Contrary to the belief of most econometrics instructors, upon completion of introductory statistics courses the vast majority of students do not understand the basic logic of classical statistics as captured in the sampling-distribution concept. They have learned to do mechanical things such as compute a sample variance, run a regression, and test an hypothesis, and they know they can pass the course by remembering how these techniques work. They view statistics as a branch of mathematics because it uses mathematical formulas, so they look at statistics through a mathematical lens. What they are missing is the statistical lens through which to view this world, allowing this world to make sense. The sampling-distribution concept is this statistical lens. My own experience discovering this lens was a revelation, akin to the experience I had when I put on my first pair of eyeglasses: suddenly everything was sharp and clear. In this paper I discuss this issue and suggest a means whereby instructors of undergraduate econometrics courses (i.e., following introductory statistics) can provide the missing statistical lens.

Eight Reasons Why Real versus Nominal Interest Rates Is the Most Important Concept in Macroeconomics Principles Courses by Peter E. Kennedy. Published in volume 90, issue 2, pages 81-84 of American Economic Review, May 2000

The norming of the third edition of the Test of Understanding College Economics (TUCE III) has produced a valuable data set (Phillip Saunders, 1994). These data describe 93 introductory macro and 96 introductory microeconomics classes taught by 131 different instructors at 53 U.S. colleges and universities in 1989-1990. It is tempting to use this sample to investigate how class size influences learning. The nonexperimental nature of the data, however, raises the possibility of an endogeneity problem: department chairs may assign better teachers to larger classes, and those teachers, in turn, may attract even greater numbers of students. Teaching quality, therefore, may be higher in larger classes. Thus, the discovery from these data of any deleterious effect on learning from larger classes may be only a lower bound. The education literature, admirably surveyed by Wilbert J. McKeachie (1990), suggests that learning is not much affected by class size. One reason for this result may be that instructors do not adjust their teaching methods to class size. In this paper we use the TUCE III data to examine whether introductory economics instructors vary pedagogy with class size. I. The Role of Instructor Behavior

In this paper decision systems are structured so that topological concepts can be applied to formulate and help solve existence problems. Existence of stationary states and orbits is established. The analysis is then applied to programs, a special class of decision systems in which the decision operator is a mathematical program. The theory is extended to of many decision makers with rolling schedules of future actions. RECURSIVE DECISION SYSTEMS (RDS's) are dynamic systems based on discrete time that represent the positive behavior of decision makers. They have been put to three basic uses, (1) to describe the behavior of various economic sectors, (2) to show how indirect policies can in some particular way improve the performance of the economic system under investigation, and (3) to formulate and analyze a variety of dynamic economic theories. In this paper we define RDS's so that topological concepts and theorems can be used to study existence questions. Existence theorems are then given for stationary states and compact orbit sets. Special attention is given to the class of RDS's called programs (RP) of which various recursive program- ming models are special cases. The paper concludes with some brief comments on the assumptions used in the analysis. Before proceeding to the formal definitions, we briefly review in a nontechnical manner the basic concepts underlying RDS's and their use in economic research. RDS's as defined here are mathematical of socioeconomic processes having two basic components: (1) a decision operator that describes the manner in which final decisions or actions are derived from a given amount of information about the decision maker's environment ;2 and (2) afeedback operator that describes how decisions once acted on, or once scheduled for the future, interact with the decision maker's environment to produce new information upon which succeeding plans can be based.3 A given decision operator may represent the decision process not only of a single decision maker, but also of a group of decision makers who make their decisions independently-or collusively-during the same time period. Further- more, the decision at a given time may represent not only an immediate choice,