Results 1  10
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46
Some Impossibility Theorems In Econometrics With Applications To Instrumental Variables, Dynamic Models And Cointegration
 Econometrica
, 1995
"... General characterizations of valid confidence sets and tests in problems which involve locally almost unidentified (LAU) parameters are provided and applied to several econometric models. Two types of inference problems are studied: (1) inference about parameters which are not identifiable on certai ..."
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Cited by 124 (16 self)
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General characterizations of valid confidence sets and tests in problems which involve locally almost unidentified (LAU) parameters are provided and applied to several econometric models. Two types of inference problems are studied: (1) inference about parameters which are not identifiable on certain subsets of the parameter space, and (2) inference about parameter transformations with singularities (discontinuities). When a LAU parameter or parametric function has an unbounded range, it is shown under general regularity conditions that any valid confidence set with level 1 \Gamma ff for this parameter should be unbounded with probability close to 1 \Gamma ff in the neighborhood of nonidentification subsets and should as well have a nonzero probability of being unbounded under any distribution compatible with the model: no valid confidence set which is bounded with probability one does exist. These properties hold even if "identifying restrictions" are imposed. Similar results also ob...
GMM with weak identification
 Econometrica
, 2000
"... This paper develops asymptotic distribution theory for GMM estimators and test statistics when some or all of the parameters are weakly identified. General results are obtained and are specialized to two important cases: linear instrumental variables regression and Euler equations estimation of the ..."
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Cited by 56 (2 self)
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This paper develops asymptotic distribution theory for GMM estimators and test statistics when some or all of the parameters are weakly identified. General results are obtained and are specialized to two important cases: linear instrumental variables regression and Euler equations estimation of the CCAPM. Numerical results for the CCAPM demonstrate that weakidentification asymptotics explains the breakdown of conventional GMM procedures documented in previous Monte Carlo studies. Confidence sets immune to weak identification are proposed. We use these results to inform an empirical investigation of various CCAPM specifications; the substantive conclusions reached differ from those obtained using conventional methods.
An Axiomatic Characterization of Causal Counterfactuals
, 1998
"... This paper studies the causal interpretation of counterfactual sentences using a modifiable structural equation model. It is shown that two properties of counterfactuals, namely, composition and effectiveness, are sound and complete relative to this interpretation, when recursive (i.e., feedback ..."
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Cited by 47 (19 self)
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This paper studies the causal interpretation of counterfactual sentences using a modifiable structural equation model. It is shown that two properties of counterfactuals, namely, composition and effectiveness, are sound and complete relative to this interpretation, when recursive (i.e., feedbackless) models are considered. Composition and effectiveness also hold in Lewis's closestworld semantics, which implies that for recursive models the causal interpretation imposes no restrictions beyond those embodied in Lewis's framework. A third property, called reversibility, holds in nonrecursive causal models but not in Lewis's closestworld semantics, which implies that Lewis's axioms do not capture some properties of systems with feedback. Causal inferences based on counterfactual analysis are exemplified and compared to those based on graphical models.
Identification, Weak Instruments, and Statistical Inference in Econometrics
 JOURNAL OF ECONOMICS
, 2003
"... ..."
Instrumental Variables Regression With Independent Observations
, 1982
"... INTRODUCTION THE METHOD OF INSTRUMENTAL VARIABLES (IV), proposed independently by Reiersol [15] and Geary [4], is one of the most useful classes of estimation techniques available to econometricians. The method is particularly useful in the context of errorsinvariables and simultaneous equation p ..."
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Cited by 32 (0 self)
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INTRODUCTION THE METHOD OF INSTRUMENTAL VARIABLES (IV), proposed independently by Reiersol [15] and Geary [4], is one of the most useful classes of estimation techniques available to econometricians. The method is particularly useful in the context of errorsinvariables and simultaneous equation problems, and it ineludes ordinary least squares (OLS), twostage least squares (2SLS) (Klein [9]), threestage least squares (3SLS) (Madansky [12]) and certain fullinformation maximum likelihood estimators (Hausman [7]) as special cases. To date, the definitive theoretical treatment of the instrumental variables method is that of Sargan [16]. Important contributions have also been made by Brundy and Jorgenson [!, 2]. However, this work has not established the properties of IV estimators for all of the kinds of data analyzed by economists. Sargan's work is appropriate for data which are stochastic processes of the kind considered by Koopmans, Rubin, and Leipnik {10] in their study of dynami
An Extended Class of Instrumental Variables for the Estimation of Causal Effects
 UCSD DEPT. OF ECONOMICS DISCUSSION PAPER
, 1996
"... This paper builds on the structural equations, treatment effect, and machine learning literatures to provide a causal framework that permits the identification and estimation of causal effects from observational studies. We begin by providing a causal interpretation for standard exogenous regresso ..."
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Cited by 32 (13 self)
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This paper builds on the structural equations, treatment effect, and machine learning literatures to provide a causal framework that permits the identification and estimation of causal effects from observational studies. We begin by providing a causal interpretation for standard exogenous regressors and standard “valid” and “relevant” instrumental variables. We then build on this interpretation to characterize extended instrumental variables (EIV) methods, that is methods that make use of variables that need not be valid instruments in the standard sense, but that are nevertheless instrumental in the recovery of causal effects of interest. After examining special cases of single and double EIV methods, we provide necessary and sufficient conditions for the identification of causal effects by means of EIV and provide consistent and asymptotically normal estimators for the effects of interest.
A new identification condition for recursive models with correlated errors
 Struct. Equ. Model
, 2002
"... This article establishes a new criterion for the identification of recursive linear models in which some errors are correlated. We show that identification is ensured as long as error correlation does not exist between a cause and its direct effect; no restrictions are imposed on errors associated w ..."
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Cited by 17 (0 self)
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This article establishes a new criterion for the identification of recursive linear models in which some errors are correlated. We show that identification is ensured as long as error correlation does not exist between a cause and its direct effect; no restrictions are imposed on errors associated with indirect causes. Before structural equation models (SEM) can be estimated and evaluated against data, a researcher must make sure that the parameters of the estimated model are identified, namely, that they can be determined uniquely from the population covariance matrix. The importance of testing identification prior to data analysis is summarized succinctly by Rigdon (1995): To avoid devoting research resources toward a hopeless cause (and to avoid ignoring productive research avenues out of an unfounded fear of underidentification), researchers need a way to quickly evaluate a model's identification status before data are collected. Furthermore, because models are often altered in the course of research (Joreskog, 1993), researchers need a technique that helps them understand the impact of potential structural changes on the identification status of the model, (p. 359) It is well known that, in recursive path models with correlated errors, the identification problem is unsolved. In other words, we are not in possession of a necessary and sufficient criterion for deciding whether the parameters in such a model can be computed from the population covariance matrix of the observed variables. Certain restricted classes of models are nevertheless known to be identifiable, and
Asset Prices and Exchange Rates
 Mimeo, MIT Rigobon, R. (2003), “Identification Through Heteroskedasticity,” Review of Economics and Statistics
, 2004
"... This paper develops a simple twocountry, twogood model, in which the real exchange rate, stock and bond prices are jointly determined. The model predicts that stock market prices are correlated internationally even though their dividend processes are independent, providing a theoretical argument i ..."
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Cited by 14 (4 self)
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This paper develops a simple twocountry, twogood model, in which the real exchange rate, stock and bond prices are jointly determined. The model predicts that stock market prices are correlated internationally even though their dividend processes are independent, providing a theoretical argument in favor of financial contagion. The foreign exchange market serves as a propagation channel from one stock market to the other. The model identifies interconnections between stock, bond and foreign exchange markets and characterizes their joint dynamics as a threefactor model. Contemporaneous responses of each market to changes in the factors are shown to have unambiguous signs. These implications enjoy strong empirical support. Estimation of various versions of the model reveals that most of the signs predicted by the model indeed obtain in the data, and the point estimates are in line with the implications of our theory. Furthermore, the uncovered interest rate parity relationship has a risk premium term in our model, shown to be volatile. We also derive agents ’ portfolio holdings and identify economic environments under which they exhibit a home bias, and demonstrate that an international CAPM obtaining in our model has two additional factors.
A Parallel CuttingPlane Algorithm for the Vehicle Routing Problem With Time Windows
, 1999
"... In the vehicle routing problem with time windows a number of identical vehicles must be routed to and from a depot to cover a given set of customers, each of whom has a specified time interval indicating when they are available for service. Each customer also has a known demand, and a vehicle may on ..."
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Cited by 11 (1 self)
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In the vehicle routing problem with time windows a number of identical vehicles must be routed to and from a depot to cover a given set of customers, each of whom has a specified time interval indicating when they are available for service. Each customer also has a known demand, and a vehicle may only serve the customers on a route if the total demand does not exceed the capacity of the vehicle. The most effective solution method proposed to date for this problem is due to Kohl, Desrosiers, Madsen, Solomon, and Soumis. Their algorithm uses a cuttingplane approach followed by a branchand bound search with column generation, where the columns of the LP relaxation represent routes of individual vehicles. We describe a new implementation of their method, using Karger's randomized minimumcut algorithm to generate cutting planes. The standard benchmark in this area is a set of 87 problem instances generated in 1984 by M. Solomon; making using of parallel processing in both the cuttingpla...
Identifying linear causal effects
 In Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI
, 2004
"... This paper concerns the assessment of linear causeeffect relationships from a combination of observational data and qualitative causal structures. The paper shows how techniques developed for identifying causal effects in causal Bayesian networks can be used to identify linear causal effects, and t ..."
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Cited by 9 (4 self)
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This paper concerns the assessment of linear causeeffect relationships from a combination of observational data and qualitative causal structures. The paper shows how techniques developed for identifying causal effects in causal Bayesian networks can be used to identify linear causal effects, and thus provides a new approach for assessing linear causal effects in structural equation models. Using this approach the paper develops a systematic procedure for recognizing identifiable direct causal effects.