scholarly journals Spatial interdependence and instrumental variable models

2019 ◽  
Vol 8 (4) ◽  
pp. 646-661 ◽  
Author(s):  
Timm Betz ◽  
Scott J. Cook ◽  
Florian M. Hollenbach
Keyword(s):  
Instrumental Variable ◽  
Ordinary Least Squares ◽  
Economic Shocks ◽  
Specific Kind ◽  
Estimation Strategy ◽  
Weighted Averages ◽  
Outcome Interdependence ◽  
Spatial Interdependence ◽  
Biased Estimates ◽  
General Estimation

AbstractInstrumental variable (IV) methods are widely used to address endogeneity concerns. Yet, a specific kind of endogeneity – spatial interdependence – is regularly ignored. We show that ignoring spatial interdependence in the outcome results in asymptotically biased estimates even when instruments are randomly assigned. The extent of this bias increases when the instrument is also spatially clustered, as is the case for many widely used instruments: rainfall, natural disasters, economic shocks, and regionally- or globally-weighted averages. Because the biases due to spatial interdependence and predictor endogeneity can offset, addressing only one can increase the bias relative to ordinary least squares. We demonstrate the extent of these biases both analytically and via Monte Carlo simulation. Finally, we discuss a general estimation strategy – S-2SLS – that accounts for both outcome interdependence and predictor endogeneity, thereby recovering consistent estimates of predictor effects.

scholarly journals Spatial Interdependence and Instrumental Variable Models

2017 ◽  
Author(s):  
Timm Betz ◽  
Scott J Cook ◽  
Florian M Hollenbach
Keyword(s):  
Monte Carlo Simulation ◽  
Instrumental Variable ◽  
Economic Shocks ◽  
Specific Kind ◽  
Estimation Strategy ◽  
Weighted Averages ◽  
Spatially Distributed ◽  
Simple Estimation ◽  
Spatial Interdependence ◽  
Biased Estimates

Instrumental variable (IV) methods are widely used to address endogeneity concerns in re- search using observational data. Yet, a specific kind of endogeneity – spatial interdependence – is regularly ignored in this research, threatening claims of causal identification. We show that ignoring spatial interdependence results in asymptotically biased estimates, even when in- struments are randomly assigned. The extent of this bias increases when the instrument is also spatially distributed, which is the case for most widely-used instruments (such as rainfall, nat- ural disasters, economic shocks, regionally- or globally-weighted averages, etc.). We demon- strate the extent of these biases both analytically and via Monte Carlo simulation. Finally, we discuss a simple estimation strategy that can be employed to recover consistent estimates of the desired effects.

scholarly journals ESTIMATION OF A SEMIPARAMETRIC IGARCH(1,1) MODEL

2010 ◽  
Vol 27 (3) ◽  
pp. 639-661 ◽  
Author(s):  
Woocheol Kim ◽  
Oliver Linton
Keyword(s):  
Rate Of Convergence ◽  
Instrumental Variable ◽  
Functional Form ◽  
Variable Method ◽  
Estimation Strategy ◽  
Instrumental Variable Method ◽  
The Difference

We propose a semiparametric IGARCH model that allows for persistence in variance but also allows for more flexible functional form. We assume that the difference of the squared process is weakly stationary. We propose an estimation strategy based on the nonparametric instrumental variable method. We establish the rate of convergence of our estimator.

Intergenerational Income Elasticities, Instrumental Variable Estimation, and Bracketing Strategies

2020 ◽  
Vol 50 (1) ◽  
pp. 1-46
Author(s):  
Pablo A. Mitnik
Keyword(s):  
Instrumental Variable ◽  
Income Elasticity ◽  
Geometric Mean ◽  
Panel Study ◽  
Generalized Method Of Moments ◽  
Ordinary Least Squares ◽  
Point Estimate ◽  
Set Estimation ◽  
Iv Estimation ◽  
Short Run

The fact that the intergenerational income elasticity (IGE)—the workhorse measure of economic mobility—is defined in terms of the geometric mean of children’s income generates serious methodological problems. This has led to a call to replace it with the IGE of the expectation, which requires developing the methodological knowledge necessary to estimate the latter with short-run measures of income. This article contributes to this aim. The author advances a “bracketing strategy” for the set estimation of the IGE of the expectation that is equivalent to that used to set estimate (rather than point estimate) the conventional IGE with estimates obtained with the ordinary least squares and instrumental variable (IV) estimators. The proposed bracketing strategy couples estimates generated with the Poisson pseudo–maximum likelihood estimator and a generalized method of moments IV estimator of the Poisson or exponential regression model. The author develops a generalized error-in-variables model for the IV estimation of the IGE of the expectation and compares it with the corresponding model underlying the IV estimation of the conventional IGE. By considering both bracketing strategies from the perspective of the partial-identification approach to inference, the author specifies how to construct confidence intervals for the IGEs, in particular when the upper bound is estimated more than once with different sets of instruments. Finally, using data from the Panel Study of Income Dynamics, the author shows that the bracketing strategies work as expected and assesses the information they generate and how this information varies across instruments and short-run measures of parental income. Three computer programs made available as companions to the article make the set estimation of IGEs, and statistical inference, very simple endeavors.

scholarly journals Evaluation of statistical methods used in the analysis of interrupted time series studies: a simulation study

2020 ◽  
Author(s):  
Simon L Turner ◽  
Andrew B Forbes ◽  
Amalia Karahalios ◽  
Monica Taljaard ◽  
Joanne E McKenzie
Keyword(s):  
Time Series ◽  
Statistical Methods ◽  
Simulated Data ◽  
Population Level ◽  
Interrupted Time Series ◽  
Ordinary Least Squares ◽  
In Series ◽  
Time Series Studies ◽  
Unbiased Estimates ◽  
Biased Estimates

AbstractInterrupted time series (ITS) studies are frequently used to evaluate the effects of population-level interventions or exposures. To our knowledge, no studies have compared the performance of different statistical methods for this design. We simulated data to compare the performance of a set of statistical methods under a range of scenarios which included different level and slope changes, varying lengths of series and magnitudes of autocorrelation. We also examined the performance of the Durbin-Watson (DW) test for detecting autocorrelation. All methods yielded unbiased estimates of the level and slope changes over all scenarios. The magnitude of autocorrelation was underestimated by all methods, however, restricted maximum likelihood (REML) yielded the least biased estimates. Underestimation of autocorrelation led to standard errors that were too small and coverage less than the nominal 95%. All methods performed better with longer time series, except for ordinary least squares (OLS) in the presence of autocorrelation and Newey-West for high values of autocorrelation. The DW test for the presence of autocorrelation performed poorly except for long series and large autocorrelation. From the methods evaluated, OLS was the preferred method in series with fewer than 12 points, while in longer series, REML was preferred. The DW test should not be relied upon to detect autocorrelation, except when the series is long. Care is needed when interpreting results from all methods, given confidence intervals will generally be too narrow. Further research is required to develop better performing methods for ITS, especially for short series.

scholarly journals The Effect of Job–Education Vertical Mismatch on Wages Among Recent PhD Graduates: Evidence From an Instrumental Variable Analysis

2021 ◽  
Author(s):  
Giuseppe Lucio Gaeta ◽  
Giuseppe Lubrano Lavadera ◽  
Francesco Pastore
Keyword(s):  
Instrumental Variable ◽  
Empirical Studies ◽  
Ordinary Least Squares ◽  
Micro Data ◽  
Cross Sectional ◽  
Instrumental Variable Analysis ◽  
Wage Effect ◽  
And Gender ◽  
The Impact ◽  
Variable Analysis

Abstract Existing studies suggest that recent PhD graduates with a job vertically mismatched with their education tend to earn lower wages than their matched counterparts. However, by being based on cross-sectional ordinary least squares (OLS) estimates, these studies raise endogeneity concerns and can only be considered evidence of a correlation between vertical mismatch and wages. This paper improves this literature by applying a heteroskedasticity-based instrumental variable estimation approach to analyzing Italian PhD holders’ cross-sectional micro-data. Our analysis suggests that previous empirical studies have provided slightly upward estimates of the impact of vertical mismatch on wages. Nevertheless, our results show that the effect of overeducation on wages is sizeable. However, no wage effect is found for overskilling. The heterogeneity of these findings by field of study and gender are also inspected.

Geographic Dispersion of Economic Shocks: Evidence from the Fracking Revolution: Comment

2020 ◽  
Vol 110 (6) ◽  
pp. 1905-1913 ◽  
Author(s):  
Alexander G. James ◽  
Brock Smith
Keyword(s):  
Energy Production ◽  
Spatial Dispersion ◽  
Spatial Aggregation ◽  
Economic Shocks ◽  
Geographic Dispersion ◽  
Estimation Strategy ◽  
Propagation Effects

Feyrer, Mansur, and Sacerdote (2017) estimates the spatial dispersion of the effects of the recent shale-energy boom by unconditionally regressing income and employment on energy production at various levels of geographic aggregation. However, producing counties tend to be located near each other and receive inward spillovers from neighboring production. This inflates the estimated effect of own-county production and spatial aggregation does not address this. We propose an alternative estimation strategy that accounts for these spillovers and identify reduced propagation effects. The proposed estimation strategy can be applied more generally to estimate the dispersion of multiple, simultaneously occurring economic shocks. (JEL E24, E32, J31, Q35, Q43, R11, R23)

scholarly journals Avoiding Invalid Instruments and Coping with Weak Instruments

2006 ◽  
Vol 20 (4) ◽  
pp. 111-132 ◽  
Author(s):  
Michael P Murray
Keyword(s):  
Instrumental Variable ◽  
Ordinary Least Squares ◽  
Paper Briefly ◽  
Weak Instruments ◽  
Instrumental Variable Estimation ◽  
Invalid Instruments ◽  
Valid Instrument ◽  
Disturbance Term ◽  
And Coping ◽  
Instrumental Variable Estimator

Archimedes said, “Give me the place to stand, and a lever long enough, and I will move the Earth.” Economists have their own powerful lever: the instrumental variable estimator. The instrumental variable estimator can avoid the bias that ordinary least squares suffers when an explanatory variable in a regression is correlated with the regression's disturbance term. But, like Archimedes' lever, instrumental variable estimation requires both a valid instrument on which to stand and an instrument that isn't too short (or “too weak”). This paper briefly reviews instrumental variable estimation, discusses classic strategies for avoiding invalid instruments (instruments themselves correlated with the regression's disturbances), and describes recently developed strategies for coping with weak instruments (instruments only weakly correlated with the offending explanator).

The impact of government ownership on the cost of debt and valuation of Vietnamese listed companies

2020 ◽  
Vol 32 (2) ◽  
pp. 255-270
Author(s):  
Ben Le
Keyword(s):  
Instrumental Variable ◽  
Ordinary Least Squares ◽  
Firm Valuation ◽  
The Other ◽  
Cost Of Debt ◽  
Government Ownership ◽  
Content Type ◽  
The Cost ◽  
The Impact ◽  
The Relationship

Purpose This paper aims to examine the impact of government ownership on the cost of debt and firm valuation in listed Vietnamese companies for the period 2007 to 2016. Design/methodology/approach The authors use both the generalised methods of the moment (GMM) and the ordinary least squares (OLS) regressions to analyse a panel data spanning over the period 2007 to 2016 in the markets of Vietnam. Further, the instrumental variable is used in the paper. Findings The authors find that firms with relative higher government stockholdings or state-owned companies where the government owns 50 per cent or more of shares outstanding enjoy a lower cost of debt compared to the other firms. Consequently, these firms have higher firm valuation and profitability. The results are robust for both the GMM and the OLS regressions. Further, firms that no longer retain government ownership have a higher cost of debt than the other firms. The results of the paper imply the importance of political connections in businesses in the market of Vietnam. Originality/value This paper connects the relationship between government ownership and the cost of debt with the relationship between government ownership and firm valuation. The paper tests the relationship between the cost of debt and government ownership using both OLS and GMM specifications and the results are robust for both approaches. The manuscript uses an instrumental variable to show that government ownership has a positive impact on higher firm performance through reducing cost of debt. Further, this paper addresses the possible issue of endogeneity.

Linear regression models for biomass table construction, using cluster samples

1989 ◽  
Vol 19 (5) ◽  
pp. 664-673 ◽  
Author(s):  
Andrew J. R. Gillespie ◽  
Tiberius Cunia
Keyword(s):  
Least Squares ◽  
Regression Models ◽  
Predictor Variable ◽  
Ordinary Least Squares ◽  
Cluster Sampling ◽  
Parameter Estimates ◽  
Least Squares Regression ◽  
Linear Regression Models ◽  
Estimation Procedures ◽  
Biased Estimates

Biomass tables are often constructed from cluster samples by means of ordinary least squares regression estimation procedures. These procedures assume that sample observations are uncorrelated, which ignores the intracluster correlation of cluster samples and results in underestimates of the model error. We tested alternative estimation procedures by simulation under a variety of cluster sampling methods, to determine combinations of sampling and estimation procedures that yield accurate parameter estimates and reliable estimates of error. Modified, generalized, and jack-knife least squares procedures gave accurate parameter and error estimates when sample trees were selected with equal probability. Regression models that did not include height as a predictor variable yielded biased parameter estimates when sample trees were selected with probability proportional to tree size. Models that included height did not yield biased estimates. There was no discernible gain in precision associated with sampling with probability proportional to size. Random coefficient regressions generally gave biased point estimates with poor precision, regardless of sampling method.

scholarly journals Explaining Causal Findings Without Bias: Detecting and Assessing Direct Effects

2016 ◽  
Vol 110 (3) ◽  
pp. 512-529 ◽  
Author(s):  
AVIDIT ACHARYA ◽  
MATTHEW BLACKWELL ◽  
MAYA SEN
Keyword(s):  
Direct Effect ◽  
Variance Estimator ◽  
Direct Effects ◽  
Estimation Strategy ◽  
Causal Pathway ◽  
The Impact ◽  
Biased Estimates ◽  
Important Objective ◽  
Original Treatment ◽  
Rule Out

Researchers seeking to establish causal relationships frequently control for variables on the purported causal pathway, checking whether the original treatment effect then disappears. Unfortunately, this common approach may lead to biased estimates. In this article, we show that the bias can be avoided by focusing on a quantity of interest called the controlled direct effect. Under certain conditions, the controlled direct effect enables researchers to rule out competing explanations—an important objective for political scientists. To estimate the controlled direct effect without bias, we describe an easy-to-implement estimation strategy from the biostatistics literature. We extend this approach by deriving a consistent variance estimator and demonstrating how to conduct a sensitivity analysis. Two examples—one on ethnic fractionalization’s effect on civil war and one on the impact of historical plough use on contemporary female political participation—illustrate the framework and methodology.

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