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  1. Kevin D. Hoover (2001). Causality in Macroeconomics. Monograph Collection (Matt - Pseudo).
     
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  2. Kevin D. Hoover (2011). Counterfactuals and Causal Structure. In Phyllis McKay Illari, Federica Russo & Jon Williamson (eds.), Causality in the Sciences. OUP Oxford
     
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  3.  11
    Kevin D. Hoover (2015). Reductionism in Economics: Intentionality and Eschatological Justification in the Microfoundations of Macroeconomics. Philosophy of Science 82 (4):689-711.
    Macroeconomists overwhelmingly believe that macroeconomics requires microfoundations, typically understood as a strong eliminativist reductionism. Microfoundations aims to recover intentionality. In the face of technical and data constraints macroeconomists typically employ a representative-agent model, in which a single agent solves the microeconomic optimization problem for the whole economy, and take it to be microfoundationally adequate. The characteristic argument for the representative-agent model holds that the possibility of the sequential elaboration of the model to cover any number of individual agents justifies treating (...)
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  4.  46
    Kevin D. Hoover (2010). Idealizing Reduction: The Microfoundations of Macroeconomics. [REVIEW] Erkenntnis 73 (3):329 - 347.
    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 (...)
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  5.  14
    Kevin D. Hoover (2015). The Ontological Status of Shocks and Trends in Macroeconomics. Synthese 192 (11):3509-3532.
    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 (...)
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  6.  19
    Kevin D. Hoover & Mark V. Siegler (2008). Sound and Fury: McCloskey and Significance Testing in Economics. Journal of Economic Methodology 15 (1):1-37.
    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. (...)
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  7.  22
    Kevin D. Hoover (2003). Nonstationary Time Series, Cointegration, and the Principle of the Common Cause. British Journal for the Philosophy of Science 54 (4):527-551.
    Elliot Sober ([2001]) forcefully restates his well-known counterexample to Reichenbach's principle of the common cause: bread prices in Britain and sea levels in Venice both rise over time and are, therefore, correlated; yet they are ex hypothesi not causally connected, which violates the principle of the common cause. The counterexample employs nonstationary data—i.e., data with time-dependent population moments. Common measures of statistical association do not generally reflect probabilistic dependence among nonstationary data. I demonstrate the inadequacy of the counterexample and of (...)
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  8.  11
    Kevin D. Hoover & Stephen J. Perez (2000). Three Attitudes Towards Data Mining. Journal of Economic Methodology 7 (2):195-210.
    'Data mining' refers to a broad class of activities that have in common, a search over different ways to process or package data statistically or econometrically with the purpose of making the final presentation meet certain design criteria. We characterize three attitudes toward data mining: first, that it is to be avoided and, if it is engaged in, that statistical inferences must be adjusted to account for it; second, that it is inevitable and that the only results of any interest (...)
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  9.  4
    Kevin D. Hoover & Mark V. Siegler (2008). The Rhetoric of 'Signifying Nothing': A Rejoinder to Ziliak and McCloskey. Journal of Economic Methodology 15 (1):57-68.
  10.  17
    Kevin D. Hoover (1995). Is Macroeconomics for Real? The Monist 78 (3):235-257.
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  11.  29
    Kevin D. Hoover (1990). The Logic of Causal Inference: Econometrics and the Conditional Analysis of Causation. Economics and Philosophy 6 (2):207.
    Discontented people might talk of corruption in the Commons, closeness in the Commons and the necessity of reforming the Commons, said Mr. Spenlow solemnly, in conclusion; but when the price of wheat per bushel had been the highest, the Commons had been the busiest; and a man might lay his hand upon his heart, and say this to the whole world, – ‘Touch the Commons, and down comes the country!’.
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  12.  14
    Kevin D. Hoover, Probability and Structure in Econometric Models.
    The difficulty of conducting relevant experiments has long been regarded as the central challenge to learning about the economy from data. The standard solution, going back to Haavelmo's famous “The Probability Approach in Econometrics” (1944), involved two elements: first, it placed substantial weight on a priori theory as a source of structural information, reducing econometric estimates to measurements of causally articulated systems; second, it emphasized the need for an appropriate statistical model of the data. These elements are usually seen as (...)
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  13.  26
    Kevin D. Hoover (2009). How Can Economics Be an Inductive Science? Economics and Philosophy 25 (2):202.
  14.  4
    Kevin D. Hoover (2002). 7 Econometrics and Reality. In Uskali Mäki (ed.), Fact and Fiction in Economics: Models, Realism and Social Construction. Cambridge University Press 152.
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  15.  11
    Kevin D. Hoover (1994). Econometrics as Observation: The Lucas Critique and the Nature of Econometric Inference. Journal of Economic Methodology 1 (1):65-80.
  16.  41
    Kevin D. Hoover (1993). Causality and Temporal Order in Macroeconomics or Why Even Economists Don't Know How to Get Causes From Probabilities. British Journal for the Philosophy of Science 44 (4):693-710.
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  17.  20
    Kevin D. Hoover (1980). Abduction and the New Riddle of Induction. The Monist 63 (3):329-341.
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  18.  4
    Kevin D. Hoover (1990). The Logic of Causal Inference. Economics and Philosophy 6 (2):207-234.
    Discontented people might talk of corruption in the Commons, closeness in the Commons and the necessity of reforming the Commons, said Mr. Spenlow solemnly, in conclusion; but when the price of wheat per bushel had been the highest, the Commons had been the busiest; and a man might lay his hand upon his heart, and say this to the whole world, – ‘Touch the Commons, and down comes the country!’.
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  19.  9
    Kevin D. Hoover (2013). History, Philosophy and Theory of the Life Sciences. Perspectives in Biology and Medicine 54 (3):316-331.
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  20.  13
    Kevin D. Hoover (2012). Causal Structure and Hierarchies of Models. Studies in History and Philosophy of Science Part C 43 (4):778-786.
    Economics prefers complete explanations: general over partial equilibrium, microfoundational over aggregate. Similarly, probabilistic accounts of causation frequently prefer greater detail to less as in typical resolutions of Simpson’s paradox. Strategies of causal refinement equally aim to distinguish direct from indirect causes. Yet, there are countervailing practices in economics. Representative-agent models aim to capture economic motivation but not to reduce the level of aggregation. Small structural vector-autoregression and dynamic stochastic general-equilibrium models are practically preferred to larger ones. The distinction between exogenous (...)
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  21.  22
    Kevin D. Hoover (2002). Symposium on Marshall's Tendencies: 5 Sutton's Critique of Econometrics. Economics and Philosophy 18 (1):45-54.
    Through most of the history of economics, the most influential commentators on methodology were also eminent practitioners of economics. And even not so long ago, it was so. Milton Friedman, Paul Samuelson, Trygve Haavelmo, and Tjalling Koopmans were awarded Nobel prizes for their substantive contributions to economics, and were each important contributors to methodological thought. But the fashion has changed. Specialization has increased. Not only has methodology become its own field, but many practitioners have come to agree with Frank Hahn's (...)
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  22.  16
    Kevin D. Hoover (2000). Truth and Progress in Economic Knowledge (Edward Elgar, 1997, X + 232 Pages) Explorations in Economic Methodology: From Lakatos to Empirical Philosophy of Science (Routledge, 1998; VII + 246 Pages) Roger E. Backhouse. [REVIEW] Economics and Philosophy 16 (2):333-378.
  23.  1
    Kevin D. Hoover (2009). When is a Model Like a Thermometer? Journal of Economic Methodology 16 (4):417-422.
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  24.  11
    Kevin D. Hoover (1990). Nature's Capacities and Their Measurement, Nancy Cartwright. Oxford: Clarendon Press, 1989, X + 268 Pages. [REVIEW] Economics and Philosophy 6 (2):309.
  25.  3
    Kevin D. Hoover (2013). Beyond Mechanical Markets – Asset Price Swings, Risk and the Role of the State. Journal of Economic Methodology 20 (1):69 - 75.
    (2013). Beyond mechanical markets – asset price swings, risk and the role of the state. Journal of Economic Methodology: Vol. 20, Methodology, Systemic Risk, and the Economics Profession, pp. 69-75. doi: 10.1080/1350178X.2013.774856.
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  26.  12
    Kevin D. Hoover (2005). Quantitative Evaluation of Idealized Models in the New Classical Macroeconomics. Poznan Studies in the Philosophy of the Sciences and the Humanities 86 (1):15-34.
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  27.  3
    Kevin D. Hoover (2006). Fragility and Robustness in Econometrics: Introduction to the Symposium. Journal of Economic Methodology 13 (2):159-160.
  28.  9
    Kevin D. Hoover, Identity, Structure, and Causal Representation in Scientific Models.
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  29.  5
    Kevin D. Hoover (2010). Introduction: Methodological Implications of the Financial Crisis. Journal of Economic Methodology 17 (4):397-398.
  30.  2
    Kevin D. Hoover (2001). Introduction. Journal of Economic Methodology 8 (2):167-167.
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  31.  2
    Mark Blaug & Kevin D. Hoover (1996). Statement. Journal of Economic Methodology 3 (2):349-350.
  32. Kevin D. Hoover (2000). Book Review Column. [REVIEW] In Craig Freedman & Rick Szostak (eds.), Tales of Narcissus: The Looking Glass of Economic Science. Nova Science Publishers 123.
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  33. Kevin D. Hoover (2012). Causal Structure and Hierarchies of Models. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 43 (4):778-786.
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  34. Kevin D. Hoover (2008). Does Macroeconomics Need Microfoundations. In Daniel M. Hausman (ed.), The Philosophy of Economics: An Anthology. Cambridge University Press 315--333.
     
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  35. Kevin D. Hoover (1990). No Title Available: Reviews. Economics and Philosophy 6 (2):309-315.
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  36. Kevin D. Hoover (2015). Thomas Mayer. Journal of Economic Methodology 22 (4):526-527.
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