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M One basic question is, How does one develop a plausible basis for reasoning in situations involving partial—incomplete information? Another basic question relates to how one goes about learning from a sample of data. For the theoretical econometrician, questions tend to be of a nonempirical and hypothetical what-if type: What if a sample of data is described by a particular imag- ined sampling process?
This leads to the question of how one characterizes the sam- pling process in terms of a probability model that properly identifies the stochastic characteristics of the sampling process as well as the data-restricting constraints, the knowns and unknowns of the problem, and the observable and unobservable com- ponents in the model.
Given that the data-sampling process can be described by a probability model that expresses the state of knowledge about possible real-world outcomes, another question then arises relating to how one devises effective esti- mation and hypothesis-testing procedures that will allow the recovery of estimates of the unknowns and provide a basis for making inferences.
The theoretical econo- metrician may, by a process of interpretation, ultimately associate the conceptualized sampling process with a set of observable economic data. For the applied econometrician, the econometric problem begins with a real-world economic question, perhaps involving the implications of scarcity and choice or perhaps the allocative or distributive impacts, resulting from an action or decision.
The next step involves restating the real-world question within a theoretical—conceptual economic model framework in which real-world components are identified to facilitate drawing logic-based conclusions about the question. This step exposes structure and defines the explicit economic model to be used in the empirical analysis of the economic question. Introduction Given a theoretical—conceptual economic playing field, a basis is needed for connect- ing the real-world data outcomes with their counterparts in the economic model.
By visualizing some imagined sampling process by which the outcomes may have evolved and then characterizing this sampling process by a probability model, an economet- ric model is born. This model then acts as a vehicle for expressing knowledge about real-world outcomes and identifies knowns, unknowns, and observed and unobservable model components. If the applied econometrician is fortunate, the resulting economet- ric model may be consistent with a probability model that already exists in the literature and for which a well-defined basis for estimation and inference is already available.
In this case the applied econometrician will use established statistical procedures to address research questions. On the other hand, the econometric model may not be consistent with a commonly specified and evaluated data-sampling process. Conse- quently, the applied econometrician must assume the role of the theoretical econome- trician in first developing effective estimation and hypothesis-testing procedures and then carrying through the estimation and inference stages needed to answer research questions.
As one reads through this chapter and the chapters ahead, it will at times be necessary to assume the roles of both a theoretical and an applied econometrician to derive maximum benefit from the econometric venture.
One goal of the exercises in each chapter is to lead and inspire the reader in this direction. Before going on to consider the question of how to specify a probability—econometric model to provide a basis for learning from a sample of observations, we focus some attention on the real-world component referred to as economic data. The Nature of Economic Data Why do we have books on econometrics?
Why not just have books devoted to statistics for economists? What is it that makes economics unique relative to other fields of science? One thing that tends to make economics and econometrics unique is the nature of economic data and the special characteristics of the sampling processes by which economic data are obtained.
In providing an answer to the opening questions of this section, William Barnett, in private correspondence, points us to a classic article by Schumpter in Econometrica, Vol.
Every economist is an econometrician whether he wants to be or not. Indeed, the very act of transacting in markets depends explicitly upon the numerical values of such variables as quantities and prices. But in physics, for example, the physical world can and will operate without dependence upon numerical measurement of variables. So in this sense, as Schumpter observed, economics is inherently more quantitative than any other scientific field.
Economies and markets carry out experiments and produce numerical data through the very nature of their operation. However, they do so in a manner that is not usually in accordance with statistically designed experiments. Because the economy is not a sta- tistically designed experiment, economists must in many cases utilize ill-conditioned data. This is a principal reason why econometrics requires special tools for probability model formulation, estimation, and inference and why econometrics is characterized as an experiment in nonexperimental model building.
The Probability Approach to Economics The probability theory that you encountered in your courses in theoretical statistics and that is reviewed in an electronic document on the CD that accompanies this text has important implications for how one should organize, incorporate, and utilize data and prior information in quantitative economic analyses.
In economic problems char- acterized by incomplete knowledge and uncertainty, this theory, through a process of abstraction and interpretation by analysts, defines a reasoning process for expressing our knowledge about real-world outcomes, for recovering information from data, and for assessing its validity. The calculus of reasoning defined by probability theory facili- tates learning and problem resolution and defines a logical basis for evaluating decisions and making choices.
The participants or play- ers in a postulated economic system are presumed to define economic processes that result in measurable outcomes and, by a process of interpretation on the part of the econometrician, these outcomes are viewed in probabilistic form.
In most econometric problems, at least a portion of the information available for analysis will be in the form of a sample of data that has been generated as an outcome of some real-world economic process. In addition, the analyst generally has some prior knowledge about the relevant economic processes and institutions that may have conditioned the sample outcomes. Judge, Douglas J. Consultez aussi Tous les livres de Ron C.
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