Causality And Objectivity In Macroeconomics

"Central banks and other policymaking institutions use causal hypotheses to justify macroeconomic policy decisions to the public and public institutions. These hypotheses say that changes in one macroeconomic aggregate (e.g. aggregate demand) cause changes in other macroeconomic aggregates (e.g. in inflation). An important (perhaps the most important) goal of macroeconomists is to provide conclusive evidence in support of these hypotheses. If they cannot provide any conclusive evidence, then policymaking institutions will be unable to use causal hypotheses to justify policy decisions, and then the scientific objectivity of macroeconomic policy analysis will be questionable. The book analyzes the accounts of causality that have been or can be proposed to capture the type of causality that underlies macroeconomic policy analysis, the empirical methods of causal inference that contemporary macroeconomists have at their disposal, and the conceptions of scientific objectivity that traditionally play a role in economics. The book argues that contemporary macroeconomists cannot provide any conclusive evidence in support of causal hypotheses, and that macroeconomic policy analysis doesn't qualify as scientifically objective in any of the traditional meanings. The book also considers a number of steps that might have to be taken in order for macroeconomic policy analysis to become more objective. The book addresses philosophers of science and economics as well as (macro-) economists, econometricians and statisticians who are interested in causality and macro-econometric methods of causal inference and their wider philosophical and social context"--

Central banks and other policymaking institutions use causal hypotheses to justify macroeconomic policy decisions to the public and public institutions. These hypotheses say that changes in one macroeconomic aggregate (e.g. aggregate demand) cause changes in other macroeconomic aggregates (e.g. in inflation). An important (perhaps the most important) goal of macroeconomists is to provide conclusive evidence in support of these hypotheses. If they cannot provide any conclusive evidence, then policymaking institutions will be unable to use causal hypotheses to justify policy decisions, and then the scientific objectivity of macroeconomic policy analysis will be questionable. The book analyzes the accounts of causality that have been or can be proposed to capture the type of causality that underlies macroeconomic policy analysis, the empirical methods of causal inference that contemporary macroeconomists have at their disposal, and the conceptions of scientific objectivity that traditionally play a role in economics. The book argues that contemporary macroeconomists cannot provide any conclusive evidence in support of causal hypotheses, and that macroeconomic policy analysis doesn’t qualify as scientifically objective in any of the traditional meanings. The book also considers a number of steps that might have to be taken in order for macroeconomic policy analysis to become more objective. The book addresses philosophers of science and economics as well as (macro-) economists, econometricians and statisticians who are interested in causality and macro-econometric methods of causal inference and their wider philosophical and social context.

First published in 2001, Causality in Macroeconomics examines causality while taking macroeconomics seriously.

Case Study from the year 2016 in the subject Economics - Monetary theory and policy, , language: English, abstract: Since the main objective of the paper is to test the existence of causality relationship between the three macroeconomic variables, namely real GDP, price level (CPI) and M2 money supply (MS), analysis has been made there by employing 40 years of data (data from 1975-2014). VAR Granger causality test has been made to verify the objective of the paper. The VAR Granger causality test result suggesting the existence of strong and significant correlation between the three variable s pairwise. The direction of causation is found to be a uni- directional causation between money supply and inflation, real GDP and Money supply and between real GDP and inflation and the causation runs from money supply to inflation, real GDP to Money supply and real GDP to inflation respectively. From the causation we observed that money supply has relationship with level of price and economic growth (real GDP). Basically targeting monetary expansion has a multiple role to boost economic growth and control the level of inflation.

The most fundamental questions of economics are often philosophical in nature, and philosophers have, since the very beginning of Western philosophy, asked many questions that current observers would identify as economic. The Routledge Handbook of Philosophy of Economics is an outstanding reference source for the key topics, problems, and debates at the intersection of philosophical and economic inquiry. It captures this field of countless exciting interconnections, affinities, and opportunities for cross-fertilization. Comprising 35 chapters by a diverse team of contributors from all over the globe, the Handbook is divided into eight sections: I. Rationality II. Cooperation and Interaction III. Methodology IV. Values V. Causality and Explanation VI. Experimentation and Simulation VII. Evidence VIII. Policy The volume is essential reading for students and researchers in economics and philosophy who are interested in exploring the interconnections between the two disciplines. It is also a valuable resource for those in related fields like political science, sociology, and the humanities.

Haavelmo’s 1944 monograph, The Probability Approach in Econometrics, is widely acclaimed as the manifesto of econometrics. This book challenges Haavelmo’s probability approach, shows how its use is delivering defective and inefficient results, and argues for a paradigm shift in econometrics towards a full embrace of machine learning, with its attendant benefits. Machine learning has only come into existence over recent decades, whereas the universally accepted and current form of econometrics has developed over the past century. A comparison between the two is, however, striking. The practical achievements of machine learning significantly outshine those of econometrics, confirming the presence of widespread inefficiencies in current econometric research. The relative efficiency of machine learning is based on its theoretical foundation, and particularly on the notion of Probably Approximately Correct (PAC) learning. Careful examination reveals that PAC learning theory delivers the goals of applied economic modelling research far better than Haavelmo’s probability approach. Econometrics should therefore renounce its outdated foundation, and rebuild itself upon PAC learning theory so as to unleash its pent-up research potential. The book is catered for applied economists, econometricians, economists specialising in the history and methodology of economics, advanced students, philosophers of social sciences.

‘Situational Analysis’ represents Karl Popper’s methodological proposal for the social sciences. Although it has been widely studied and interpreted, this book argues that Situational Analysis remains underestimated and undeveloped and, if properly reformulated, could yet become a broad methodological framework that encompasses both the ‘historical’ social sciences and a large section of the ‘theoretical’ social sciences. The first part of this book develops this idea by returning to Popper’s texts. This book calls for both a reformulation of Situational Analysis and a clarification of the status and role of Popper’s ‘Rationality Principle’. This book also develops Situational Analysis by addressing two aspects of the Problem-Situation facing individual actors that are only sketched out in Popper’s work: institutions and expectations. The second part of this book utilizes the reformulated version of Situational Analysis to shed light on the core methodological differences across some of the most widespread approaches in contemporary economics and sociology. In particular, by showing that many of these approaches are variants of the reformulated version of Situational Analysis presented in the first part of this book, this book argues that apparent methodological differences across these approaches may be reinterpreted as differences in the way social scientists reconstruct the Problem-Situation facing individual actors. This book will be vital reading for academic economists, sociologists, philosophers of science, and other social scientists interested in methodology.