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Stan Hurn Publications

Publish Date
Discussion Paper
Abstract

Financial econometrics is a dynamic discipline that began to take on its present form around the turn of the century. Since then it has found a permanent position as a popular course sequence in both undergraduate and graduate teaching programs in economics, finance, and business schools. Because of the breadth of the subject’s foundations, its extensive coverage in applications and because these courses attract a wide range of students with accompanying interests and skill sets that cover diverse areas and technical capabilities, teaching financial econometrics presents many challenges to the university educator. This chapter addresses some of these challenges, provides helpful guidelines to educators, and draws on the combined experience of the authors as teachers and researchers of modern financial econometrics as well as their recent textbook Financial Econometric Modeling (Hurn et al., 2021). The focus is on students converting to finance and econometrics with limited technical background

Abstract

This paper re-examines changes in the causal link between money and income in the United States for over the past half century (1959 - 2014). Three methods for the data-driven discovery of change points in causal relationships are proposed, all of which can be implemented without prior detrending of the data. These methods are a forward recursive algorithm, a recursive rolling algorithm and the rolling window algorithm all of which utilize subsample tests of Granger causality within a lag-augmented vector autoregressive framework. The limit distributions for these subsample Wald tests are provided. The results from a suite of simulation experiments suggest that the rolling window algorithm provides the most reliable results, followed by the recursive rolling method. The forward expanding window procedure is shown to have worst performance. All three approaches find evidence of money-income causality during the Volcker period in the 1980s. The rolling and recursive rolling algorithms detect two additional causality episodes: the turbulent period of late 1960s and the starting period of the subprime mortgage crisis in 2007.