Causal Relations via Econometrics

Applied econometric work takes a superficial approach to causality. Understanding economic affairs, making good policy decisions, and progress in the economic discipline depend on our ability to infer causal relations from data. We review the dominant approaches to causality in econometrics, and suggest why they fail to give good results. We feel the problem cannot be solved by traditional tools, and requires some out-of-the-box thinking. Potentially promising approaches to solutions are discussed.Causality, Regression, Exogeneity, Hendry Methodology, Natural Experiments

Causal Relations via Econometrics

Applied econometric work takes a superficial approach to causality. Understanding economic affairs, making good policy decisions, and progress in the economic discipline depend on our ability to infer causal relations from data. We review the dominant approaches to causality in econometrics, and suggest why they fail to give good results. We feel the problem cannot be solved by traditional tools, and requires some out-of-the-box thinking. Potentially promising approaches to solutions are discussed.causality, regression, Granger Causality, Exogeneity, Cowles Commission, Hendry Methodology, Natural Experiments

An Alternative Estimation to Spurious Regression Model

In sturdy econometrics specification search problems of unit roots and multicollinearity are well documented since the inception of regression analysis. In examining the likely consequences of nonsense relationship Granger and Newbold (1974) make it clear that first differencing is not the universal sure fire solution to problem of spurious regression models. This has prompted the discovery of cointegration regression estimation by Engle and Granger (1987). In recent years applied econometricians are debating with the problem of spurious regression model when the co movements between the variables are different. If the variables of the model are not cointegrated, there is a question whether the background economic or financial theory is plausible with the data that we are analyzing. This paper reviews the debate and proposes an alternative solution to the problem. Our approach uses a suitable data transformation of the variables of the model based on Hendry (1995) and Phillips (1998) approaches to reduce the spurious correlation, stochastic means and variances in standard level. In a non cointegrated USA information processing investment model, we apply our technique and found a meaningful solution.Spurious Regression, Unit Roots, Cointegration

Distance Estimation in Cosmology

In this paper we outline the framework of mathematical statistics with which one may study the properties of galaxy distance estimators. We describe, within this framework, how one may formulate the problem of distance estimation as a Bayesian inference problem, and highlight the crucial question of how one incorporates prior information in this approach. We contrast the Bayesian approach with the classical `frequentist' treatment of parameter estimation, and illustrate -- with the simple example of estimating the distance to a single galaxy in a redshift survey -- how one can obtain a significantly different result in the two cases. We also examine some examples of a Bayesian treatment of distance estimation -- involving the definition of Malmquist corrections -- which have been applied in recent literature, and discuss the validity of the assumptions on which such treatments have been based.Comment: Plain Latex version 3.1, 18 pages + 2 figures, `Vistas in Astronomy' in pres

Economic forecasting in a changing world

This article explains the basis for a theory of economic forecasting developed over the past decade by the authors. The research has resulted in numerous articles in academic journals, two monographs, Forecasting Economic Time Series, 1998, Cambridge University Press, and Forecasting Nonstationary Economic Time Series, 1999, MIT Press, and three edited volumes, Understanding Economic Forecasts, 2001, MIT Press, A Companion to Economic Forecasting, 2002, Blackwells, and the Oxford Bulletin of Economics and Statistics, 2005. The aim here is to provide an accessible, non-technical, account of the main ideas. The interested reader is referred to the monographs for derivations, simulation evidence, and further empirical illustrations, which in turn reference the original articles and related material, and provide bibliographic perspective

Laudatio on the occasion of the Investure of Professor John Denis Sargan with the Degree of Doctor Honoris Causa of the Universidad Carlos III, 2 de Febrero 1993

Publicad

Arbitrage, market definition and monitoring a time series approach

This article considers the application to regional price data of time series methods to test stationarity, multivariate cointegration and exogeneity. The discovery of stationary price differentials in a bivariate setting implies that the series are rendered stationary by capturing a common trend and we observe through this mechanism long-run arbitrage. This is indicative of a broader market definition and efficiency. The problem is considered in relation to more than 700 weekly data points on gasoline prices for three regions of the US and similarly calibrated simulated series. The discovery of a single common trend is consistent with competitive pricing and a broad market definition, but the finding of a single weakly exogenous variable affects this conclusion