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Causal Diagrams For Empirical Research
"... The primary aim of this paper is to show how graphical models can be used as a mathematical language for integrating statistical and subjectmatter information. In particular, the paper develops a principled, nonparametric framework for causal inference, in which diagrams are queried to determine if ..."
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Cited by 180 (35 self)
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The primary aim of this paper is to show how graphical models can be used as a mathematical language for integrating statistical and subjectmatter information. In particular, the paper develops a principled, nonparametric framework for causal inference, in which diagrams are queried to determine if the assumptions available are sufficient for identifying causal effects from nonexperimental data. If so the diagrams can be queried to produce mathematical expressions for causal effects in terms of observed distributions; otherwise, the diagrams can be queried to suggest additional observations or auxiliary experiments from which the desired inferences can be obtained. Key words: Causal inference, graph models, interventions treatment effect 1 Introduction The tools introduced in this paper are aimed at helping researchers communicate qualitative assumptions about causeeffect relationships, elucidate the ramifications of such assumptions, and derive causal inferences from a combination...
On the Constancy of TimeSeries Econometric Equations
 Economic and Social Review
, 1996
"... Parameter constancy is a fundamental requirement for empirical models to be useful for forecasting, analysing economic policy, or testing economic theories. However, there are surprises in defining a constantparameter model, such that models with timevarying coefficients, and expansion of the para ..."
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Cited by 6 (6 self)
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Parameter constancy is a fundamental requirement for empirical models to be useful for forecasting, analysing economic policy, or testing economic theories. However, there are surprises in defining a constantparameter model, such that models with timevarying coefficients, and expansion of the parameterization over time are both compatible with constancy, yet unbiased forecasts may not entail a sensible model choice. Insample tests cannot determine likely postsample predictive failure. A comparison of two models of UK money demand illustrates the analysis empirically, as one suffers considerable predictive failure yet the other does not, despite being identical insample. 1 Introduction Parameter constancy is a fundamental requirement for empirical models to be useful for forecasting, analyzing economic policy, or testing economic theories. Nevertheless, it remains unclear precisely what constancy entails, what aspects of models should be constant, and what features of models insa...
The Error Term in the History of Time Series Econometrics.” Econometric Theory
, 2001
"... We argue that many methodological confusions in timeseries econometrics may be seen as arising out of ambivalence or confusion about the error terms. Relationships between macroeconomic time series are inexact and, inevitably, the early econometricians found that any estimated relationship would on ..."
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Cited by 4 (1 self)
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We argue that many methodological confusions in timeseries econometrics may be seen as arising out of ambivalence or confusion about the error terms. Relationships between macroeconomic time series are inexact and, inevitably, the early econometricians found that any estimated relationship would only fit with errors. Slutsky interpreted these errors as shocks that constitute the motive force behind business cycles. Frisch tried to dissect further the errors into two parts: stimuli, which are analogous to shocks, and nuisance aberrations. However, he failed to provide a statistical framework to make this distinction operational. Haavelmo, and subsequent researchers at the Cowles Commission, saw errors in equations as providing the statistical foundations for econometric models, and required that they conform to a priori distributional assumptions specified in structural models of the general equilibrium type, later known as simultaneousequations models (SEM). Since theoretical models were at that time mostly static, the structural modelling strategy relegated the dynamics in timeseries data frequently to nuisance, atheoretical complications. Revival of the shock interpretation in theoretical models came about through the rational expectations movement and development of the VAR (Vector AutoRegression) modelling approach. The socalled LSE (London School of Economics) dynamic specification approach decomposes the dynamics of modelled variable into three parts: shortrun shocks, disequilibrium shocks and innovative residuals, with only the first two of these sustaining an economic interpretation.
On The Identification Of Nonparametric Structural Models
, 1997
"... In this paper we study the identifiability of nonparametric models, that is, models in which both the functional forms of the equations and the probability distributions of the disturbances remain unspecified. Identifiability in such models does not mean uniqueness of parameters but rather uniquenes ..."
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Cited by 2 (1 self)
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In this paper we study the identifiability of nonparametric models, that is, models in which both the functional forms of the equations and the probability distributions of the disturbances remain unspecified. Identifiability in such models does not mean uniqueness of parameters but rather uniqueness of the set of predictions of interest to the investigator. For example, predicting the effects of changes, interventions, and control. We provide sufficient and necessary conditions for identifying a set of causal predictions of the type: "Find the distribution of Y , assuming that X is controlled by external intervention", where Y and X are arbitrary variables of interest. Whenever identifiable, such predictions can be expressed in closed algebraic form, in terms of observed distributions. We also show how the identifying criteria can be verified qualitatively, by inspection, using the graphical representation of the structural model. When compared to standard identifiability tests of lin...
Model Discovery and Trygve Haavelmo’s Legacy
"... Trygve Haavelmo’s Probability Approach aimed to implement economic theories, but he later recognized their incompleteness. Although he did not explicitly consider model selection, we apply it when theoryrelevant variables,{xt}, are retained without selection while selecting other candidate variable ..."
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Cited by 1 (1 self)
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Trygve Haavelmo’s Probability Approach aimed to implement economic theories, but he later recognized their incompleteness. Although he did not explicitly consider model selection, we apply it when theoryrelevant variables,{xt}, are retained without selection while selecting other candidate variables, {wt}. Under the null that the {wt} are irrelevant, by orthogonalizing with respect to the {xt}, the estimator distributions of the xt’s parameters are unaffected by selection even for more variables than observations and for endogenous variables. Under the alternative, when the joint model nests the generating process, an improved outcome results from selection. This implements Haavelmo’s program relatively costlessly.
Causality in Macroeconomics Identifying Causal Relationships from Policy Instruments to Target Variables
, 2002
"... Can causal relationships between macroeconomic variables be identified using econometric methods? Only partly, we argue. Normally causal relationships cannot be identified from empirical analysis. However, if enough changes in policy rules have occurred during the observation period, it can be possi ..."
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Can causal relationships between macroeconomic variables be identified using econometric methods? Only partly, we argue. Normally causal relationships cannot be identified from empirical analysis. However, if enough changes in policy rules have occurred during the observation period, it can be possible to identify causal relationships form policy instruments to target variables. An estimation procedure for obtaining this is sketched. This estimation procedure makes it possible to distinguish between causal effects of expected policy changes on one hand and (unexpected) shocks on the other.
Model Identification and Nonunique Structure
 University of Oxford
, 2002
"... Identification is an essential attribute of any model's parameters, so we consider its three aspects of `uniqueness', `correspondence to reality' and `interpretability'. Observationallyequivalent overidentified models can coexist, and are mutually encompassing in the population; correctlyidenti ..."
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Identification is an essential attribute of any model's parameters, so we consider its three aspects of `uniqueness', `correspondence to reality' and `interpretability'. Observationallyequivalent overidentified models can coexist, and are mutually encompassing in the population; correctlyidentified models need not correspond to the underlying structure; and may be wrongly interpreted. That a given model is overidentified with all overidentifying restrictions valid (even asymptotically) is insufficient to demonstrate that it is a unique representation. Moreover, structure (as invariance under extended information) need not be identifiable. We consider the role of structural breaks to discriminate between such representations.