Browsing by Author "Hoover, KD"

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    A bootstrap method for identifying and evaluating a structural vector autoregression
    (Oxford Bulletin of Economics and Statistics, 2008-08-01) Demiralp, S; Hoover, KD; Perez, SJ
    Graph-theoretic methods of causal search based on the ideas of Pearl (2000), Spirtes et al. (2000), and others have been applied by a number of researchers to economic data, particularly by Swanson and Granger (1997) to the problem of finding a data-based contemporaneous causal order for the structural vector autoregression, rather than, as is typically done, assuming a weakly justified Choleski order. Demiralp and Hoover (2003) provided Monte Carlo evidence that such methods were effective, provided that signal strengths were sufficiently high. Unfortunately, in applications to actual data, such Monte Carlo simulations are of limited value, as the causal structure of the true data-generating process is necessarily unknown. In this paper, we present a bootstrap procedure that can be applied to actual data (i.e. without knowledge of the true causal structure). We show with an applied example and a simulation study that the procedure is an effective tool for assessing our confidence in causal orders identified by graph-theoretic search algorithms. © 2008. Blackwell Publishing Ltd and the Department of Economics, University of Oxford.
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    Against Psychosis: A Review of Roman Frydman and Michael D. Goldberg’s Beyond Mechanical Markets: Asset Price Swings, Risk, and the Role of the State
    (CHOPE Working Paper, 2012-01-18) Hoover, KD
    A review essay of Roman Frydman & Michael D. Goldberg’s Beyond Mechanical Markets: Asset Price Swings, Risk, and the Role of the State.
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    Allowing the data to speak freely: The macroeconometrics of the cointegrated vector autoregression
    (American Economic Review, 2008-05-01) Hoover, KD; Johansen, S; Juselius, K
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    Empirical Identification of the Vector Autoregression: The Causes and Effects of U.S. M2
    (2008) Hoover, KD; Demiralp, S; Perez, SJ
    The M2 monetary aggregate is monitored by the Federal Reserve, using a broad brush theoretical analysis and an informal empirical analysis. This paper illustrates empirical identification of an eleven-variable system, in which M2 and the factors that the Fed regards as causes and effects are captured in a vector autogregression. Taking account of cointegration, the methodology combines recent developments in graph-theoretical causal search algorithms with a general-to-specific search algorithm to identify a fully specified structural vector autoregression (SVAR). The SVAR is used to examine the causes and effects of M2 in a variety of ways. We conclude that, while the Fed has rightly identified a number of special factors that influence M2 and while M2 detectably affects other important variables, there is 1) little support for the core quantity-theoretic approach to M2 used by the Fed; and 2) M2 is a trivial linkage in the transmission mechanism from monetary policy to real output and inflation.
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    Experiments, Passive Observation and Scenario Analysis: Trygve Haavelmo and the Cointegrated Vector Autoregression
    (Univ. of Copenhagen Dept. of Economics Discussion Paper, 2012-11-05) Hoover, KD; Juselius, K
    The paper provides a careful, analytical account of Trygve Haavelmo's unsystematic, but important, use of the analogy between controlled experiments common in the natural sciences and econometric techniques. The experimental analogy forms the linchpin of the methodology for passive observation that he develops in his famous monograph, The Probability Approach in Econometrics (1944). We show how, once the details of the analogy are systematically understood, the experimental analogy can be used to shed light on theory-consistent cointegrated vector autoregression (CVAR) scenario analysis. CVAR scenario analysis can be seen as a clear example of Haavelmo's 'experimental' approach; and, in turn, it can be shown to extend and develop Haavelmo's methodology and to address issues that Haavelmo regarded as unresolved.
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    Idealizing Reduction: The Microfoundations of Macroeconomics
    (Erkenntnis, 2010-11-01) Hoover, KD
    The dominant view among macroeconomists is that macroeconomics reduces to microeconomics, both in the sense that all macroeconomic phenomena arise out of microeconomic phenomena and in the sense that macroeconomic theory-to the extent that it is correct-can be derived from microeconomic theory. More than that, the dominant view believes that macroeconomics should in practice use the reduced microeconomic theory: this is the program of microfoundations for macroeconomics to which the vast majority of macroeconomists adhere. The "microfoundational" models that they actually employ are, however, characterized by another feature: they are highly idealized, even when they are applied as direct characterizations of actual data, which itself consists of macroeconomic aggregates. This paper explores the interrelationship between reductionism and idealization in the microfoundational program and the role of idealization in empirical modeling. © 2010 The Author(s).
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    In the Kingdom of Solovia: The rise of growth economics at MIT, 1956–70
    (History of Political Economy, 2014-01-01) Boianovsky, M; Hoover, KD
    © 2015 by Duke University Press.From its flow tide, fueled by the Cold War, to its ebbing with the anti-growth movement and the economic crises of the early 1970s, the “growthmen” of MIT stood at the center of the dominant field in macroeconomics. The history of MIT growth economics is traced from Robert Solow’s seminal neoclassical growth model of 1956 through the stabilization of growth theory in the first graduate textbooks.
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    John Maynard Keynes of Bloomsbury: Four Short Talks
    (2009) Goodwin, C; Weintraub, ER; Hoover, KD; Caldwell, B
    on Keynes in relation to the Bloomsbury Group: I. Maynard Keynes of Bloomsbury (Craufurd Goodwin); II. Keynes as Policy Advisor (E. Roy Weintraub); III. Keynes and Economics (Kevin D. Hoover); IV. Keynes and Hayek (Bruce Caldwell). The talks were delivered as part of roundtable discussion on John Maynard Keynes of Bloomsbury, the inaugural event of the Center for the History of Political Economy at Duke University, and were held in conjunction with Vision and Design: A Year of Bloomsbury, a campus-wide interdisciplinary program surrounding an exhibition of Bloomsbury art at Duke University's Nasher Museum.
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    MATHEMATICAL ECONOMICS COMES to AMERICA: CHARLES S. PEIRCE'S ENGAGEMENT with COURNOT'S
    (Journal of the History of Economic Thought, 2015-11-12) Wible, JR; Hoover, KD
    © The History of Economics Society 2015.Although Cournot's mathematical economics was generally neglected until the mid-1870s, he was taken up and carefully studied by the Scientific Club of Cambridge, Massachusetts, even before his discovery by Walras and Jevons. The episode is reconstructed from fragmentary manuscripts of the pragmatist philosopher Charles S. Peirce, a sophisticated mathematician. Peirce provides a subtle interpretation and anticipates Bertrand's criticisms.
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    Measuring causes: Episodes in the quantitative assessment of the value of money
    (History of Political Economy, 2001-12-01) Hoover, KD; Dowell, ME
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    Milton Friedman's Stance: The Methodology of Causal Realism
    (2004) Hoover, KD
    Milton Friedman is usually regarded as an instrumentalist on the basis of his infamous claim that economic theories are to be judged by their predictions and not by the realism of their assumptions. This interpretation sits oddly with Friedman's empirical work - e.g., Friedman and Schwartz''s monetary history - and his explicit rejection of theories of the business cycle that, while based on accurate correlations, nevertheless do not make economic sense. In this paper, I try to reconcile Friedman's methodological writings with his practices as an empirical economist by, first, taking his roots in Alfred Marshall seriously and, second, by taking the methodological implications of his empirical work seriously. Friedman dislikes the word "cause". Nevertheless, appearances to the contrary notwithstanding, Friedman is best understood as a causal realist - that is, one who understands the object of scientific inquiry as the discovery through empirical investigation of the true causal mechanisms underlying observable phenomena.
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    Post hoc ergo propter once more an evaluation of 'does monetary policy matter?' in the spirit of James Tobin
    (Journal of Monetary Economics, 1994-01-01) Hoover, KD; Perez, SJ
    Christina and David Romer's paper 'Does Monetary Policy Matter?' advocates the so-called 'narrative' approach to causal inference. We demonstrate that this method will not sustain causal inference. First, it is impossible to distinguish monetary shocks from oil shocks as causes of recessions. Second, a world in which the Fed only announces intentions to act cannot be distinguished from one in which it in fact acts. Third, the techniques of dynamic simulation used in the Romers' study are inappropriate and quantitatively misleading. And, finally, their approach provides no basis for establishing causal asymmetry. © 1994.
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    Solow's Harrod: Transforming Cyclical Dynamics into a Model of Long-Run Growth
    (Economic Research Initiatives at Duke (ERID), 2013-03-27) Halsmayer, V; Hoover, KD
    Modern growth theory derives mostly from Robert Solow’s “A Contribution to the Theory of Economic Growth” (1956). Solow’s own interpretation locates the origins of his “Contribution” in his view that the growth model of Roy Harrod implied a tendency toward progressive collapse of the economy. He formulates his view in terms of Harrod’s invoking a fixed-coefficients production function. We challenge Solow’s reading of Harrod’s “Essay in Dynamic Theory,” arguing that Harrod’s object in providing a “dynamic” theory had little to do with the problem of long-run growth as Solow understood it, but instead addressed medium-run fluctuations, the “inherent instability” of economies. It was an attempt to isolate conditions under which the economy might tend to run below potential. In making this argument, Harrod does not appeal to a fixed-coefficients production function – or to any production function at all, as that term is understood by Solow. Solow interpreted Harrod’s “Essay” in the light of a particular culture of understanding grounded in the practice of formal modeling that emerged in economics in the post-World War II period. The fate of Harrod’s analysis is a case study in the difficulties in communicating across distinct interpretive communities and of the potential for losing content and insights in the process. From Harrod’s English Keynesian point of view, Solow’s interpretation arose out of a culture of misunderstanding, and his objects – particularly, of trying to account for a tendency.
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    Some causal lessons from macroeconomics
    (Journal of Econometrics, 2003-01-01) Hoover, KD
    Some of the well-posed causal aspects from macroeconomics were discussed. The causal lessons were supported by the vector-autoregression (VAR) framework of macroeconomics which was analogous to the panel-data approach. The analysis of causality in a VAR framework carried important lessons for the panel-studies. The results show that the direct and indirect linkages among the health indicators and the counterfactual simulations were sensitive to the omission of a contemporaneous link from wealth to health.
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    Sound and fury: McCloskey and significance testing in economics
    (Journal of Economic Methodology, 2008-03-01) Hoover, KD; Siegler, MV
    For more than 20 years, Deidre McCloskey has campaigned to convince the economics profession that it is hopelessly confused about statistical significance. She argues that many practices associated with significance testing are bad science and that most economists routinely employ these bad practices: 'Though to a child they look like science, with all that really hard math, no science is being done in these and 96 percent of the best empirical economics ' (McCloskey 1999). McCloskey's charges are analyzed and rejected. That statistical significance is not economic significance is a jejune and uncontroversial claim, and there is no convincing evidence that economists systematically mistake the two. Other elements of McCloskey's analysis of statistical significance are shown to be ill-founded, and her criticisms of practices of economists are found to be based in inaccurate readings and tendentious interpretations of those economists' work. Properly used, significance tests are a valuable tool for assessing signal strength, for assisting in model specification, and for determining causal structure.
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    The causal direction between money and prices. An alternative approach
    (Journal of Monetary Economics, 1991-01-01) Hoover, KD
    Causality is viewed as a matter of control. Controllability is captured in Simon's analysis of causality as an asymmetrical relation of recursion between variables in the unobservable data-generating process. Tests of the stability of marginal and conditional distributions for these variables can provide evidence of causal ordering. The causal direction between prices and money in the United States 1950-1985 is assessed. The balance of evidence supports the view that money does not cause prices, and that prices do cause money. © 1991.
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    The neoclassical growth model and twentieth-century economics
    (History of Political Economy, 2009-12-01) Boianovsky, M; Hoover, KD
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    The ontological status of shocks and trends in macroeconomics
    (Synthese, 2015-11-01) Hoover, KD
    © 2014, Springer Science+Business Media Dordrecht.Modern empirical macroeconomic models, known as structural autoregressions (SVARs) are dynamic models that typically claim to represent a causal order among contemporaneously valued variables and to merely represent non-structural (reduced-form) co-occurence between lagged variables and contemporaneous variables. The strategy is held to meet the minimal requirements for identifying the residual errors in particular equations in the model with independent, though otherwise not directly observable, exogenous causes (“shocks”) that ultimately account for change in the model. In nonstationary models, such shocks accumulate so that variables have discernible trends. Econometricians have conceived of variables that trend in sympathy with each other (so-called “cointegrated variables”) as sharing one or more of these unobserved trends as a common cause. It is possible for estimates of the values of both the otherwise unobservable individual shocks and the otherwise unobservable common trends to be backed-out of cointegrated systems of equations. The issue addressed in this paper is whether and in what circumstances these values can be regarded as observations of real entities rather than merely artifacts of the representation of variables in the model. The issue is related, on the one hand, to practical methodological problems in the use of SVARs for policy analysis—e.g., does it make sense to estimate of shocks or trends in one model and then use them as measures of variables in a conceptually distinct model? The issue is also related to debates in the philosophical analysis of causation—particularly, whether we are entitled, as assumed by the developers of Bayes-net approaches, to rely on the causal Markov condition (a generalization of Reichenbach’s common-cause condition) or whether cointegration generates a practical example of Nancy Cartwright’s “byproducts” objection to the causal Markov condition.
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    The Past as the Future: The Marshallian Approach to Post-Walrasian Econometrics
    (2004-10-13) Hoover, KD
    The popular image of the scientific revolution usually pits young revolutionaries against old conservatives. Freeman Dyson (2004, p. 16) observes that, in particle physics in the mid-20th century, something had to change. But in the revolution of quantum electrodynamics, Einstein, Dirac, Heisenberg, Born, and Schödinger were old revolutionaries, while the winners, Feynman, Schwinger, and Tomonaga, were young conservatives. Post-Walrasian economics is not a doctrine, but a slogan announcing that something has to change. Most of the self-conscious efforts to forge a post-Walrasian economics are due to old radicals. Here I want to explore the space of the young conservative: the future is past, particularly in the methodology of Alfred Marshall’s methodological essay, “The Present Position of Economics” (1885). The radical approach identifies the problem as Walrasian theory and seeks to replace it with something better and altogether different. The conservative approach says that theory is not the problem. The problem is rather to establish an empirical discipline that connects theory to the world. Marshall’s methodology places the relationship between theory and empirical tools on center stage. In North America, if not in Europe, the dominant tools of macroeconometrics are the vector autoregression (VAR) and calibration techniques. These techniques reached their current status as the result of two nearly simultaneous reactions to the Cowles-Commission program, which dominated macroeconometrics during the two decades 1950-1970. These are the famous Lucas critique, and the practically influential, if less storied, Sims critique. I will briefly consider the nature of these two critiques and, then, the competing Walrasian and Marshallian visions of the role of theory in econometrics. I conclude with some suggestions about how to do Marshallian macroeconometrics.