition to observe an increase in the level of effort. I propose an interpretation of the model in the context of climate change, so that self-insurance and self-protection are respectively seen as adaptation and mitigation efforts a policy-maker should provide to deal with an uncertain catastrophic event, and interpret the results obtained as an expression of the Precautionary Principle.

In the third chapter, I introduce the economic theory developed to deal with ambiguity in the context of medical decision-making. I show that, under diagnostic uncertainty, an increase in ambiguity aversion always leads a physician whose goal is to act in the best interest of his patient, to choose a higher level of treatment. In the context of a dichotomic choice (treatment versus no treatment), this result implies that taking into account the attitude agents generally manifest towards ambiguity may induce a physician to change his decision by opting for treatment more often. I further show that under therapeutic uncertainty, the opposite happens, i.e. an ambiguity averse physician may eventually choose not to treat a patient who would have been treated under ambiguity neutrality.

Doctorat en Sciences économiques et de gestion
info:eu-repo/semantics/nonPublished

In the widely used over-identified econometric model, the two-step Generalized Methods of Moments (GMM) estimator and inference, first suggested by Hansen (1982), require the estimation of optimal weighting matrix at the initial stages. For time series data and clustered dependent data, which is our focus here, the optimal weighting matrix is usually referred to as the long run variance (LRV) of the (scaled) sample moment conditions. To maintain generality and avoid misspecification, nowadays we do not model serial dependence and within-cluster dependence parametrically but use the heteroscedasticity and autocorrelation robust (HAR) variance estimator in standard practice. These estimators are nonparametric in nature with high variation in finite samples, but the conventional increasing smoothing asymptotics, so called small-bandwidth asymptotics, completely ignores the finite sample variation of the estimated GMM weighting matrix. As a consequence, empirical researchers are often in danger of making unreliable inferences and false assessments of the (efficient) two-step GMM methods. Motivated by this issue, my dissertation consists of three papers which explore the efficiency and approximation issues in the two-step GMM methods by developing new, more accurate, and easy-to-use approximations to the GMM weighting matrix.

The first chapter, "Simple and Trustworthy Cluster-Robust GMM Inference" explores new asymptotic theory for two-step GMM estimation and inference in the presence of clustered dependence. Clustering is a common phenomenon for many cross-sectional and panel data sets in applied economics, where individuals in the same cluster will be interdependent while those from different clusters are more likely to be independent. The core of new approximation scheme here is that we treat the number of clusters G fixed as the sample size increases. Under the new fixed-G asymptotics, the centered two-step GMM estimator and two continuously-updating estimators have the same asymptotic mixed normal distribution. Also, the t statistic, J statistic, as well as the trinity of two-step GMM statistics (QLR, LM and Wald) are all asymptotically pivotal, and each can be modified to have an asymptotic standard F distribution or t distribution. We also suggest a finite sample variance correction further to improve the accuracy of the F or t approximation. Our proposed asymptotic F and t tests are very appealing to practitioners, as test statistics are simple modifications of the usual test statistics, and the F or t critical values are readily available from standard statistical tables. We also apply our methods to an empirical study on the causal effect of access to domestic and international markets on household consumption in rural China.

The second paper "Should we go one step further? An Accurate Comparison of One-step and Two-step procedures in a Generalized Method of Moments Framework” (coauthored with Yixiao Sun) focuses on GMM procedure in time-series setting and provides an accurate comparison of one-step and two-step GMM procedures in a fixed-smoothing asymptotics framework. The theory developed in this paper shows that the two-step procedure outperforms the one-step method only when the benefit of using the optimal weighting matrix outweighs the cost of estimating it. We also provide clear guidance on how to choose a more efficient (or powerful) GMM estimator (or test) in practice.

While our fixed smoothing asymptotic theory accurately describes sampling distribution of two-step GMM test statistic, the limiting distribution of conventional GMM statistics is non-standard, and its critical values need to be simulated or approximated by standard distributions in practice. In the last chapter, "Asymptotic F and t Tests in an Efficient GMM Setting" (coauthored with Yixiao Sun), we propose a simple and easy-to-implement modification to the trinity (QLM, LM, and Wald) of two-step GMM statistics and show that the modified test statistics are all asymptotically F distributed under the fixed-smoothing asymptotics. The modification is multiplicative and only involves the J statistic for testing over-identifying restrictions. In fact, what we propose can be regarded as the multiplicative variance correction for two-step GMM statistics that takes into account the additional asymptotic variance term under the fixed-smoothing asymptotics. The results in this paper can be immediately generalized to the GMM setting in the presence of clustered dependence.

Regarding the approach pursued to tackle these problems, we have chosen to strictly remain within the boundaries of empirical (macro)economics - that is, applied econometrics. Though we systematically provide theoretical models to back up our empirical approach, our only real concern is to look at the stories the data can (or cannot) tell us. As to the econometric methodology, we will restrict ourselves to the use of panel data analysis. The large spectrum of techniques available within the panel framework allows us to utilize, for each of the problems at hand, the most suitable approach (or what we think it is).
Doctorat en sciences économiques, Orientation économie
info:eu-repo/semantics/nonPublished

Accurate estimation of alcohol price elasticity is important for policy analysis – e.g.., determining optimal taxes and projecting revenues generated from proposed tax changes. Several approaches to specifying and estimating the price elasticity of demand for alcohol can be found in the literature. There are two keys to policy-relevant specification and estimation of alcohol price elasticity. First, the underlying demand model should take account of alcohol consumption decisions at the extensive margin – i.e., individuals’ decisions to drink or not – because the price of alcohol may impact the drinking initiation decision and one’s decision to drink is likely to be structurally different from how much they drink if they decide to do so (the intensive margin). Secondly, the modeling of alcohol demand elasticity should yield both theoretical and empirical results that are causally interpretable. The elasticity estimates obtained from the existing two-part model takes into account the extensive margin, but are not causally interpretable.

The elasticity estimates obtained using aggregate-level models, however, are causally interpretable, but do not explicitly take into account the extensive margin. There currently exists no specification and estimation method for alcohol price elasticity that both accommodates the extensive margin and is causally interpretable. I explore additional sources of bias in the extant approaches to elasticity specification and estimation: 1) the use of logged (vs. nominal) alcohol prices; and 2) implementation of unnecessarily restrictive assumptions underlying the conventional two-part model. I propose a new approach to elasticity specification and estimation that covers the two key requirements for policy relevance and remedies all such biases. I find evidence of substantial divergence between the new and extant methods using both simulated and the real data. Such differences are profound when placed in the context of alcohol tax revenue generation.