Identification and Inference for Econometric Models edited.
IDENTIFICATION IN PARAMETRIC MODELS BY THOMAS J. ROTHENBERG' A theory of identification is developed for a general stochastic model whose probability law is determined by a finite number of parameters. It is shown under weak regularity con- ditions that local identifiability of the unknown parameter vector is equivalent to non-.
This thesis contains three essays on inference in econometric models. Chapter 1 considers the question of bootstrap inference for Propensity Score Matching. Propensity Score Matching, where the propensity scores are estimated in a first step, is widely used for estimating treatment effects. In this context, the naive bootstrap is invalid (Abadie and Imbens, 2008).
Econometric Models: A model is a simplified representation of a real-world process. It should be representative in the sense that it should contain the salient features of the phenomena under study. In general, one of the objectives in modeling is to have a simple model to explain a complex phenomenon. Such an objective may sometimes lead to oversimplified model and sometimes the assumptions.
Econometrics is the study of estimation and inference for economic models using economic data. Econometric theory concerns the study and development of tools and methods for applied econo-metric applications. Applied econometrics concerns the application of these tools to economic data. 1.1 Economic Data Aneconometric studyrequires datafor.
This thesis is divided in three essays. The first essay examines the reactions by incumbent airhnes to the threat and actual entry of the low-cost carrier Gol in the Brazilian domestic air transport market. By estimating the reactions in prices, quantities and supply variables, it investigates the plausibility of theories of entry deterrence and accommodation.
Identification in econometric models maps prior assumptions and the data to information about a parameter of interest. The partial identification approach to inference recognizes that this process should not result in a binary answer that consists of whether the parameter is point identified. Rather, given the data, the partial identification approach characterizes the informational content of.
The identification problem is very general as it (1) arises in many contexts, and (2) it may refer to generic identification (in that certain conditions have to be checked), to empirical.