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Journal ArticleDOI

Sargan's Intrumental Variables Estimation and the Generalized Method of Moments

Manuel Arellano
- 01 Oct 2002 - 
- Vol. 20, Iss: 4, pp 450-459
TLDR
Sargan's work on instrumental variables (IV) estimation and its connections with the generalized method of moments (GMM) is surveyed in this paper, where the authors present the modeling context in which Sargan motivated IV estimation and their results for nonlinear-in-parameters IV models are described.
Abstract
This article surveys J. D. Sargan's work on instrumental variables (IV) estimation and its connections with the generalized method of moments (GMM). First the modeling context in which Sargan motivated IV estimation is presented. Then the theory of IV estimation as developed by Sargan is discussed. His approach to efficiency, his minimax estimator, tests of overidentification and underidentification, and his later work on the finite-sample properties of IV estimators are reviewed. Next, his approach to modeling IV equations with serial correlation is discussed and compared with the GMM approach. Finally, Sargan's results for nonlinear-in-parameters IV models are described.

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Citations
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Treatment effect heterogeneity in theory and practice

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Treatment Effect Heterogeneity in Theory and Practice

TL;DR: In this paper, a theoretical framework that nests all possible homogeneity assumptions for a causal treatment-effects model with a binary instrument is presented. But the authors make it clear that efforts to go from local average treatment effects (LATE) to population average treatment effect are inherently speculative.
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Greenhouse gases emissions, growth and the energy mix in Europe

TL;DR: In this article, the impact of energy consumption on emissions would depend on the primary energy mix and on the final use of this energy, and they considered both factors in the model.
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Intra- and extra-bank determinants of Latin American Banks' profitability

TL;DR: In this paper, the authors find evidence of several major relationships involving bank profitability, including: 1) an inverse U-shaped relationship between banks' capital ratios and profitability, 2) a positive relationship between asset diversification (e.g. security trading, hedge funds, foreign exchange, assurance, etc.) and profitability; 3) a negative relationship between revenue diversification and profitability.
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Nobel Lecture: Uncertainty Outside and Inside Economic Models

TL;DR: In this paper, a risk premium is defined as the price of risk and the degree of exposure to risk in a decentralized security market, and the risk premium reflects both the price and exposure of a security to macroeconomic shocks.
References
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Journal ArticleDOI

The estimation of economic relationships using instrumental variables

John Denis Sargan
- 01 Jul 1958 - 
TL;DR: In this article, the asymptotic error variance matrix for the coefficients of one of the relationships is obtained in the case in which these relationships are estimated using instrumental variables, and the problem of choice that arises when there are more instrumental variables available than the minimum number required to enable the method to be used is discussed.
Journal ArticleDOI

Forward Exchange Rates as Optimal Predictors of Future Spot Rates: An Econometric Analysis

TL;DR: In this article, the authors examined the hypothesis that the expected rate of return to speculation in the forward foreign exchange market is zero; that is, the logarithm of the forward exchange rate is the market's conditional expectation of the future spot rate, and they were able to reject the simple market efficiency hypothesis for exchange rates from the 1970s and the 1920s.
Journal ArticleDOI

The Method of Path Coefficients

TL;DR: The Method of Path Coefficients (MPC) as discussed by the authors is a flexible means of relating the correlation coefficients between variables in a multiple system to the functional relations among them, which has been applied in quite a variety of cases.
Journal ArticleDOI

Estimation of the Parameters of a Single Equation in a Complete System of Stochastic Equations

TL;DR: In this article, a method is given for estimating the coefficients of a single equation in a complete system of linear stochastic equations (see expression (2.1)), provided that a number of coefficients of the selected equation are known to be zero.
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