Efficient Estimation of Time-Invariant and Rarely Changing Variables in Finite Sample Panel Analyses with Unit Fixed Effects

2007 ◽  
Vol 15 (2) ◽  
pp. 124-139 ◽  
Author(s):  
Thomas Plümper ◽  
Vera E. Troeger
Keyword(s):  
Fixed Effects ◽  
Error Term ◽  
Efficient Estimation ◽  
Fixed Effects Model ◽  
Finite Sample ◽  
Real World Data ◽  
Third Stage ◽  
Finite Sample Properties ◽  
Time Invariant ◽  
Estimation Of Time

This paper suggests a three-stage procedure for the estimation of time-invariant and rarely changing variables in panel data models with unit effects. The first stage of the proposed estimator runs a fixed-effects model to obtain the unit effects, the second stage breaks down the unit effects into a part explained by the time-invariant and/or rarely changing variables and an error term, and the third stage reestimates the first stage by pooled OLS (with or without autocorrelation correction and with or without panel-corrected SEs) including the time-invariant variables plus the error term of stage 2, which then accounts for the unexplained part of the unit effects. We use Monte Carlo simulations to compare the finite sample properties of our estimator to the finite sample properties of competing estimators. In doing so, we demonstrate that our proposed technique provides the most reliable estimates under a wide variety of specifications common to real world data.

Fixed-Effects Vector Decomposition: Properties, Reliability, and Instruments

2011 ◽  
Vol 19 (2) ◽  
pp. 147-164 ◽  
Author(s):  
Thomas Plümper ◽  
Vera E. Troeger
Keyword(s):  
Fixed Effects ◽  
Shrinkage Estimator ◽  
Time Varying ◽  
Finite Sample ◽  
Simultaneous Presence ◽  
Political Analysis ◽  
Vector Decomposition ◽  
Finite Sample Properties ◽  
Time Invariant ◽  
Decomposition Properties

This article reinforces our 2007 Political Analysis publication in demonstrating that the fixed-effects vector decomposition (FEVD) procedure outperforms any other estimator in estimating models that suffer from the simultaneous presence of time-varying variables correlated with unobserved unit effects and time-invariant variables. We compare the finite-sample properties of FEVD not only to the Hausman-Taylor estimator but also to the pretest estimator and the shrinkage estimator suggested by Breusch, Ward, Nguyen and Kompas (BWNK), and Greene in this symposium. Moreover, we correct the discussion of Greene and BWNK of FEVD's asymptotic and finite-sample properties.

scholarly journals Fixed Effects Vector Decomposition: A Magical Solution to the Problem of Time-Invariant Variables in Fixed Effects Models?

2011 ◽  
Vol 19 (2) ◽  
pp. 135-146 ◽  
Author(s):  
William Greene
Keyword(s):  
Fixed Effects ◽  
Ordinary Least Squares ◽  
Fe Model ◽  
Real World Data ◽  
Problem Of Time ◽  
Dummy Variable ◽  
Vector Decomposition ◽  
Step Procedure ◽  
Time Invariant ◽  
Subsequent Literature

Plümper and Troeger (2007) propose a three-step procedure for the estimation of a fixed effects (FE) model that, it is claimed, “provides the most reliable estimates under a wide variety of specifications common to real world data.” Their fixed effects vector decomposition (FEVD) estimator is startlingly simple, involving three simple steps, each requiring nothing more than ordinary least squares (OLS). Large gains in efficiency are claimed for cases of time-invariant and slowly time-varying regressors. A subsequent literature has compared the estimator to other estimators of FE models, including the estimator of Hausman and Taylor (1981) also (apparently) with impressive gains in efficiency. The article also claims to provide an efficient estimator for parameters on time-invariant variables (TIVs) in the FE model. None of the claims are correct. The FEVD estimator simply reproduces (identically) the linear FE (dummy variable) estimator then substitutes an inappropriate covariance matrix for the correct one. The consistency result follows from the fact that OLS in the FE model is consistent. The “efficiency” gains are illusory. The claim that the estimator provides an estimator for the coefficients on TIVs in an FE model is also incorrect. That part of the parameter vector remains unidentified. The “estimator” relies upon a strong assumption that turns the FE model into a type of random effects model.

scholarly journals Not so Harmless After All: The Fixed-Effects Model

2018 ◽  
Vol 27 (1) ◽  
pp. 21-45 ◽  
Author(s):  
Thomas Plümper ◽  
Vera E. Troeger
Keyword(s):  
Model Selection ◽  
Fixed Effects ◽  
Time Varying ◽  
Multiple Model ◽  
Fixed Effects Model ◽  
Omitted Variables ◽  
Hausman Test ◽  
Fixed Effects Estimator ◽  
Time Invariant

The fixed-effects estimator is biased in the presence of dynamic misspecification and omitted within variation correlated with one of the regressors. We argue and demonstrate that fixed-effects estimates can amplify the bias from dynamic misspecification and that with omitted time-invariant variables and dynamic misspecifications, the fixed-effects estimator can be more biased than the ‘naïve’ OLS model. We also demonstrate that the Hausman test does not reliably identify the least biased estimator when time-invariant and time-varying omitted variables or dynamic misspecifications exist. Accordingly, empirical researchers are ill-advised to rely on the Hausman test for model selection or use the fixed-effects model as default unless they can convincingly justify the assumption of correctly specified dynamics. Our findings caution applied researchers to not overlook the potential drawbacks of relying on the fixed-effects estimator as a default. The results presented here also call upon methodologists to study the properties of estimators in the presence of multiple model misspecifications. Our results suggest that scholars ought to devote much more attention to modeling dynamics appropriately instead of relying on a default solution before they control for potentially omitted variables with constant effects using a fixed-effects specification.

Bias in Conditional and Unconditional Fixed Effects Logit Estimation

2001 ◽  
Vol 9 (4) ◽  
pp. 379-384 ◽  
Author(s):  
Ethan Katz
Keyword(s):  
Maximum Likelihood ◽  
Fixed Effects ◽  
Panel Data Analysis ◽  
Asymptotic Properties ◽  
Logit Models ◽  
Finite Sample ◽  
Monte Carlo Experiments ◽  
Logit Estimation ◽  
Finite Sample Properties ◽  
Conditional Maximum

Fixed-effects logit models can be useful in panel data analysis, when N units have been observed for T time periods. There are two main estimators for such models: unconditional maximum likelihood and conditional maximum likelihood. Judged on asymptotic properties, the conditional estimator is superior. However, the unconditional estimator holds several practical advantages, and therefore I sought to determine whether its use could be justified on the basis of finite-sample properties. In a series of Monte Carlo experiments for T < 20, I found a negligible amount of bias in both estimators when T ≥ 16, suggesting that a researcher can safely use either estimator under such conditions. When T < 16, the conditional estimator continued to have a very small amount of bias, but the unconditional estimator developed more bias as T decreased.

Adversarial Inference Is Efficient

2021 ◽  
Vol 111 ◽  
pp. 621-625
Author(s):  
Tetsuya Kaji ◽  
Elena Manresa ◽  
Guillaume A. Pouliot
Keyword(s):  
Data Model ◽  
Fixed Effects ◽  
Minimum Distance ◽  
Mean Squared Error ◽  
Finite Sample ◽  
Squared Error ◽  
Autoregressive Parameter ◽  
Finite Sample Properties ◽  
Minimum Distance Estimators ◽  
Hidden Layer

We study properties of the adversarial framework, introduced in Kaji, Manresa and Pouliot (2020). We show that the adversarial inference with an oracle classifier is statistically efficient. In addition, we study the finite sample properties of the adversarial estimation framework for the autoregressive parameter of a linear dynamic fixed effects panel data model with Gaussian errors. Unlike maximum likelihood, but similarly as other minimum distance estimators, the adversarial estimators do not suffer from the incidental parameter bias. In our simulations, using a one-hidden-layer neural network as discriminator delivers the estimates with smallest root mean squared error.

scholarly journals Robust Standard Errors for Panel Regressions with Cross-Sectional Dependence

2007 ◽  
Vol 7 (3) ◽  
pp. 281-312 ◽  
Author(s):  
Daniel Hoechle
Keyword(s):  
Least Squares ◽  
Fixed Effects ◽  
Weighted Least Squares ◽  
Ordinary Least Squares ◽  
Standard Errors ◽  
Least Squares Regression ◽  
Finite Sample ◽  
Cross Sectional ◽  
Finite Sample Properties ◽  
Cross Sectional Dependence

I present a new Stata program, xtscc, that estimates pooled ordinary least-squares/weighted least-squares regression and fixed-effects (within) regression models with Driscoll and Kraay (Review of Economics and Statistics 80: 549–560) standard errors. By running Monte Carlo simulations, I compare the finite-sample properties of the cross-sectional dependence–consistent Driscoll–Kraay estimator with the properties of other, more commonly used covariance matrix estimators that do not account for cross-sectional dependence. The results indicate that Driscoll–Kraay standard errors are well calibrated when cross-sectional dependence is present. However, erroneously ignoring cross-sectional correlation in the estimation of panel models can lead to severely biased statistical results. I illustrate the xtscc program by considering an application from empirical finance. Thereby, I also propose a Hausman-type test for fixed effects that is robust to general forms of cross-sectional and temporal dependence.

Dampak Kebijakan Moneter Pada Output Di Negara-Negara ASEAN Tahun 1980-2014

2017 ◽  
Vol 4 (1) ◽  
pp. 58-70
Author(s):  
Nur Widiastuti
Keyword(s):  
Monetary Policy ◽  
Interest Rate ◽  
Fixed Effects ◽  
Empirical Studies ◽  
Negative Relationship ◽  
Least Square ◽  
Fixed Effects Model ◽  
State Data ◽  
Ordinary Least Square ◽  
The Impact

The Impact of monetary Policy on Ouput is an ambiguous. The results of previous empirical studies indicate that the impact can be a positive or negative relationship. The purpose of this study is to investigate the impact of monetary policy on Output more detail. The variables to estimatate monetery poicy are used state and board interest rate andrate. This research is conducted by Ordinary Least Square or Instrumental Variabel, method for 5 countries ASEAN. The state data are estimated for the period of 1980 – 2014. Based on the results, it can be concluded that the impact of monetary policy on Output shown are varied.Keyword: Monetary Policy, Output, Panel Data, Fixed Effects Model

A Systematic Review and Meta-Analysis about the Effect of Bisphosphonates on the Risk of Skeletal-Related Event in Men with Prostate Cancer

2020 ◽  
Vol 20 (13) ◽  
pp. 1604-1612
Author(s):  
Congcong Wu ◽  
Hua Jiang ◽  
Jianghua Chen
Keyword(s):  
Prostate Cancer ◽  
Fixed Effects ◽  
Data Extraction ◽  
Controlled Trial ◽  
Meta Analysis ◽  
Control Group ◽  
Fixed Effects Model ◽  
Related Event ◽  
Mineral Density ◽  
Skeletal Related Event

Background: Although the adjuvant therapy of bisphosphonates in prostate cancer is effective in improving bone mineral density, it is still uncertain whether bisphosphonates could decrease the risk of Skeletal- Related Event (SRE) in patients with prostate cancer. We reviewed and analyzed the effect of different types of bisphosphonates on the risk of SRE, defined as pathological fracture, spinal cord compression, radiation therapy to the bone, surgery to bone, hypercalcemia, bone pain, or death as a result of prostate cancer. Methods: A systemic literature search was conducted on PubMed and related bibliographies. The emphasis during data extraction was laid on the Hazard Ratio (HR) and the corresponding 95% Confidence Interval (CI) from every eligible Randomized Controlled Trial (RCT). HR was pooled with the fixed effects model, and preplanned subgroup analyses were performed. Results: 5 RCTs (n = 4651) were included and analyzed finally after screening 51 articles. The meta-analysis of all participants showed no significant decrease in the risk of SRE when adding bisphosphonates to control group (HR = 0.968, 95% CI = 0.874 - 1.072, p = 0.536) with low heterogeneity (I2 = 0.0% (d.f. = 4) p = 0.679). There was no significant improvement on SRE neither in the subgroups with Metastases (M1) or Castration-Sensitive Prostate Cancer (CSPC) (respectively HR = 0.968, 95% CI = 0.874 - 1.072, p = 0.536, I2 = 0.0% (d.f. = 4) p = 0.679; HR = 0.954, 95% CI = 0.837 - 1.088, p = 0.484, I2 = 0.0% (d.f. = 3) p = 0.534). Conclusion: Our study demonstrated that bisphosphonates could not statistically significantly reduce the risk of SRE in patients with prostate cancer, neither in the subgroups with M1 or CSPC.

scholarly journals Cultural and Creative Cities and Regional Economic Efficiency: Context Conditions as Catalyzers of Cultural Vibrancy and Creative Economy

2021 ◽  
Vol 13 (13) ◽  
pp. 7150
Author(s):  
Silvia Cerisola ◽  
Elisa Panzera
Keyword(s):  
Fixed Effects ◽  
Local Context ◽  
Creative Economy ◽  
Fixed Effects Model ◽  
Creative Cities ◽  
Economic Output ◽  
Innovative Methodology ◽  
Holistic Conception ◽  
Panel Fixed Effects ◽  
The Impact

Following the hype that has been given to culture and creativity as triggers and enhancers of local economic performance in the last 20 years, this work originally contributes to the literature with the objective of assessing the impact of cultural and creative cities (CCCs) on the economic output of their regions. In this sense, the cultural and creative character of cities is considered a strategic strength and opportunity that can spillover, favoring the economic system of the entire regions in which the cities are located. Through an innovative methodology that exploits a regional production function estimated by a panel fixed effects model, the effect of cities’ cultural vibrancy and creative economy on the output of their regions is econometrically explored. The data source is the Cultural and Creative Cities Monitor (CCCM) provided by the JRC, which also allows the investigation of the possible role played by the enabling environment in catalyzing the action of cultural vibrancy and creative economy. The results are thoroughly examined: especially through cultural vibrancy, CCCs strategically support the output of their region. This is particularly the case when local context conditions—such as human capital and education, openness, tolerance and trust, and quality of governance—catalyze their effect. Overall, CCCs contribute to feeding a long-term self-supporting system, interpreted according to a holistic conception that includes economic, social, cultural, and environmental domains.

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