"Money, as a physical medium of exchange, made a diversified civilization possible,. .. And yet it is money, in its mechanical more than in its spiritual effects, which may well, having brought us to the present level, actually destroy society." PREFACE This book grew out of an invitation to speak on the Depression of 1929-32 before the American Association for the Advancement of Science and is an elaboration of my address at the meeting of the Association, held at New Orleans, Jan. 1, 1932. The vast field of "business cycles" is one on which I had scarcely ever entered before, and I had never attempted to analyze it as a whole. The scope of the present work is restricted, for the most part, to the role of nine main factors, not because they cover the whole subject, but because they include what seem to me to be the outstanding influences in the present, as well as in most, if not all, other major depressions. By this restriction it has been possible to make the book much shorter and, I hope, much more intelligible to the lay reader than if it set out to be an exhaustive treatise on an inexhaustible subject. At any rate, the nine factors are so inherently and obviously related to each other that we are not compelled to resort entirely to empirical correlation. Empirical studies are important and essential in this field 5 but, by excluding those which apparently have no rational basis, it is possible to mark out a clear cut set of "first principles*" The results of the analysis here presented seem largely new. But, being so unfamiliar with the immense literature, I decided to submit the first draft of this book in mimeo-graphed form to a number of authorities, several of whom vii Digitized for FRASER viii PREFACE had given much of their lives to the study of the so-called business cycle. With few exceptions these have found in the theory much that they regard as both new and true. Yet, as I could not, without years of searching, be sure how far any or all of what to me seems new may have been anticipated by previous writers, I leave to others to determine how far this book is the original contribution which it is intended to be. As will be seen, the main conclusion of this book is that depressions are, …

The Problem of Business Forecasting.

Booms and depressions

To reconcile forecast failure with building congruent empirical models, we analyze the sources of mis-prediction. This reveals that ex ante forecast failure is purely a function of forecast-period events, not determinable from in-sample information. The primary causes are unmodelled shifts in deterministic factors, rather than model mis-specification, collinearity, or a lack of parsimony. We examine the effects of deterministic breaks on equilibrium-correction mechanisms, and consider the role of causal variables. Throughout, Monte Carlo simulation and empirical models illustrate the analysis, and support a progressive research strategy based on learning from past failures.

background

/pdf/the-implications-for-econometric-modelling-of-forecast-4js70qfsnj.pdf

The Implications for Econometric Modelling of Forecast Failure

When an econometric model coincides with the mechanism generating the data in an unchanging world, the theory of economic forecasting is reasonably well developed. However, less is known about forecasting when model and mechanism differ in a non-stationary and changing world. The paper addresses the basic concepts; the invariance of forecast accuracy measures to isomorphic model representations; the roles of causal information, parsimony and collinearity; a reformulated taxonomy of forecast errors; differencing and intercept corrections to robustify forecasts against biases due to shifts in deterministic factors; the removal of structural breaks by co-breaking; and forecasting using leading indicators.

/pdf/the-econometrics-of-macroeconomic-forecasting-2u7wnh3lb3.pdf

The Econometrics of Macroeconomic Forecasting

methods

Forecasting by factors, by variables, by both or neither?

Parameter constancy is a fundamental requirement for empirical models to be useful for forecasting, analysing economic policy, or testing economic theories. However, there are surprises in defining a constant-parameter model, such that models with time-varying coeff­ icients, and expansion of the parameterisation over time are both compatible with constancy, yet unbiased forecasts may not entail a sensible model choice. In-sample tests cannot determine likely post-sample predictive failure. A comparison of two models of UK money demand illus­ trates the analysis empirically, as one suffers considerable predictive failure yet the other does not, despite being identical in-sample.

/pdf/on-the-constancy-of-time-series-econometric-equations-3zaan6v1u3.pdf

The Problem of Business Forecasting.

Citations

Booms and depressions

The Implications for Econometric Modelling of Forecast Failure

The Econometrics of Macroeconomic Forecasting

Forecasting by factors, by variables, by both or neither?

On the constancy of time-series econometric equations

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