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Since Hayek’s pioneering work in the 1930s, the Austrian business cycle theory (ABCT) has been presented as a disequilibrium theory populated by less-than-perfectly rational agents. In contrast, we maintain that (1) the Austrian business cycle theory is consistent with rational expectations and (2) the post-boom adjustment process can be understood in an equilibrium framework. Hence, we offer a new interpretation of the existing theory. In doing so, we also address concerns raised with Garrison’s (2001) diagrammatic approach, wherein the economy moves beyond the production possibilities frontier. Our interpretation might accurately be described as a monetary disequilibrium approach grounded in an implicit general equilibrium framework with positive costs of reallocation.

Lester and Wolff (2013) find little empirical support for the Austrian business cycle theory. According to their analysis, an unexpected monetary shock does not alter the structure of production in a way consistent with the Austrian view. Rather than increasing production in early and late stages relative to middle stages, they find the opposite – a positive monetary shock typically decreases production in early and late stages relative to middle stages. We argue that the measures of production and prices employed by Lester and Wolff (2013) are constructed in such a way that makes them inappropriate for assessing the empirical relevance of the Austrian business cycle theory’s unique features. After describing how these measures are constructed and why using ratios of stages is problematic, we use a structural vector autoregression to consider the effects of a monetary shock on each stage of the production process. We show that, with a clearer understanding of what is actually being measured by the stage of process data, the results are consistent with (but not exclusive to) the Austrian view.

This chapter elaborates the concept of Structural Equation Modelling (SEM) and further validates the model for the sustainability of financial innovation using factors of e-payment system, adoption intention, trust and sustainability, and finally derives a model for the sustainability of the financial innovation using SEM. The chapter also presents Sustainability of E-payment System on Customer Perspective (SEPSCP) through segmentation approach.

Though contemporaries, Adam Smith and Sir James Steuart are commonly portrayed as if they belonged to different eras. Whereas Smith went down in history as both founder of the science of political economy and patron saint of economic liberalism, Steuart became known as the last, outdated advocate for mercantilist policies in Britain. Smith himself was responsible for popularizing the notion of the “system of commerce” as an approach to political economy that dominated the early modern period. As a historiographical concept, the mercantile system became a misguided international trade theory grounded upon the Midas fallacy and the favorable balance of trade doctrine. Smith’s treatment of international trade in the Wealth of Nations, however, was criticized for its inconsistencies and lack of analytical clarity even by some among his own followers. Given Smith’s doubtful credentials as an international trade theorist, the chapter investigates the reasons that led him and Steuart to be placed on opposite sides of the mercantilist divide. The authors analyze the works of both authors in depth, showing that their disagreements had chiefly to do with different views on money and monetary policy. Additionally, the authors explore how early nineteenth-century writers such as Jean-Baptiste Say and J. R. McCulloch helped forge the intellectual profiles of both Steuart and Smith.

This chapter is concerned with the estimation of spillover effects when outcomes arise as a consequence of bilateral interactions instead of from individual actions. In this type of environments, outcomes are generated on links instead of on nodes of a network, like bilateral prices in over-the-counter markets. The author proposes a link-based spatial autoregressive (SAR) model and discusses identification conditions and a two step least square estimation procedure. The author shows analytically that using a standard node-based SAR, which models nodes instead of links’ outcomes, produces misleading results when the data generating process is link-based. The methodology is illustrated using Monte Carlo experiments and real data from an interbank network.