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Structural breaks have attracted considerable attention recently, especially in light of the financial crisis, Great Recession, the COVID-19 pandemic, and war. While structural breaks pose significant econometric challenges, machine learning provides an incisive tool for detecting and quantifying breaks. The current paper presents a unified framework for analyzing breaks; and it implements that framework to test for and quantify changes in precipitation in Mauritania over 1919–1997. These tests detect a decline of one third in mean rainfall, starting around 1970. Because water is a scarce resource in Mauritania, this decline—with adverse consequences on food production—has potential economic and policy consequences. [ABSTRACT FROM AUTHOR]

To purchase or authenticate to the full-text of this article, please visit this link: http://dx.doi.org/10.1111/j.1468-0084.2008.00535.x Byline: Neil R. Ericsson Keywords: C52; E41 Abstract: Abstract Robustness and fragility in Leamer's sense are defined with respect to a particular coefficient over a class of models. This paper shows that inclusion of the data generation process in that class of models is neither necessary nor sufficient for robustness. This result holds even if the properly specified model has well-determined, statistically significant coefficients. The encompassing principle explains how this result can occur. Encompassing also provides a link to a more common-sense notion of robustness, which is still a desirable property empirically; and encompassing clarifies recent discussion on model averaging and the pooling of forecasts.

This paper analyzes the continued dollarization of the Argentine economy, which experienced a rapid decline in inflation following its hyperinflations of the 1980s. First, a measure of dollar currency circulating in Argentina is developed, thereby improving upon previous studies of dollarization and currency substitution that focused on dollar deposit holdings only. Second, cointegration analysis of peso money demand in Argentina finds a negative `ratchet effect' from inflation. The reduction in peso money demand attributable to the ratchet mirrors the estimated stock of all dollar assets held domestically by Argentine residents, consistent with the hypothesis of irreversible dollarization. Keywords: Argentina; Broad money; Cointegration; Currency substitution; Dollarization; Error correction; Hyperinflation; Irreversibility; Money demand; Ratchet effect

Some recent studies have suggested constructing a Monetary Conditions Index (or MCI) to serve as an indicator of monetary policy stance. The central banks of Canada, Sweden and Norway all construct an MCI and (to varying degrees) use it in conducting monetary policy. Empirically, an MCI is calculated as the weighted sum of changes in a short-term interest rate and the exchange rate relative to values in a baseline year. The weights aim to reflect these variables' effects on longer-term focuses of policy -- economic activity and inflation. This paper derives analytical and empirical properties of MCIs in an attempt to ascertain their usefulness in monetary policy. An MCI assumes an underlying model relating economic activity and inflation to the variables in the MCI. Several issues arise for that model, including its empirical constancy, cointegration, exogeneity, dynamics and potential omitted variables. Because of its structure, the model is unlikely to be constant or to have strongly exogenous variables, yet constancy and exogeneity are critical for the usefulness of an MCI. Empirical analyses of Canadian, Swedish and Norwegian MCIs confirm such difficulties. Thus, the value of an MCI for conduct of economic policy is in doubt., An analytical and empirical characterization is made on 'Monetary Conditions Index' (MCI), using the properties of the models upon which the MCI is built. The usefulness of MCI in economic policy depend on the empirical and analytical assumptions of the models used. These assumptions include valid treatment of factors concerning dynamics, cointegration, parameter constancy, and omitted variables. The assumptions are tested by applications to Swedish, Norwegian and Canadian MCIs.

Structural breaks in stationary time series can induce apparent unit roots in those series. Thus, using recently developed recursive Monte Carlo techniques, this paper investigates the properties of several cointegration tests when the marginal process of one of the variables in the cointegrating relationship is stationary with a structural break. The break has little effect on the tests' size. However, tests based on estimated error correction models generally are more powerful than Engle and Granger's two-step procedure employing the Dickey-fuller unit root test. Discrepancies in power arise when the data generation process does not have a common factor. Key words: Cointegration; Error correction; Monte Carlo; Structural breaks; Tests JEL classification: C12; C15

A `structural' error correction model (in Boswijk's sense) is a representation of a conditional error correction model that satisfies certain restrictions. This paper examines the conditions under which such a structural error correction model exists and when the associated representation is of interest. To clarify the nature of such models, several analytical and empirical examples are considered, which violate those conditions. Structural error correction models are economically appealing, but their limitations imply that some care must be taken when applying them in practice. Key words: Cointegration; Conditional models; Error correction; Exogeneity; Structural models. JEL classification: C32; C51; C52

Various observations are presented that serve as commentaries to the issues outlined by Michael P. Clements and David F. Hendry on forecast accuracy measurements. Using the mean square forecast error, both Clements and Hendry succeeded in outlining an acceptable criterion for evaluating forecasts. With respect to a reasonable set of transformations, however, the criterion only appeals to the principle of invariance thus leaving open the question of determining between alternative invariant measures.

Measures of forecast performance of macromodels are commonly through a study of its parameter constancy and the model's mean square forecast. The model's forecast accuracy in terms of these two measures is considered incomplete. While parameter constance and mean swquare forecast are necessary, they are not sufficient by themselves. Model evaluation should also satisfy the criteria of the target model data and the data of alternative models.

Measurements and analysis of long-run economic factors are discussed through the concepts of cointegration and exogeneity. Cointegration is the economic summation of the relationship of long-run variables and a statistical model of those variables. Exogeneity, on the other hand, is the derivation of the implications of cointegration for policy analysis. The excerpts are further explained by models and examples provided.