Two-way Error Component Model Research Articles

A Pretest Estimator for the Two-Way Error Component Model

For a panel data linear regression model with both individual and time effects, empirical studies select the two-way random-effects (TWRE) estimator if the Hausman test based on the contrast between the two-way fixed-effects (TWFE) estimator and the TWRE estimator is not rejected. Alternatively, they select the TWFE estimator in cases where this Hausman test rejects the null hypothesis. Not all the regressors may be correlated with these individual and time effects. The one-way Hausman-Taylor model has been generalized to the two-way error component model and allow some but not all regressors to be correlated with these individual and time effects. This paper proposes a pretest estimator for this two-way error component panel data regression model based on two Hausman tests. The first Hausman test is based upon the contrast between the TWFE and the TWRE estimators. The second Hausman test is based on the contrast between the two-way Hausman and Taylor (TWHT) estimator and the TWFE estimator. The Monte Carlo results show that this pretest estimator is always second best in MSE performance compared to the efficient estimator, whether the model is random-effects, fixed-effects or Hausman and Taylor. This paper generalizes the one-way pretest estimator to the two-way error component model.

The two-way Mundlak estimator

Mundlak shows that the fixed effects estimator is equivalent to the random effects estimator in the one-way error component model once the random individual effects are modeled as a linear function of all the averaged regressors over time. In the spirit of Mundlak, this paper shows that this result also holds for the two-way error component model once the individual and time effects are modeled as linear functions of all the averaged regressors across time and across individuals. Wooldridge also shows that the two-way fixed effects estimator can be obtained as a pooled OLS with the regressors augmented by the time and individual averages and calls it the two-way Mundlak estimator. While Mundlak used GLS rather than OLS on this augmented regression, we show that both estimators are equivalent for this augmented regression. This extends Baltagi’s results from the one-way to the two-way error component model. The F test suggested by Mundlak to test for this correlation between the random effects and the regressors generate a Hausman type test that is easily generalizable to the two-way Mundlak regression. In fact, the resulting F-tests for the two-way error component regression are related to the Hausman type tests proposed by Kang for the two-way error component model.

건강기능식품 제조업체의 수익성에 대한 영향 요인 분석

This paper analyzes factors that affect the profitability of health functional food manufacturers from the strategic business management perspective. For this purpose, we estimated a two-way error components model based on panel data of financial statements for health functional foods manufacturers, considering time effects. Our empirical evidence suggests that both industry-level and firm-level strategies are needed for individual firms to gain a competitive advantage by enhancing their profit performance. In response to competition intensification within the health functional food industry, they should consider the following efforts to increase profit performance to secure a competitive advantage: (1) expanding the firm’s size, (2) increasing market share, (3) realizing leverage effect through efficient capital management, (4) enhancing the advertising effectiveness, and (5) improving the efficiency of resource management.

Testing for individual and time effects in the two-way error component model with time-invariant regressors

In this paper, we focus on testing for individual and time effects in the two-way error component model with time-invariant regressors. We present the so-called FEF estimators when time-invariant regressors are exogenous and the FEF-IV estimators when one or more of time-invariant variables are endogenous, and obtain their asymptotic properties under some mild conditions. In the light of the moment-based test methods of Wu and Li (2014), we construct several tests for the existence of individual and time effects in the two-way error component model with time-invariant regressors. The resulting tests can be shown to have some desired properties as follows: they do not need any distributional assumptions on the error components; they do not require any assumptions on the correlation among the two random effects and the time-varying regressors; they are robust to the presence of one effect when the other one is tested. Simulation study and real data analysis are carried out for illustration of the above.

Moment-based tests for random effects in the two-way error component model with unbalanced panels

This paper extends Wu and Li (2014)'s moment-based tests for random effects to the case with unbalanced panel data. Based on the difference of variance estimators of the idiosyncratic errors at different robust levels, two statistics are constructed to test for the existence of individual and time effects, respectively. Some variants of the two statistics and joint tests for both the two effects are also discussed. Their asymptotic properties are obtained under some mild conditions. It is shown that the tests retain the desired properties observed in the balanced panel data case. Monte Carlo simulation experiments and a real data analysis are carried out for illustration.

Impact of Improved Creditorss Rights on Bank Competition and Credit Allocation: An Empirical Study

This study empirically investigates the impact of SRFAESI Act on debt financing by foreign banks in India. Two-way error component model (fixed effect panel) was used for this within-country assessment. Taking advantage of a policy event (Securitization and Reconstruction of Financial Asset and Enforcement of Security Interests in India (SRFAESI), Act, 2002, which improved the creditors’ rights (banks) in India ; we empirically examined its effect on firms in India. Specifically, we analyse if the entry of a foreign bank in a district, post SRFAESI has improved credit access for the firms. SRFAESI gives the banks a right to liquidate collateral in case of a default, therefore it should encourage lending. The impact is expected to be more in the districts were the foreign bank has entered for the first time compared to other locations where a foreign bank already existed. Theoretical literature suggests that enforcement of law should encourage more lending. Also, prior studies on credit allocation argue that use of collateral reduces credit rationing. Hence, combining these two strands of literature we can provide empirical evidence of the impact of stronger creditor’s rights on reducing information asymmetry. Amount of bank borrowings pre- and post-event; after controlling for firm level factors, was the model used for the analysis. The estimates indicate that High tangible firms received larger share of bank loans, but that on average, younger firms (

Public Procurement: A Panel Data Approach

Public procurement is subject of many scientific analyses. Some of the analyses use econometrics methods on the public procurement data. Available dataset provides mainly cross-section data, therefore most of the analyses works with basic econometrics methods. This paper describes data sources about public procurement in the Czech Republic, shows how to transform available dataset into panel data and what methods could be used for the analyses of the newly created datasets. The use of error components models is suggested, it is shown which type of panel data transformation is suitable for which model (one-way or two-way error components model) and the potential strengths and weaknesses of panel data approach to public procurement are identified. The suggestion for the future research is to try out proposed panel data approach on available public procurement data.

Conditional Score Tests for Heteroscedasticity in the Two-Way Error Components Model

This paper extends the one-way heteroskedasticity score test of Holly and Gardiol (2000, In: Krishnakumar, J, Ronchetti, E (Eds.), Panel Data Econometrics: Future Directions, North-Holland, Amsterdam, pp. 199–211) to two conditional Lagrange Multiplier (LM) tests of heteroskedasticity under contiguous alternatives within the two-way error components model framework. In each case, the derivation of Rao's efficient score statistics for testing heteroskedasticity is first obtained. Then, based on a specific set of assumptions, the asymptotic distribution of the score under contiguous alternatives is established. Finally, the expression for the score test statistic in the presence of heteroskedasticity and related asymptotic local powers of these score test statistics are derived and discussed.

A Joint Score Test for Heteroscedasticity in the Two Way Error Components Model

This article extends the work by Holly and Gardiol (2000) (A score test for individual heteroscedasticity in a one-way error component model. In: Krishnakumar, J., Ronchetti, E., Eds. Panel Data Econometrics: Future Directions. Elsevier, North-Holland, Amsterdam, pp. 199–211, Ch. 10) to the two-way error components model. It deals exclusively with a joint heteroscedasticity test by first deriving Rao's efficient score statistics. Then, based on appropriate set of assumptions, we deduce the asymptotic distribution of the score under contiguous alternatives. Finally, we provide the expression for the score test statistic in the presence of heteroscedasticity and discuss its asymptotic local power.

The Robustness of Price Elasticity Estimates: A Revisit of Various Methodologies Used To Estimate Demand for Cigarettes

Although the price elasticity of cigarettes has been estimated using a variety of models and methods, few studies apply these methods to the same dataset and compare the estimates from alternative approaches. This paper employs the National Longitudinal Survey of Youth 1997 to provide the first comprehensive study that addresses most methodology limitations found in the previous literature and compares cigarette price elasticity estimated from many approaches. We estimate the elasticity by allowing the addictiveness to differ by non-addictive, myopic and rational smoking. We account for the state-level anti-smoking sentiment using its time-invariant and time-variant measures. We control for the heterogeneity of smoking behavior among population using the two-part model in a cross-sectional approach and the two-way error component model in a longitudinal approach. The estimates of elasticity are significant in almost all specifications and strikingly close in magnitude. The price elasticity of smoking participation ranges from -0.08 to -0.24 with an average of -0.16, and the price elasticity of cigarette consumption rages from -0.07 to -0.24 with an average of -0.14. This indicates that when estimated using the same panel dataset with a relative long time span, the elasticity is likely to be estimated consistently regardless of approach.

A Note on Two-Way ECM Estimation of SUR Systems on Unbalanced Panel Data

This article considers the two-way error components model (ECM) estimation of seemingly unrelated regressions (SUR) on unbalanced panel by generalized least squares (GLS). As suggested by Biørn (2004) for the one-way case, in order to use the standard results for the balanced case the individuals are arranged in groups according to the number of times they are observed. Thus, the GLS estimator can be interpreted as a matrix weighted average of the group specific GLS estimators with weights equal to the inverse of their respective covariance matrices.

Testing for Cross-Sectional Dependence in a RandomEffects Model

This paper extends and generalizes the works of [1,2] to allow for cross-sectional dependence in the context of a two-way error components model and consequently develops LM test. The cross-sectional dependence follows the first order spatial autoregressive error (SAE) process and is imposed on the remainder disturbances. It is important to note that this paper does not consider alternative forms of spatial lag dependence other than SAE. It also does not allow for endogeneity of the regressors and requires the normality assumption to derive the LM test.

A Transformation for General Variance Covariance Structures in a Two-Way Error Component Model

This note examines general variance-covariance structures for the specific effects and the overall error term in a two-way error component (EC) model. So far panel data literature has considered these general structures in a one-way model and followed the approach of a Cholesky-type transformation that brings the model back to a 'classical' one-way EC setting. In this note, we first show that in a two-way context it is impossible to find a Cholesky-type transformation when the error components have a general variance-covariance structure (which includes autocorrelation). Then we propose two solutions which use the spectral decomposition of the variance components and give a general transformation leading to a block-diagonal structure which can be easily handled. The results are obtained under some general conditions on the matrices involved which are satisfied by most commonly used structures. Thus our results provide a general framework for introducing new variance-covariance structures in a panel data model and this is illustrated by taking some interesting special cases and showing how the spectral decomposition can be derived using our results.

Prediction from the regression model with two-way error components

In this paper we extend the Baillie and Baltagi (1999) paper (Prediction from the regression model with one-way error components. In Analysis of Panels and Limited Dependent Variables Models, Hsiao C, Lahiri K, Lee LF, Pesaran H (eds). Cambridge University Press, Cambridge, UK). In particular, we derive six predictors for the two-way error components model, as well as their associated asymptotic mean squared error (AMSE) of multi-step prediction. In addition, we also provide both theoretical and simulation evidence as to the relative efficiency of our six alternative predictors. The adequacy of the prediction AMSE formula is also investigated by the use of Monte Carlo methods which indicate that the ordinary optimal predictors perform well for various accuracy criteria. Copyright © 2010 John Wiley & Sons, Ltd.

Firm leverage and investment decisions in an emerging market

In this study, the effect of leverage on investment is analyzed by employing panel data methods for the Turkish non-financial firms that are quoted on Istanbul Stock Exchange. For one-way error component models, it is shown that there is a negative impact of leverage on investment for only firms with low Tobin’s Q. These results are in conformity with the previous literature and agency theories of corporate finance stating that leverage has a disciplining role for firms with low growth opportunities. However, when the model is extended to include the time effects in a two-way error component model, the relation between leverage and investment disappears.

The Recardian Theory of Comparative Advantage Between South Africa and the Usa in the Manufacturing Sector

This study focuses on the Ricardian model of comparative advantage with one factor of production and two countries, but in a multi-goods dimension. This is used to explain the pattern of trade that exists between South Africa and the United States’s manufacturing industries. The empirical test of the Ricardian model is carried out using a panel data approach on 23 manufacturing industries. The estimations were performed using both a one-way and two-way error component model and a stationary panel technique were applied to the equations. The advantages of progressing from cross-sectional estimations to a panel data estimation technique is that the efficiency of estimating the parameters will be improved. It also gives more informative data, more variability, less collinearity, more degrees of freedom and the effect of omitted variable bias is reduced. The results show that the pattern of trade between South Africa and the United States conforms to the Ricardian theorem.

High Wage Workers and Low Wage Firms: Negative Assortative Matching or Limited Mobility Bias?

SummaryIn the empirical literature on assortative matching using linked employer–employee data, unobserved worker quality appears to be negatively correlated with unobserved firm quality. We show that this can be caused by standard estimation error. We develop formulae that show that the estimated correlation is biased downwards if there is true positive assortative matching and when any conditioning covariates are uncorrelated with the firm and worker fixed effects. We show that this bias is bigger the fewer movers there are in the data, which is ‘limited mobility bias’. This result applies to any two-way (or higher) error components model that is estimated by fixed effects methods. We apply these bias corrections to a large German linked employer–employee data set. We find that, although the biases can be considerable, they are not sufficiently large to remove the negative correlation entirely.

장애모수가 존재하는 불균형 패널회귀모형에서의 검정법

본 논문에서는 불균형 패널회귀모형에서 장애모수가 존재하는 경우 오차성분의 존재유무를 검정하기 위하여 Lagrange Multiplier 검정통계량을 제안하였다 이러한 LM통계량은 그룹변환에 대한 불변성의 성질을 이용하여 유도된 통계량으로 모의실험 결과, 제안된 LM검정은 명목유의수준을 잘 유지하고 있는 것으로 나타났으며 LR검정에 비하여 검정력에 있어서도 높게 나타났다. This paper consider the testing problem of variance component for the unbalanced two-way error component model with nuisance parameter. We derive the one-sided LM test statistic for testing zero individual(time) effects assuming that the other time-specific(individual) effects are present. Using the Monte Carlo experiments, the computational more demanding LR test slightly underestimates the nominal size and has the low powers relative to LM test statistic.

A comparative study of alternative estimators for the unbalanced two‐way error component regression model

Summary This paper considers the unbalanced two-way error component model studied by Wansbeek and Kapteyn (1989). Alternative analysis of variance (ANOVA), minimum norm quadratic unbiased and restricted maximum likelihood (REML) estimation procedures are proposed. The mean squared error performance of these estimators are compared using Monte Carlo experiments. Results show that for the estimates of the variance components, the computationally more demanding maximum likelihood (ML) and minimum variance quadratic unbiased (MIVQUE) estimators are recommended, especially if the unbalanced pattern is severe. However, focusing on the regression coefficient estimates, the simple ANOVA methods perform just as well as the computationally demanding ML and MIVQUE methods and are recommended.

Small sample properties of the power function ofF tests in two-way error component regression

In this paper we investigate properties of the power function of the generalized least squaresF test for linear hypotheses under regression models with two-way error component model. The covariance structure of the model depends on the correlation coefficients ρ1 and ρ2 corresponding to the random effects. This model has been frequently applied to the analysis of panel data. In general, we show that the power is a monotonically increasing function of ρ1(ρ2) in a region which is close to the ρ1(ρ2) axis, and a monotonically decreasing function of ρ1(ρ2) in a region close to the ρ2(ρ1) axis.