This study compares the out-of-sample forecasting accuracy of various structural and time series exchange rate models. We find that a random walk model performs as well as any estimated model at one to twelve month horizons for the dollar/pound, dollar/mark, dollar/yen and tradeweighted dollar exchange rates. The candidate structural models include the flexible-price (Frenkel-Bilson) and sticky-price (Dornbusch-Frankel) monetary models, and a sticky-price model which incorporates the current account (Hooper-Morton). The structural models perform poorly despite the fact that we base their forecasts on actual realized values of future explanatory variables. 1.

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In the literature on Unobservable Component Models, three main statistical instruments have been used for signal extraction: Fixed Interval Smoothing (FIS) which derives from Kalman’s seminal work on optimal state-space filter theory in the time domain; Wiener-Kolmogorov-Whittle Optimal Signal Extraction (OSE) theory, which is normally set in the frequency domain and dominates the field of classical statistics; and Regularisation, which was developed mainly by numerical analysts but is referred to as Smoothing in the statistical literature (e.g. smoothing splines, kernel smoothers and local regression). Although some recognition of the inter-relationship between these methods can be discerned from the literature, no clear discussion of their equivalence has appeared. This paper exposes clearly the inter-relationships between the three methods; highlights important properties of the smoothing filters used in signal extraction; and stresses the advantages of the FIS algorithms as a practical solution to the signal extraction problem. It also emphasises the importance of the classical OSE theory as an analytical tool for a better understanding of the problem of signal extraction.

We examine the prevalence of data, specification, and parameter uncertainty in the formation of simple rules which mimic monetary policy-making decisions. Our approach is to build realtime datasets and simulate a real-time policy-setting environment in which we are able to assess the actual performance of rules, had they been followed in real time. This approach allows us not only to track the performance of alternative rules over time (hence facillitating a type of model selection among competing rules), but also allows us more generally to assess the importance of the data revision process in the formation of macroeconomic time series models. From the perspective of real time data, our results suggest that the use of data which are erroneous, in the sense that they were not available at the time decisions based on forecasts from the rules were used, can lead to the selection of quantitatively di®erent models. From the perspective of policy rules, we find that: our version of "calibration" is better than naive estimation, although both are dominated by an approach to rule formation based on adaptive least squares learning using; rules based on seasonally unadjusted data are more reliable than those based on seasonally adjusted data; and rules based soly on preliminary data do not minimize mean square forecast error (MSE) risk. In particular, early releases of data can be noisy, and for this reason it is useful to also use data which have been revised when making decisions using policy rules.

Introduction 1.1. ############ von Neumann and Morgenstern (1944) and Savage (1954) created mathematical foundations that applied economists have used to construct quantitative dynamic models for policy making and empirical analyses. The mathematical foundations give modern dynamic models internal coherence and sharp empirical predictions. However, those foundations should invite researchers to confront the unsettling fact that their models are approximations. That would expose logical problems that until recently have been swept under the rug, but that still call for repair. A model is a probability distribution over a sequence. Applied dynamic economists readily accept that their models are approximations # because applied models must be tractable, that is, feasible to solve, estimate, and simulate. With tractability comes misspecication. Model misspecication is unavoidable in applied economic research. Admitting model misspecication rai

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Le CIRANO est une corporation privée à but non lucratif constituée en vertu de la Loi des compagnies du Québec. Le financement de son infrastructure et de ses activités de recherche provient des cotisations de ses organisations-membres, d’une subvention d’infrastructure du ministère de l’Industrie, du Commerce, de la Science et de la Technologie, de même que des subventions et mandats obtenus par ses équipes de recherche. La Série Scientifique est la réalisation d’une des missions que s’est données le CIRANO, soit de développer l’analyse scientifique des organisations et des comportements stratégiques. CIRANO is a private non-profit organization incorporated under the Québec Companies Act. Its infrastructure and research activities are funded through fees paid by member organizations, an infrastructure grant from the Ministère de l’Industrie, du Commerce, de la Science et de la Technologie, and grants and research mandates obtained by its research teams. The Scientific Series fulfils one of the missions of CIRANO: to develop the scientific analysis of organizations and strategic behaviour.

Le CIRANO est une corporation privée à but non lucratif constituée en vertu de la Loi des compagnies du Québec. Le financement de son infrastructure et de ses activités de recherche provient des cotisations de ses organisations-membres, d’une subvention d’infrastructure du ministère de l’Industrie, du Commerce, de la Science et de la Technologie, de même que des subventions et mandats obtenus par ses équipes de recherche. La Série Scientifique est la réalisation d’une des missions que s’est données le CIRANO, soit de développer l’analyse scientifique des organisations et des comportements stratégiques. CIRANO is a private non-profit organization incorporated under the Québec Companies Act. Its infrastructure and research activities are funded through fees paid by member organizations, an infrastructure grant from the Ministère de l’Industrie, du Commerce, de la Science et de la Technologie, and grants and research mandates obtained by its research teams. The Scientific Series fulfils one of the missions of CIRANO: to develop the scientific analysis of organizations and strategic behaviour.

We derive monthly and quarterly series of UK GDP for the inter-war period from a set of monthly indicators that were constructed by The Economist at the time. The monthly information is complemented with data for quarterly industrial production, allowing us to employ mixed-frequency methods to produce monthly estimates of GDP and of industrial production. We proceed to illustrate how the new data compare with existing high frequency data and how they can be used to contribute to our understanding of the economic history of the UK in the inter-war period and to draw comparisons between recession profiles in the inter-war and the post-war period.