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Causal inference with observational data AgEcon
Nichols, Austin.
Problems with inferring causal relationships from nonexperimental data are briefly reviewed, and four broad classes of methods designed to allow estimation of and inference about causal parameters are described: panel regression, matching or reweighting, instrumental variables, and regression discontinuity. Practical examples are offered, and discussion focuses on checking required assumptions to the extent possible.
Tipo: Article Palavras-chave: Xtreg; Psmatch2; Nnmatch; Ivreg; Ivreg2; Ivregress; Rd; Lpoly; Xtoverid; Ranktest; Causal inference; Match; Matching; Reweighting; Propensity score; Panel; Instrumental variables; Excluded instrument; Weak identification; Regression; Discontinuity; Local polynomial; Research Methods/ Statistical Methods.
Ano: 2007 URL: http://purl.umn.edu/119292
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Do Production Contracts Raise Farm Productivity? An Instrumental Variables Approach AgEcon
Key, Nigel D.; McBride, William D..
Estimating how the use of production contracts affects farm productivity is difficult when unobservable factors are correlated with both the decision to contract and productivity. To account for potential selection bias, this study uses the local availability of production contracts as an instrument for whether a farm uses a contract in order to estimate the impact of contract use on total factor productivity. Results indicate that use of a production contract is associated with a large increase in productivity for feeder-to-finish hog farms in the United States. The instrumental variable method makes it credible to assert that the observed association is a causal relationship rather than simply a correlation.
Tipo: Journal Article Palavras-chave: Productivity; Production contracts; Instrumental variables; Sample selection; Productivity Analysis.
Ano: 2008 URL: http://purl.umn.edu/45659
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Does Farm Size Really Converge? The Role of Unobserved Farm Efficiency AgEcon
Dolev, Yuval; Kimhi, Ayal.
We analyze the growth of family farms in Israeli cooperative villages between 1981 and 1995, using longitudinal data. We use instrumental variables to account for the endogeneity of initial farm size, and correct for selectivity due to farm survival. We also include a technical efficiency index, derived from the estimation of a stochastic frontier production model, as an explanatory variable. We find that technical efficiency is an important determinant of farm growth, and that not controlling for technical efficiency could seriously bias the results. The size distribution of Israeli family farms is found to be mostly diverging, while without technical efficiency farm growth seemed to be predominantly random.
Tipo: Working or Discussion Paper Palavras-chave: Farm size; Farm growth; Farm survival; Instrumental variables; Sample selection; Technical efficiency; Farm Management; Productivity Analysis; Q12; L25; C34.
Ano: 2008 URL: http://purl.umn.edu/45778
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Enhanced routines for instrumental variables/generalized method of moments estimation and testing AgEcon
Baum, Christopher F.; Schaffer, Mark E.; Stillman, Steven.
We extend our 2003 paper on instrumental variables and generalized method of moments estimation, and we test and describe enhanced routines that address heteroskedasticity- and autocorrelation-consistent standard errors, weak instruments, limited-information maximum likelihood and k-class estimation, tests for endogeneity and Ramsey’s regression specification-error test, and autocorrelation tests for instrumental variable estimates and panel-data instrumental variable estimates.
Tipo: Article Palavras-chave: Ivactest; Ivendog; Ivhettest; Ivreg2; Ivreset; Overid; Ranktest; Instrumental variables; Weak instruments; GMM; Endogeneity; Heteroskedasticity; Serial correlation; HAC standard errors; LIML; CUE; Overidentifying restrictions; Frisch–Waugh–Lovell theorem; RESET; Cumby–Huizinga test; Research Methods/ Statistical Methods.
Ano: 2007 URL: http://purl.umn.edu/119291
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Erratum and discussion of propensity–score reweighting AgEcon
Nichols, Austin.
Tipo: Article Palavras-chave: Xtreg; Psmatch2; Nnmatch; Ivreg; Ivreg2; Ivregress; Rd; Lpoly; Xtoverid; Ranktest; Causal inference; Match; Matching; Reweighting; Propensity score; Panel; Instrumental variables; Excluded instrument; Weak identification; Regression; Discontinuity; Local polynomial; Research Methods/ Statistical Methods.
Ano: 2008 URL: http://purl.umn.edu/122619
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From the help desk: Bootstrapped standard errors AgEcon
Guan, Weihua.
Bootstrapping is a nonparametric approach for evaluating the distribution of a statistic based on random resampling. This article illustrates the bootstrap as an alternative method for estimating the standard errors when the theoretical calculation is complicated or not available in the current software.
Tipo: Journal Article Palavras-chave: Bootstrap; Cluster; Nl; Instrumental variables; Research Methods/ Statistical Methods.
Ano: 2003 URL: http://purl.umn.edu/116034
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Implementing tests with correct size in the simultaneous equations model AgEcon
Moreira, Marcelo J.; Poi, Brian P..
In this paper, we propose a fix to the size distortions of tests for structural parameters in the simultaneous equations model by computing critical value functions based on the conditional distribution of test statistics. The conditional tests can then be used to construct informative confidence regions for the structural parameter with correct coverage probability. Commands to implement these tests in Stata are also introduced.
Tipo: Journal Article Palavras-chave: Instrumental variables; Weak instruments; Similar tests; Score test; Wald test; Likelihood-ratio test; Confidence regions; 2SLS estimator; LIML estimator; Research Methods/ Statistical Methods.
Ano: 2002 URL: http://purl.umn.edu/116032
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Instrumental variables and GMM: Estimation and testing AgEcon
Baum, Christopher F.; Schaffer, Mark E.; Stillman, Steven.
We discuss instrumental variables (IV) estimation in the broader context of the generalized method of moments (GMM), and describe an extended IV estimation routine that provides GMM estimates as well as additional diagnostic tests. Stand-alone test procedures for heteroskedasticity, overidentification, and endogeneity in the IV context are also described.
Tipo: Journal Article Palavras-chave: Instrumental variables; Generalized method of moments; Endogeneity; Heteroskedasticity; Overidentifying restrictions; Clustering; Intra-group correlation; Research Methods/ Statistical Methods.
Ano: 2002 URL: http://purl.umn.edu/116029
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Instrumental variables, bootstrapping, and generalized linear models AgEcon
Hardin, James W.; Schmiediche, Henrik; Carroll, Raymond J..
This paper discusses and illustrates the qvf command for fitting generalized linear models. The differences between this new command and Stata’s glm command are highlighted. One of the most notable features of the qvf command is its ability to include instrumental variables. This functionality was added specifically to address measurement error but may be utilized by the user for other purposes. The qvf command was developed in the C-language using Stata’s new plugin features and executes much faster than the glm ado-file.
Tipo: Journal Article Palavras-chave: Measurement error; Instrumental variables; Murphy–Topel; Bootstrap; Generalized linear models; Research Methods/ Statistical Methods.
Ano: 2003 URL: http://purl.umn.edu/116178
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Jackknife instrumental variables estimation in Stata AgEcon
Poi, Brian P..
The two-stage least-squares (2SLS) instrumental variables estimator is commonly used to address endogeneity. However, the estimator suffers from bias that is exacerbated when the instruments are only weakly correlated with the endogenous variables and when many instruments are used. In this article, I discuss jackknife instrumental variables estimation as an alternative to 2SLS. Monte Carlo simulations comparing the jackknife instrument variables estimators to 2SLS and limited information maximum likelihood (LIML) show that two of the four variants perform remarkably well even when 2SLS does not. In a weak-instrument experiment, the two best performing jackknife estimators also outperform LIML.
Tipo: Journal Article Palavras-chave: Jive; 2SLS; LIML; JIVE; Instrumental variables; Endogeneity; Weak instruments; Research Methods/ Statistical Methods.
Ano: 2006 URL: http://purl.umn.edu/117586
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Nonlinearity in the Return to Education AgEcon
Trostel, Philip A..
This study estimates marginal rates of return to investment in schooling in 12 countries. Significant systematic nonlinearity in the marginal rate of return is found. In particular, the marginal rate of return is increasing significantly at low levels of education, and decreasing significantly at high levels of education. This may help explain why estimates of the return to schooling are often considerably higher when instrumenting for education.
Tipo: Journal Article Palavras-chave: Return to education; Nonlinearity; Instrumental variables; I20; J24.
Ano: 2005 URL: http://purl.umn.edu/37550
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ON THE EMPIRICAL FINDING OF A HIGHER RISK OF POVERTY IN RURAL AREAS: IS RURAL RESIDENCE ENDOGENOUS TO POVERTY? AgEcon
Fisher, Monica G..
Research shows households are more likely to be poor in rural versus urban America. Does this phenomenon partly reflect that people who choose rural residence have unmeasured attributes related to human impoverishment? To address this, two models are estimated using Panel Study of Income Dynamics data. A single equation Probit model of household poverty replicates the well-documented finding of higher poverty risk in rural places. However, a two-stage instrumental variables approach accounting for residential choice finds no measured effect of rural location on poverty. Results suggest failure to correct for endogenous rural residence leads to over-estimation of the "rural effect".
Tipo: Working or Discussion Paper Palavras-chave: Endogeneity; Households; Instrumental variables; Poverty; Rural; Food Security and Poverty.
Ano: 2004 URL: http://purl.umn.edu/18917
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On the Empirical Finding of a Higher Risk of Poverty in Rural Areas: Is Rural Residence Endogenous to Poverty? AgEcon
Fisher, Monica G..
Includes: On the Empirical Finding of a Higher Risk of Poverty in Rural Areas: Is Rural Residence Endogenous to Poverty?:COMMENT, by Thomas A. Hirschl; On the Empirical Finding of a Higher Risk of Poverty in Rural Areas: Is Rural Residence Endogenous to Poverty?: REPLY, by Monica Fisher. Research shows people are more likely to be poor in rural versus urban America. Does this phenomenon partly reflect that people who choose rural residence have unmeasured attributes related to human impoverishment? To address this question, two models are estimated using Panel Study of Income Dynamics data. A single equation Probit model of individual poverty replicates the well-documented finding of higher poverty risk in rural places. However, an instrumental variables...
Tipo: Journal Article Palavras-chave: Endogeneity; Instrumental variables; Omitted variable bias; Poverty; Rural; Food Security and Poverty.
Ano: 2005 URL: http://purl.umn.edu/31219
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Production Contracts and Farm Business Growth and Survival AgEcon
Key, Nigel D..
In recent decades there has been a substantial increase in the scale of production and the use of production contracts in the hog sector. This paper explores empirically whether these two phenomena are related by examining whether the use of production contracts has allowed finish hog operations to expand in scale. The study takes advantage of recently collected information from the Census of Agriculture that permits a comparisons of individual independent and contract hog producers over time. The study first examines whether operations that used a contract grew at a faster rate or had lower exit rates over the subsequent five-year period than did operations that produced independently, controlling for observable factors. The study then examines how the...
Tipo: Conference Paper or Presentation Palavras-chave: Production contracts; Farm structure; Farm business exit rate; Instrumental variables; Hogs; Agribusiness; Agricultural and Food Policy; Community/Rural/Urban Development; Farm Management; Industrial Organization; Livestock Production/Industries; Marketing; Production Economics.
Ano: 2010 URL: http://purl.umn.edu/61022
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PRODUCTION CONTRACTS AND FARM PRODUCTIVITY: EXAMINING THE LINK USING INSTRUMENTAL VARIABLES AgEcon
Key, Nigel D.; McBride, William D..
Estimating how production contracts affect farm productivity is difficult because the decision to use a contract is endogenous to other decisions affecting productivity. This study uses the local availability of production contracts as an instrument for whether a farm uses a contract in order to estimate the impact of contract use on total factor productivity. Results indicate that use of a production contract is associated with a large increase in productivity for feeder-to-finish hog farms in the U.S. The instrumental variable method makes it credible to assert that the observed association is a causal relationship rather than simply a correlation.
Tipo: Conference Paper or Presentation Palavras-chave: Productivity; Production contracts; Instrumental variables; Sample selection; Farm Management; Productivity Analysis.
Ano: 2007 URL: http://purl.umn.edu/9716
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Spatial Dimensions of US Crop Selection: Recent Responses to Markets and Policy AgEcon
Motamed, Mesbah J.; McPhail, Lihong Lu.
We explicitly measure corn acreage response to the biofuels boom from 2006 to 2010. Specifically, we use newly available micro-scale planting data over time to test whether corn cultivation intensifies in proportion to the proximity of ethanol processors. We control for the endogeneity of plant location to corn acreage by using transportation network data for instruments. Our results show that reducing the distance between a farm and an ethanol plant by one percent increases acreage in corn by 0.64% and reveal a price elasticity of supply of 0.47%. To our knowledge, this is the first study that measures changes in location and intensity of corn planting in response to incentives posed by the recent biofuels boom. The results can serve as a springboard for...
Tipo: Conference Paper or Presentation Palavras-chave: Corn acreage; Ethanol; Panel data analysis; Instrumental variables; Agricultural and Food Policy; Crop Production/Industries; Land Economics/Use; Q1; Q28; C33.
Ano: 2011 URL: http://purl.umn.edu/103270
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Tests and confidence sets with correct size when instruments are potentially weak AgEcon
Mikusheva, Anna; Poi, Brian P..
We consider inference in the linear regression model with one endogenous variable and potentially weak instruments. We construct confidence sets for the coefficient on the endogenous variable by inverting the Anderson–Rubin, Lagrange multiplier, and conditional likelihood-ratio tests. Our confidence sets have correct coverage probabilities even when the instruments are weak. We propose a numerically simple algorithm for finding these confidence sets, and we present a Stata command that supersedes the one presented in Moreira and Poi (Stata Journal 3: 57–70).
Tipo: Journal Article Palavras-chave: Condivreg; Instrumental variables; Weak instruments; Confidence set; Similar test; Research Methods/ Statistical Methods.
Ano: 2006 URL: http://purl.umn.edu/117584
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The regression-calibration method for fitting generalized linear models with additive measurement error AgEcon
Hardin, James W.; Schmiediche, Henrik; Carroll, Raymond J..
This paper discusses and illustrates the method of regression calibration. This is a straightforward technique for fitting models with additive measurement error. We present this discussion in terms of generalized linear models (GLMs) following the notation defined in Hardin and Carroll (2003). Discussion will include specified measurement error, measurement error estimated by replicate error-prone proxies, and measurement error estimated by instrumental variables. The discussion focuses on software developed as part of a small business innovation research (SBIR) grant from the National Institutes of Health (NIH).
Tipo: Journal Article Palavras-chave: Regression calibration; Measurement error; Instrumental variables; Replicate measures; Generalized linear models; Research Methods/ Statistical Methods.
Ano: 2003 URL: http://purl.umn.edu/116180
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The simulation extrapolation method for fitting generalized linear models with additive measurement error AgEcon
Hardin, James W.; Schmiediche, Henrik; Carroll, Raymond J..
We discuss and illustrate the method of simulation extrapolation for fitting models with additive measurement error. We present this discussion in terms of generalized linear models (GLMs) following the notation defined in Hardin and Carroll (2003). As in Hardin, Schmiediche, and Carroll (2003), our discussion includes specified measurement error and measurement error estimated by replicate error-prone proxies. In addition, we discuss and illustrate three extrapolant functions.
Tipo: Journal Article Palavras-chave: Simulation extrapolation; Measurement error; Instrumental variables; Replicate measures; Generalized linear models; Research Methods/ Statistical Methods.
Ano: 2003 URL: http://purl.umn.edu/116184
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Variance estimation for the instrumental variables approach to measurement error in generalized linear models AgEcon
Hardin, James W.; Carroll, Raymond J..
This paper derives and gives explicit formulas for a derived sandwich variance estimate. This variance estimate is appropriate for generalized linear additive measurement error models fitted using instrumental variables. We also generalize the known results for linear regression. As such, this article explains the theoretical justification for the sandwich estimate of variance utilized in the software for measurement error developed under the Small Business Innovation Research Grant (SBIR) by StataCorp. The results admit estimation of variance matrices for measurement error models where there is an instrument for the unknown covariate.
Tipo: Journal Article Palavras-chave: Sandwich estimate of variance; Measurement error; White's estimator; Robust variance; Generalized linear models; Instrumental variables; Research Methods/ Statistical Methods.
Ano: 2003 URL: http://purl.umn.edu/116177
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