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455
Policy Rules for Open Economies
 Monetary Policy Rules
, 1999
"... January 1998. I am grateful for research assistance from Qiming Chen, ..."
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Cited by 193 (1 self)
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January 1998. I am grateful for research assistance from Qiming Chen,
Structural equations, treatment effects and econometric policy evaluation. Econometrica 2005; 73(3
"... This paper uses the marginal treatment effect (MTE) to unify the nonparametric literature on treatment effects with the econometric literature on structural estimation using a nonparametric analog of a policy invariant parameter; to generate a variety of treatment effects from a common semiparametri ..."
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Cited by 95 (21 self)
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This paper uses the marginal treatment effect (MTE) to unify the nonparametric literature on treatment effects with the econometric literature on structural estimation using a nonparametric analog of a policy invariant parameter; to generate a variety of treatment effects from a common semiparametric functional form; to organize the literature on alternative estimators; and to explore what policy questions commonly used estimators in the treatment effect literature answer. A fundamental asymmetry intrinsic to the method of instrumental variables (IV) is noted. Recent advances in IV estimation allow for heterogeneity in responses but not in choices, and the method breaks down when both choice and response equations are heterogeneous in a general way.
The Art of Causal Conjecture
, 1996
"... Causal relations are regularities in the way Nature’s predictions change. Since we usually do not stand in Nature’s shoes, we usually do not observe these dynamic regularities directly. But we sometimes observe statistical regularities that are most easily explained by hypothesizing such dynamic reg ..."
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Cited by 92 (18 self)
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Causal relations are regularities in the way Nature’s predictions change. Since we usually do not stand in Nature’s shoes, we usually do not observe these dynamic regularities directly. But we sometimes observe statistical regularities that are most easily explained by hypothesizing such dynamic regularities. In this chapter, I illustrate this process of causal conjecture with a few simple examples. I first consider a negative causal relation: causal uncorrelatedness. Two variables are causally uncorrelated if there are no steps in Nature’s event tree that change them both in expected value. They have, in this sense, no common causes. This implies, as we shall see, that the two variables are uncorrelated in the classical sense in every situation in the tree. When we observe that variables are uncorrelated in many different situations, then we may conjecture that this is due to their being causally uncorrelated. I will also discuss three causal relations of a positive character. These relations assert, each in a different way, that the causes (steps in Nature’s tree) that affect a certain variable X also affect another variable Y. This implies regularities in certain classical statistical predictions. The first causal relation, which I call linear sign, implies regularity in linear regression. The second, scored sign, implies regularity in conditional
Mapping the Two Faces of R&D: Productivity Growth in a Panel of OECD Industries
, 2004
"... Many writers have claimed that research and development (R&D) has two faces. In addition to the conventional role of stimulating innovation, R&D enhances technology transfer (absorptive capacity). We explore this idea empirically using a panel of industries across twelve OECD countries. We ..."
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Cited by 76 (8 self)
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Many writers have claimed that research and development (R&D) has two faces. In addition to the conventional role of stimulating innovation, R&D enhances technology transfer (absorptive capacity). We explore this idea empirically using a panel of industries across twelve OECD countries. We find R&D to be statistically and economically important in both technological catchup and innovation. Human capital also plays an major role in productivity growth, but we only find a small effect of trade. In failing to take account of R&Dbased absorptive capacity, existing U.S.based studies may underestimate the return to R&D.
Measuring monetary policy with VAR models: An evaluation
 European Economic Review
, 1998
"... This paper evaluates VAR models designed to analyse the monetary policy transmission mechanism in the United States by considering three issues: specification, identification, and the effect of the omission of the longterm interest rate. Specification analysis suggests that only VAR models estimate ..."
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Cited by 71 (2 self)
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This paper evaluates VAR models designed to analyse the monetary policy transmission mechanism in the United States by considering three issues: specification, identification, and the effect of the omission of the longterm interest rate. Specification analysis suggests that only VAR models estimated on a single monetary regime feature parameters stability and do not show signs of misspecification. The identification analysis shows that VARbased monetary policy shocks and policy disturbances identified from alternative sources are not highly correlated but yield similar descriptions of the monetary transmission mechanism. Lastly, the inclusion of the longterm interest rate in a benchmark VAR delivers a more precise estimation of the structural parameters capturing behaviour in the market for reserves and shows that contemporaneous fluctuations in longterm interest rates are an important determinant of the monetary authority’s
Dynamics of trade–by–trade price movements: decomposition and models. Working paper, Nuffield
 Newton Institute, Cambridge University
, 1998
"... and models ..."
Graphs, Causality, And Structural Equation Models
, 1998
"... Structural equation modeling (SEM) has dominated causal analysis in the social and behavioral sciences since the 1960s. Currently, many SEM practitioners are having difficulty articulating the causal content of SEM and are seeking foundational answers. ..."
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Cited by 51 (14 self)
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Structural equation modeling (SEM) has dominated causal analysis in the social and behavioral sciences since the 1960s. Currently, many SEM practitioners are having difficulty articulating the causal content of SEM and are seeking foundational answers.