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A panel (or longitudinal or temporal cross-sectional) data set is one which follows a number of individuals over time, and thus provides multiple observations on each individual in the sample. Prominent examples are the University of Michigan’s Panel Study of Income Dynamics, the National Longitudinal Surveys of Labor Market Experience, and National Longitudinal Survey of Youth in the US and the Social Economic Panel, the Expenditure Index Panel of Intromart, and the Labor Mobility Survey from the Organization of Strategic Labor Market Research in the Netherlands. These labor market data samples contain thousands of individuals and variables followed over a number of years. In addition, panel data are also common in marketing studies, biomedical sciences, and ﬁnancial market analysis, etc.
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The increasing popularity of panel studies is partly a consequence of more cost eﬀective ways of developing and maintaining panels (e.g., Matyas and Sevestre 1996). More importantly, panel data oﬀers many more possibilities for exploring analytical and substantive issues than purely cross-sectional or time series data. However, new data sources also raise new issues. This research paper reviews the major advantages and limitations of panel data in the context of speciﬁc econometric methodologies. Section 2 gives an overview of the major advantages of the informational content of panel data. Section 3 reviews typical speciﬁcations for the linear models. Section 4 discusses issues of nonlinear models. Section 5 considers the implication of sample attrition and sample selection. Conclusions are in Sect. 6.
2. Advantages Of Panel Data
Panel data oﬀer several advantages over a single cross-sectional or time series data. For instance, the use of panel data may:
2.1 Improve The Accuracy Of Parameter Estimates
Most statistical estimators converge to the true parameters at the speed of the square root of the degrees of freedom. Models for panel data are based on many more equivalent observations than a cross-sectional or time series data, and hence can yield more accurate parameter estimates.
2.2 Lessen The Problem Of Multicollinearity
Many economic variables tend to move collinearly over time. The shortage of degrees of freedom and severe multicollinearity problems found in time series data often frustrate investigators who wish to determine the individual inﬂuences of each explanatory variable. Panel data may lessen the degree of multicollinearity in the time dimension by appealing to inter-individual diﬀerences.
2.3 Allow The Speciﬁcation Of More Complicated Behavioral Hypotheses
A single time series is not useful for discriminating hypotheses which depend on diﬀerent social–demographic factors. Cross-sectional data cannot be used to model dynamics. Cross-sectional data can show what proportion of the labor force is unemployed or describe the distribution of wage rates at a point in time. Aggregate time series data can provide indications of general trends and cyclical patterns in unemployment, wages, income, and so forth. But neither cross-sectional nor time series data can provide information regarding how many of those unemployed in one month can ﬁnd employment in the next month. Neither can the data provide information on whether the tendency of welfare families to stay on welfare occurs simply because the same factors that cause them to be on welfare keep them there or whether, in addition, the experience of receiving welfare has some addictive eﬀect that induces continuing welfare dependence. For instance, suppose that a cross-sectional sample of married women is found to have an average yearly labor force participation rate of 50 percent. At one extreme this might be interpreted as implying that each woman in a homogenous population has a 50 percent chance of being in the labor force in any given year while at the other extreme it might imply that 50 percent of the women in a heterogenous population always work and 50 percent never work. In the ﬁrst case, each woman would be expected to spend half of her married life in the labor force, and half out of the labor force, and job turnover would be expected to be frequent, with an average job duration of two years. In the second case, there is no turnover, and current information about work status is a perfect predictor of future work status. Panel data, by providing sequential observations for a number of individuals, oﬀer the possibility to distinguish inter-individual diﬀerences from intra-individual dynamics and to construct a proper recursive structure to study the eﬀects of changes by comparing the diﬀerences before and after the changes (e.g., Heckman 1981).
2.4 Pro Ide Possibilities For Reducing Estimation Bias
A standard assumption in statistical speciﬁcation is that the random error term representing the eﬀect of omitted variables is orthogonal to the included explanatory variables. Otherwise, the estimates are subject to omitted variable bias when these correlations are not explicitly allowed for. Panel data provide means to eliminate or reduce the omitted variable bias through various data transformations when the correlations between included explanatory variables and omitted variables follow certain speciﬁc patterns (e.g., Baltagi 1995), Chamberlain 1984, Hsiao (1986). For instance, suppose that
where the explanatory variables xit, vary across individuals i, and over time t, and the explanatory variables zi, vary across i, but stay constant over t, such as sex status, social-demographic background variables, etc. the error term u is assumed to be uncorrelated with x or z. The least squares regression of y on x and z will yield consistent estimates of β and γ. But if z are correlated with x, yet are unobservable, the least squares regression of y on x will yield bias estimates of β. However, in the pairwise diﬀerence,
the explanatory variables zi longer enter. Regressing ( yit-yi, t−1 ) on (xit-xi, t−1) can yield consistent estimates of β.
MaCurdy’s (1981) life-cycle labor supply of primeage males under certainty is an example of this approach. Under certain simplifying assumptions, MaCurdy shows that a worker’s labor supply function can be written as in Eqn. (1), where y is the logarithm of hours worked, x is the logarithm of the real wage rate, and z is the logarithm of the worker’s (unobserved) marginal utility of initial wealth, which, as a summary measure of worker’s lifetime wages and property income, is assumed to stay constant through time but to vary across individuals. Given the economic problem, not only is xit correlated with zi, but every economic variable that could act as an instrument for xit (such as education) is also correlated with zi. Another example is the ‘conditional convergence’ of the growth rate. The growth rate regression model typically includes investment ratio, initial income, and measures of policy outcomes like school enrolment and the black market exchange rate premium as regressors; however, an important component, the initial level of a country’s technical eﬃciency zi0 , is not observed. Since a country that is less eﬃcient is also more likely to have lower investment rate or school enrolment, one can easily imagine that zi0 is correlated with the included repressors. Neither model can be estimated with cross-sectional data, but both can be estimated using Eqn. (2) if panel data are available.
2.5 Reduce Computational Complexities
The use of panel data is often perceived as increasing computational complexities because the data contain both cross-sectional and time series dimensions. In many cases, however, panel data become a distinct advantage computationally. For instance, if one were to estimate a dynamic model using time series data with censored or missing observations in between, the computation can be horrendous because of the need to compute multiple integrals when the conditional variables are censored. To see this, consider the model
where uit is independent normal with mean 0 and variance σu2. Conditional on y*t,i-1, y*u is normally distributed with mean γy*t,i-1 and variance σu. If instead of observing y*it , however, one observes the censored counterpart yit, where yit=y*it if y*it>0 and yit=0 if y*it≤0, then the conditional distribution of yit given yi, t−1 0 becomes
where f( y*I,t-1) denotes the marginal density of (y*I,t-1).
When there are a number of censored observations over time, Eqn. (4) becomes relatively complicated to solve. With panel data, one can often adopt models that ignore observations that are censored and focus on those individuals for which successive observations are greater than zero to simplify the computation (e.g., Arellano et al. 1999).
2.6 Simplify Statistical Inference
In estimating a dynamic model using time series, the limiting distribution depends on whether the system is stable, integrated, or explosive. With panel data, as long as the cross-sectional dimension approaches inﬁnity, the limiting distribution of the coeﬃcients of lagged dependent variables is typically normal no matter what the time series properties of a process are. The limiting distribution remains the same whether the time series dimension is ﬁxed (Anderson and Hsiao 1982) or approaches inﬁnity (Levin and Lin 1993, Phillips and Moon 1999).
2.7 Obtain More Accurate Prediction Of Individual Outcomes
When people’s behavior is similar in nature, namely, they satisfy De Finnetti’s (1964) exchangeability assumption, each individual’s behavior can be better understood by observing the behavior of other similar individuals, hence can yield more accurate predictions of individual outcomes by pooling the data (e.g., Hsiao et al. 1989).
3. Linear Models
Panel data, by its nature, put emphasis on individual outcomes. Diﬀerent individuals may be subject to diﬀerent inﬂuences. In explaining individual behavior, one may extend the list of factors ad inﬁnitum. It is neither feasible nor desirable to include all the factors aﬀecting the outcome of all diﬀerent individuals in a model speciﬁcation. It is typical to leave out those factors that are believed to have insigniﬁcant impact or are peculiar to a given individual. However, when important factors peculiar to a given individual are left out, the individual outcomes conditional on the included explanatory variables may not be viewed as random draws from a common population, so standard statistical procedures may mislead. Therefore, one focus of panel data modeling is to determine whether heterogeneities exist among cross-sectional units over time that are not captured by the included explanatory variables, say x. If they are, one needs to control the impact of such unobserved heterogeneity in order to draw inference about the population characteristics.
If the unobserved heterogeneity among N cross- sectional units over T time periods can not be completely captured by x, there are two primary ways to model it. One is to incorporate it into the error term. The other is to let the coeﬃcients vary across individuals and over time. For instance, if the heterogeneity among individual units is due to some individual speciﬁc and time invariant variables, zi, that aﬀect yit linearly, then the scatter diagram between y and x conditional on z may look like Fig. 1 in which the broken-line circles represent the point scatter for an individual over time, and the broken straightline represents the mean relationship for the ith individual conditional on zi. The dark line indicates a simple pooled estimates of a model
where v represents the eﬀects of omitted factors and the subscript, it, indicates observations of the ith cross-sectional unit at tth time period. It is obvious that the simple pooled estimates give a very misleading relationship between y and x. For data of the form of Fig. 1 it is necessary to specify a variable intercept model of the form
where αi represents the eﬀect of unobserved zi.
A more complicated structure to capture the unobserved heterogeneity lets
but this speciﬁcation is not very useful for drawing inferences about the population because the coefﬁcients are individual and time speciﬁc. Neither can it be estimated because there are more unknown parameters than degrees of freedom. In order to take advantage of panel data, a structure has to be introduced that allows the possibility to draw inference about the population characteristics while controlling for the impact of unobserved heterogeneity.
Suppose that the unobserved heterogeneity is due to some individual speciﬁc but time invariant variables, then an unrestricted linear model will have the form
If T is large, Eqn. (8) can be estimated by Zellner’s (1962) seemingly unrelated regression method; however, most panel data contains a large number of individuals observed over a short period of time. When T is ﬁxed, increasing N increases the number of unknown parameters, αi and βi, i=1, …, N. This is the classical incidental parameter problem. The presence of the incidental parameters violates the usual regularity conditions for the consistency of the maximum likelihood estimator (MLE). Neither is Eqn. 3.4 very useful to draw inference about the population because each individual has a diﬀerent behavioral pattern. Assuming Eqn. (8) is tantamount to assuming that the cross-sectional observations come from heterogeneous population. In this case there is no particularly appealing reason to pool the data.
To draw inference about the population, one must impose restrictions on Eqn. (7) or Eqn. (8). For instance, if the slope coeﬃcients are the same, βi=β, for all i as in Eqn. (6), the incidental parameter αi can be eliminated by pairwise diﬀerencing of individual time series observations as in Eqn. 2 or by taking deviations from the individual time series mean:
where yi=(1/T ) ∑Tt=1yu,x1=(1/T)∑Tt=1xu and ut=(1/T)∑Tt=1uit. Applying the least squares method to Eqn. (9) yields the so-called covariance or within estimator of β. The covariance estimator of β is consistent when either N or T or both N and T tend to inﬁnity (e.g., Hsiao 1986).
Alternatively, one can assume that βi and/or αi in Eqn. (8) are randomly distributed with common mean (β , α) and covariance matrix ∆ or assume that some of the coeﬃcients are randomly distributed with common mean and constant covariance matrix, while others are ﬁxed and diﬀerent. Decomposing xit into (wit, zit), various formulations can be represented as placing diﬀerent restrictions on the unconstrained model Eqn. (8):
where y=( y`1, …, y`N), W=diag(Wi), Z=diag (Zi), yi=( yi1 , …, yiT)`, Wi and Zi are T×k1 and T×k2 submatrices of T observed values of (1, x`it), δ = (δ1, …, δ N)` and γ=(γ1, …, γN) are Nk1×1 and Nk2×1 vectors of (αi, βi), i=1, …, N. Suppose that the coeﬃcients of wit are subject to stochastic constraints:
where A1 is an Nk1×m matrix with known elements, δ is an m×1 vector of constants, ε is random with mean 0, covariance matrix C and EεW`=0, EεZ`=0. Suppose that the coeﬃcients of zit are subject to deterministic constraints:
where A2 is an Nk2×q matrix with known elements, and γ is a q×1 vector of constants.
The model given by Eqns. (10)–(12) is of a mixed form: the coeﬃcients of Wi are assumed to be random and the coeﬃcients of Zi are assumed ﬁxed, (e.g., Hsiao and Tahmiscioglu 1997). When W=0, A2=eN ϱΘIk , we have a common model for all NT observations, where eN denotes an N×1 vector of ones, Ip denotes a p×p identity matrix and denotes Kronecker product. When W=0 and A=INk , model Eqn. (8) results. when W=0, Zi =(eT, Xi), A2= (INΘik2:eNΘI*k2-1) is a k2×1 vector of (1, 0, …, 0)’’, and I*k2-1 is a k2 ×(k2-1) matrix of (I0k2-1), model Eqn. (6) (e.g., Mundlak 1978) is a special case. When Xi=eT, A1=eN, ∆=σ2δ IN, the model is the error components model (e.g., Balestra and Nerlove 1966). Finally when Z=0, A1=eNΘIk , C=INΘ∆, we have the random coeﬃcients model (e.g., Swamy 1970).
Substituting Eqns. (11) and (12) into Eqn. (10) yields
where v=Wε+u, Evv`=WCW`+Cov (u). Model Eqn. (13) can be estimated by the generalized least squares (GLS) method. The individual coeﬃcients δ can be estimated by a Bayes estimator which is a weighted average of the GLS estimator of δ and the individual least squares estimator of δ (e.g., Hsiao et al. 1993).
The issue of whether to treat βi and/or αi or a subvector of them as ﬁxed or random is an issue of whether conditional on x, one can treat the outcome variable as a random draw from a common population. If it is, then the individual and time subscript, it, can be viewed as purely a labeling device and a priori, E (yit|x)=E (yjs|x). This is what de Finnetti (1964) called the exchangeability criterion and it is more appropriate to treat βi and αi as random. If the distribution of βi is not independent of xit or conditional on x, individual outcomes are more appropriately viewed as generated from a heterogenous population, then the subscript, it, contains speciﬁc information about which heterogenous population the particular observation is generated from. It is therefore more appropriate to treat βi and/or αi as ﬁxed parameters. Unfortunately, a priori, very little knowledge is available about which coeﬃcients should be treated as random and diﬀerent, and which coeﬃcients should be treated as ﬁxed and diﬀerent. Hsiao and Sun (2000) have argued that the random versus ﬁxed eﬀects speciﬁcation should be viewed as a model selection issue and suggest a Bayesian approach for model selection. Their limited Monte Carlo studies appear to favor the Schwarz (1978) information criterion to select the appropriate speciﬁcations. The Schwarz criterion works here because although from a Bayesian perspective there is no real diﬀerence between random eﬀects and ﬁxed eﬀects since every parameter has a distribution, there is a diﬀerence between how the prior is formulated under the random and ﬁxed eﬀects formulation. Under the di Finnetti exchangeability criterion, βi is distributed independently of xi with a common mean β and constant variance C (e.g., Eqn. (11)). On the other hand, under the ﬁxed eﬀects formulation, each βi is supposed to be independently distributed with a diﬀerent mean and diﬀerent variance because of the correlation between βi and xi or because each βi is a random draw from heterogenous population.
4. Nonlinear Models
The estimation of a nonlinear panel data model involving individual speciﬁc eﬀects is particularly diﬃcult to handle. If the individual speciﬁc eﬀects are treated as random, just as in the linear model, we no longer have the incidental parameters problem; however, the estimation of the structural parameters requires integrating out the individual speciﬁc eﬀects which can be nontrivial. For instance, consider a binary choice model in which yit takes the value of either 1 or 0. Suppose that the probability of observing yit=1, P (yit=1|xit), is given by F (β xit +αi), where the individual speciﬁc eﬀects αi is randomly distributed with density function f (αi). Even though conditional on αi , yit may be independently distributed over i and t, unconditionally, yit is correlated over T. The log-likelihood function is of the form
Equation (14) is a function of ﬁnite number of parameters and maximizing it under weak regularity conditions will yield consistent estimator of β (and the parameters characterizing the density function of αi). However, maximizing (Eqn. (14)) is often computationally infeasible. For instance, if F (·) takes the form of an integrated standard normal distribution function, then a probit model results. Conditional on αi, it involves a univariate integration. Maximizing (Eqn. (14)) but without the conditioning involves the evaluation of T-dimensional integrals (e.g., Hsiao 1986).
On the other hand, suppose that the individual speciﬁc eﬀects are ﬁxed. First, in general there is no simple transformation of the data as in Eqns. (2) or (9) to eliminate the individual speciﬁc eﬀects (incidental parameters). Second, the estimation of the individual speciﬁc parameters and the structural (or common) parameters are not independent of each other. When a panel contains a large number of individuals but only over a short time period, the error in the estimation of the individual speciﬁc coeﬃcients propagates into the estimating of the structural parameters, and hence leads to inconsistency of the structural parameter estimation (e.g., Anderson and Hsiao 1982).
Many approaches have been suggested for estimating ﬁxed eﬀects nonlinear panel data models. One approach is by conditioning the likelihood function on the minimum suﬃcient statistics for the incidental parameters to eliminate the individual speciﬁc parameters (Andersen 1973, Chamberlain 1980). For instance, consider a binary logit model in which the probability of yit=1 is given by
For ease of exposition, assume that T=2. It can be shown that when xi =0 and xi=1 the MLE of β converges to 2β as N→∞, which is not consistent (e.g., Hsiao 1986). However, one may condition the likelihood function on the minimal suﬃcient statistic for αi. We note that those individuals i satisfying yi1 +yi2 =2 or yi1+yi2=0 provide no information about β since the former leads to αi=∞ and the latter leads to αi=-∞. Hence one can concentrate on those individuals with yi1+yi2=1. For those individuals satisfying yi1+yi2=1, let di=1 if yi1=0 and yi2=1 and di=0 if yi1=1 and yi2=0. Then
Eqn. (16) no longer contains the individual eﬀects αi and is of the form of a standard logit model. Therefore, maximizing the conditional log-likelihood function on the subset of individuals with yi1+yi2=1 is consistent and asymptotically normally distributed.
Alternatively, one can transform the data to eliminate the individual eﬀects if the nonlinear model has a latent linear structure, and then apply semiparametric methods. For instance, consider a binary choice model with yit =1 if y*i1>0 and yit=0 if y*i1≤ 0, where
If uit follows a logistic distribution, a logit model results; if uit is normally distributed, a probit model results. Then
Rewriting (Eqn. (18)) into the equivalent ﬁrst diﬀerence form, Manski (1975) proposes a maximum score estimator (MS) that maximizes the sample average function
where sgn [(xit-xi, t−1)`β ]=1 if (xit -xi, t−1 )`β≥0 and sgn [(xit-xi, t−1 )`β ]=-1 if (xit-xi, t−1 )β<0. The MS estimator is consistent but is not root-n consistent, where n=N (T-1). Its rate of convergence is n1/3 and n1/3(β-β) converges to a random variable that maximizes a certain Gaussian process.
A third approach is to ﬁnd an orthogonal reparameterization of the ﬁxed eﬀects for each individual, say αi, to a new ﬁxed eﬀects, say gi, which are independent of the structural parameters in the information matrix sense. The gi are then integrated out of the likelihood with respect to an uninformative, uniform prior distribution which is independent of the prior distribution of the structural parameters. Lancaster (2001) uses a two period duration model to show that the marginal posterior density of the structural parameter possesses a mode which consistently estimates the true parameter.
While all these methods are quite ingenious, unfortunately, none can claim general applicability. For instance, the conditional maximum likelihood cannot work for the probit model because there are no minimal suﬃcient statistics for the incidental parameters that are independent of the structural parameters. The data transformation approach cannot work if the model does not have a latent linear structure. The orthogonal reparameterization approach works only for particular models. In short, the methodology for and consistency of nonlinear panel data estimators must be established case by case.
5. Sample Attrition And Sample Selection
Missing observations occur frequently in panel data. If individuals are missing randomly, most estimation methods for the balanced panel can be extended in a straightforward manner to the unbalanced panel (e.g., Hsiao 1986). For instance, suppose that
where dit is an observable scalar indicator variable which denotes whether information about ( yit, xit`) for the ith individual at tth time period is available or not. The indicator variable dit is assumed to depend on a q- dimensional variables, wit, individual speciﬁc eﬀects λi and an unobservable error term ηit,
where I (·) is the indicator function that takes the value of 1 if λi+δ`wit+ηit>0 and 0 otherwise. In other words, the indicator variable dit determines whether ( yit, xit)) in Eqn. (20) is observed or not.
Without sample selectivity, that is dit=1 for all I and t, Eqn. (20) is the standard variable intercept (or ﬁxed eﬀects) model for panel data discussed in Sect. 2. With sample selection and if ηit and uit are correlated, E(uit|xit, dit =1)≠0. Let θ(·) denote the conditional expectation of uit conditional on dit=1 and xit, then Eqn. (20) can be written as
where E (εit|xit, dit=1)=0. The form of the selection function is derived from the joint distribution of u and η. For instance, if u and η are bivariate normal, then we have the Heckman (1979) sample selection model with
where σwη denotes the covariance between w and η, φ(·) and Φ(·) are integrated standard normal density and distribution, respectively and the variance of η is normalized to be 1. Therefore, in the presence of sample attrition or selection, regressing yit on xit using only the observed information is invalidated by two problems—ﬁrst, the presence of the unobserved eﬀects αi, and second, the ‘selection bias’ arising from the fact that E (uit|xit, dit=1)=θ(λi+δwit).
When individual eﬀects are random and the joint distribution function of (u, η, γi, λi) is known, both maximum likelihood and two or multi-step estimators can be derived (e.g., Heckman 1979). The resulting estimators are consistent and asymptotically normally distributed. The speed of convergence is proportional to the square root of the sample size. However, if the joint distribution of u and η is mis-speciﬁed, then even without the presence of αi, both the maximum likelihood and Heckman (1979) two step estimators are inconsistent. This sensitivity of parameter estimate to the exact speciﬁcation of the error distribution has motivated interest in semiparametric methods.
The individual speciﬁc eﬀects in Eqn. (20) are easily eliminated by time diﬀerencing for those individuals that are observed for two time periods t and s, i.e., those individuals i satisfying dit=dis=1. However, the sample selectivity factors are not eliminated by time diﬀerencing. Ahn and Powell (1993) note that if θ is a suﬃciently ‘smooth’ function, and δ is a consistent estimator of δ, observations for which the diﬀerence (wit -w) δ is close to zero should have θit-θis ≈0. They propose a two-step procedure. In the ﬁrst step, consistent semiparametric estimates of the coeﬃcients of the ‘selection’ equation are obtained. The result is used to obtain estimates of the ‘single index, witδ,’ variables characterizing the selectivity bias in the index equation. The second step of the approach estimates the parameters of the equation of interest by a weighted instrumental variables regression of pairwise diﬀerences in dependent variables in the sample on the corresponding diﬀerences in explanatory variables: the weights put more emphasis on pairs with witδ≈wi, t−1 δ.
Kyriazidou (1997) generalizes this concept and proposes to estimate the ﬁxed eﬀects sample selection models in two steps: in the ﬁrst step, estimate δ by either Chamberlain (1980) conditional maximum likelihood approach or the Manski (1975) maximum score method. In the second step, the estimated δ is used to estimate γ, based on pairs of observations for which dit=dis=1 and for which (wit -wis)`δ is ‘close’ to zero. This last requirement is operationalized by weighting each pair of observations with a weight that depends inversely on the magnitude of (wit -wis)`δ, so that pairs with larger diﬀerences in the selection eﬀects receive less weight in the estimation. The Kyriazidou (1997) estimator takes the form
where K is a kernel density function which tends to zero as the magnitude of its argument increases and hN is a positive constant that decreases to zero as N→∞. Under appropriate regularity conditions, Eqn. (24) is consistent but the rate of convergence is much slower than the standard square root of the sample size.
Panel data have opened up avenues of research that simply could not have been pursued otherwise, but their power depends on the extent and reliability of the data as well as on the validity of the restrictions upon which the statistical methods have been built. Otherwise, panel data may provide a solution for one problem, but aggravate another. For instance, consider the income-schooling example given by Griliches (1979):
where y is a measure of income, S is a measure of schooling, A is an unmeasured ability variable which is assumed to be positively related to S. If S and A are uncorrelated, the least squares regression of y on S will yield consistent estimates of β1. If S and A are correlated, then the least squares estimator of β1 will suﬀer from omitted variable bias. If A is a purely ‘family’ variable in the sense that siblings have exactly the same level of A, then estimation of β1 from diﬀerences between the brothers’ earnings and diﬀerences between the brothers’ education is consistent. But, if ability, apart from having a family component also has an individual component related to the schooling variables, then the within family estimate of β is again biased and the bias is not necessarily less than that of the least squares estimate.
This example demonstrates that the usefulness of panel data in providing particular answers to certain issues depends on the validity of the assumptions implicit in the model. Since there is no universally acceptable way of modeling unobserved heterogeneity in panel data, it would be more fruitful to explicitly recognize the limitations of the data and focus attention on developing models and estimation methods based on what is observable, avoiding imposing arbitrary assumptions on the model and data. For additional discussions on panel data methodology, see Baltagi (1995), Hsiao (1986), and Matayes and Sevestre (1996).
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