Introduction to Measurement with Theory

This working paper is the first draft of an overview and commentary on the papers to appear in a Macroeconomic Dynamics Special Issue on Measurement with Theory. The included papers in the special issue are part of a larger initiative to promote "measurement with theory" in economics and planned to appear as special issues of other journals. A later revised draft of this initial commentary is planned to appear as the introduction to the Macroeconomic Dynamics special issue, expected to appear in 2010.Measurement; index number theory; aggregation theory.

Introduction to Measurement with Theory

This is the author's final draft of an article for which the publisher's official version is available electronically from doi:10.1017/S1365100509090233This paper is the introduction to the Macroeconomic Dynamics Special Issue on Measurement with Theory. The Guest Editors of the special issue are William A. Barnett, W. Erwin Diewert, Shigeru Iwata, and Arnold Zellner. The papers included are part of a larger initiative to promote measurement with theory in economics

Bayesian specification analysis and estimation of simultaneous equation models using Monte Carlo methods

Bayesian procedures for specification analysis or diagnostic checking of modeling assumptions for structural equations of econometric models are developed and applied using Monte Carlo numerical methods. Checks on the validity of identifying restrictions, exogeneity assumptions and other specifying assumptions are performed using posterior distributions for discrepancy vectors and functions representing departures from specifying assumptions. Several mappings or functions of reduced form coefficients are defined and their posterior distributions are computed. A restricted reduced form approach is used to compute posterior distributions for structural parameters. These procedures are applied in analyses of two econometric models

Introduction to Measurement with Theory

This working paper is the first draft of an overview and commentary on the papers to appear in a Macroeconomic Dynamics Special Issue on Measurement with Theory. The included papers in the special issue are part of a larger initiative to promote "measurement with theory" in economics and planned to appear as special issues of other journals. A later revised draft of this initial commentary is planned to appear as the introduction to the Macroeconomic Dynamics special issue, expected to appear in 2010

Bayesian Analysis of Instrumental Variable Models: Acceptance-Rejection within Direct Monte Carlo

We discuss Bayesian inferential procedures within the family of instrumental variables regression models and focus on two issues: existence conditions for posterior moments of the parameters of interest under a flat prior and the potential of Direct Monte Carlo (DMC) approaches for efficient evaluation of such possibly highly non-elliptical posteriors. We show that, for the general case of m endogenous variables under a flat prior, posterior moments of order r exist for the coefficients reflecting the endogenous regressors' effect on the dependent variable, if the number of instruments is greater than m +r, even though there is an issue of local non-identification that causes non-elliptical shapes of the posterior. This stresses the need for efficient Monte Carlo integration methods. We introduce an extension of DMC that incorporates an acceptance-rejection sampling step within DMC. This Acceptance-Rejection within Direct Monte Carlo (ARDMC) method has the attractive property that the generated random drawings are independent, which greatly helps the fast convergence of simulation results, and which facilitates the evaluation of the numerical accuracy. The speed of ARDMC can be easily further improved by making use of parallelized computation using multiple core machines or computer clusters. We note that ARDMC is an analogue to the well-known "Metropolis-Hastings within Gibbs" sampling in the sense that one 'more difficult' step is used within an 'easier' simulation method. We compare the ARDMC approach with the Gibbs sampler using simulated data and two empirical data sets, involving the settler mortality instrument of Acemoglu et al. (2001) and father's education's instrument used by Hoogerheide et al. (2012a). Even without making use of parallelized computation, an efficiency gain is observed both under strong and weak instruments, where the gain can be enormous in the latter case

Science and Ideology in Economic, Political, and Social Thought

This paper has two sources: One is my own research in three broad areas: business cycles, economic measurement and social choice. In all of these fields I attempted to apply the basic precepts of the scientific method as it is understood in the natural sciences. I found that my effort at using natural science methods in economics was met with little understanding and often considerable hostility. I found economics to be driven less by common sense and empirical evidence, then by various ideologies that exhibited either a political or a methodological bias, or both. This brings me to the second source: Several books have appeared recently that describe in historical terms the ideological forces that have shaped either the direct areas in which I worked, or a broader background. These books taught me that the ideological forces in the social sciences are even stronger than I imagined on the basis of my own experiences. The scientific method is the antipode to ideology. I feel that the scientific work that I have done on specific, long standing and fundamental problems in economics and political science have given me additional insights into the destructive role of ideology beyond the history of thought orientation of the works I will be discussing