Simplicity, scientific inference and econometric modelling

Economic Schools;Econometric Models;Economic Methodology

Bayesian and Non-Bayesian Approaches to Scientific Modeling and Inference in Economics and Econometrics

After brief remarks on the history of modeling and inference techniques in economics and econometrics , attention is focused on the emergence of economic science in the 20th century. First, the broad objectives of science and the Pearson-Jeffreys' "unity of science" principle will be reviewed. Second, key Bayesian and non-Bayesian practical scientific inference and decision methods will be compared using applied examples from economics, econometrics and business. Third, issues and controversies on how to model the behavior of economic units and systems will be reviewed and the structural econometric modeling, time series analysis (SEMTSA) approach will be described and illustrated using a macro-economic modeling and forecasting problem involving analyses of data for 18 industrialized countries over the years since the 1950s. Point and turning point forecasting results will be summarized. Last, a few remarks will be made about the future of scientific inference and modeling techniques in economics and econometrics.

Why There Can\u27t be a Logic of Induction

Carap\u27s attempt to develop an inductive logic has been criticized on a variety of grounds, and while there may be some philosophers who believe that difficulties with Carnap\u27s approach can be overcome by further elaborations and modifications of his system, I think it is fair to say that the consensus is that the approach as a whole cannot succeed. In writing a paper on problems with inductive logic (and with Carnap\u27s approach in particular), I might therefore be accused of beating a dead horse. However, there are still some (e.g., Spirtes, Glymour and Scheines 1993) who seem to believe that purely formal methods for scientific inference can be developed. It may still then be useful to perform an autopsy on a dead horse when establishing the cause of death can shed light on issues of current concern. My intention in this paper is to point out a problem in Carnap\u27s inductive logic which has not been clearly articulated, and which applies generally to any inductive logic. My conclusion will be that scientific inference is inevitably and ineliminably guided by background beliefs and that different background beliefs lead to the application of different inductive rules and different standards of evidentiary relevance. At the end of this paper I will discuss the relationship between this conclusion and the problem of justifying induction

Cartesian Certainty, Realism and Scientific Inference

In the Principles, Descartes explains several observable phenomena showing that they are caused by special arrangements of unobservable microparticles. Despite these microparticles being unobservable, many passages suggest that he was very confident that these explanations were correct. In other passages, however, Descartes points out that these explanations merely hold the status of ‘suppositions’ or ‘conjectures’ that could be wrong. The aim of this chapter is to clarify this apparent conflict. I argue that the possibility of natural explanations being wrong should be understood as these explanations not being absolutely certain, but as being morally certain. Cartesian explanations rely on what Ernan McMullin calls retroduction, which is a mode of inference that justifies beliefs in concrete unobservable entities and processes. I use as a foil the debate in contemporary philosophy of science between scientific realism and instrumentalism, and argue that for Descartes we could indeed have knowledge of the unobservable world. In that sense, he was closer to being a scientific realist

Ressenyes

Obra resseyada: Gary KING; Robert KEOHANE; Sidney VERBA, Designing Social Inquiry: Scientific Inference in Qualitative Research. Princeton: Princeton University Press, 1994

Probability as a physical motive

Recent theoretical progress in nonequilibrium thermodynamics, linking the physical principle of Maximum Entropy Production ("MEP") to the information-theoretical "MaxEnt" principle of scientific inference, together with conjectures from theoretical physics that there may be no fundamental causal laws but only probabilities for physical processes, and from evolutionary theory that biological systems expand "the adjacent possible" as rapidly as possible, all lend credence to the proposition that probability should be recognized as a fundamental physical motive. It is further proposed that spatial order and temporal order are two aspects of the same thing, and that this is the essence of the second law of thermodynamics.Comment: Replaced at the request of the publisher. Minor corrections to references and to Equation 1 added

To P or not to P: on the evidential nature of P-values and their place in scientific inference

The customary use of P-values in scientific research has been attacked as being ill-conceived, and the utility of P-values has been derided. This paper reviews common misconceptions about P-values and their alleged deficits as indices of experimental evidence and, using an empirical exploration of the properties of P-values, documents the intimate relationship between P-values and likelihood functions. It is shown that P-values quantify experimental evidence not by their numerical value, but through the likelihood functions that they index. Many arguments against the utility of P-values are refuted and the conclusion is drawn that P-values are useful indices of experimental evidence. The widespread use of P-values in scientific research is well justified by the actual properties of P-values, but those properties need to be more widely understood.Comment: 31 pages, 9 figures and R cod