Estimating Poisson pseudomaximumlikelihood rather than loglinear model of a logtransformed dependent variable
Abstract
Purpose
The purpose of this study is to account for a recent nonmainstream econometric approach using microdata and how it can inform research in business administration. More specifically, the paper draws from the applied microeconometric literature stances in favor of fitting Poisson regression with robust standard errors rather than the OLS linear regression of a logtransformed dependent variable. In addition, the authors point to the appropriate Stata coding and take into account the possibility of failing to check for the existence of the estimates – convergency issues – as well as being sensitive to numerical problems.
Design/methodology/approach
The author details the main issues with the loglinear model, drawing from the applied econometric literature in favor of estimating multiplicative models for noncount data. Then, he provides the Stata commands and illustrates the differences in the coefficient and standard errors between both OLS and Poisson models using the health expenditure dataset from the RAND Health Insurance Experiment (RHIE).
Findings
The results indicate that the use of Poisson pseudo maximum likelihood estimators yield better results that the loglinear model, as well as other alternative models, such as Tobit and twopart models.
Originality/value
The originality of this study lies in demonstrating an alternative microeconometric technique to deal with positive skewness of dependent variables.
Keywords
Citation
Motta, V. (2019), "Estimating Poisson pseudomaximumlikelihood rather than loglinear model of a logtransformed dependent variable", RAUSP Management Journal, Vol. 54 No. 4, pp. 508518. https://doi.org/10.1108/RAUSP0520190110
Download as .RISPublisher
:Emerald Publishing Limited
Copyright © 2019, Victor Motta.
License
Published in RAUSP Management Journal. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and noncommercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
Researchers in the different fields within business administration often estimate models with a logtransformed dependent variable. The main reasons for log transforming the outcome variable include dealing with a positively skewed variable as well as interpreting a covariate as either elasticity or having a multiplicative response (Manning, 1998). An unfortunate consequence of this approach, however, is that the estimated coefficients are relevant to the distribution of the logtransformed dependent variable rather than to the distribution of the dependent variable in their natural units. As a result, coefficients from the logtransformed ordinary least squares (OLS) model are often retransformed back to unlogged terms to make inferences in their natural units.
The retransformed estimate of either the conditional mean or the impact of an independent variable on the dependent variable – the slope – needs to adjust for both heteroskedasticity and the distribution of the residual (Mullahy, 1998). Failure to account for both may lead to biased estimates of the conditional mean and the slope on its original scale. The presence of heteroskedasticity can generate different estimates in loglinear models rather than estimated in levels. This suggests that inferences drawn on loglinear regressions may produce misleading conclusions.
Although suggestions have been offered in favor of estimating loglinear models to inform about the conditional mean of the distribution of the dependent variable, they rely on strong underlying assumptions that may not hold. Among the several models used to correct the issues of coefficient biasedness and heteroskedasticity in loglinear models, the Poisson pseudomaximumlikelihood estimator is a robust substitute for the standard loglinear model (Silva & Tenreyro, 2006).
The purpose of this paper is to account for a recent nonmainstream econometric approach using microdata and how it can inform research in business administration. More specifically, the paper draws from the applied microeconometric literature stances in favor of fitting Poisson regression with robust standard errors rather than the OLS linear regression of a logtransformed dependent variable. In addition, we point to the appropriate Stata coding and take into account the possibility of failing to check for the existence of the estimates – convergency issues – as well as being sensitive to numerical problems.
The remainder of the paper proceeds as follow. Section 2 details the main issues with the loglinear model, while Section 3 draws from the applied econometric literature in favor of estimating multiplicative models for noncount data. Section 4 provides the Stata commands, while Section 5 illustrates the differences in the coefficient and standard errors between both OLS and Poisson models using the health expenditure dataset from the RAND Health Insurance Experiment (RHIE). Section 6 concludes the paper.
2. Main issues with loglinearized model
Jensen’s inequality implies that E(In y) ≠ In E(y), that is, the expected value of the logarithm of a random variable is different from the logarithm of its expected value. An important implication of Jensen’s inequality is that interpreting the parameters of loglinear models estimated by OLS as elasticities may be misleading in the presence of heteroskedastic. The use of the logtransformed dependent variable creates a potential bias when computing estimates of E[yx] on the original scale provided the residual term does not have a normal distribution or is heteroskedastic. As Silva and Tenreyro (2006) posit, estimating the loglinear model
First, loglinearization is not feasible if y_{i} = 0 since In 0 = –∞. In addition, even if all observations of y_{i} > 0, the expected value of the loglinear residual will depend on the vector of covariates. Therefore, estimating by OLS will yield in inconsistent estimators. For instance, consider a model:
To obtain a consistent estimator of the slope parameters of y_{i} estimating the loglinear equation above by OLS, it is necessary that E [ln η_{i}x] does not depend on x_{i}. In addition, consistent estimation of the intercept also requires that E[ln η_{i}x] = 0 Since
Since y_{i} > 0, the probability of y_{i} approaches zero when E(y_{i}x_{i}) approaches zero. This implies that the conditional variance of y_{i}, Var(y_{i}x_{i}) tends to disappear as E(y_{i}x_{i}) approaches zero. However, it may be possible to observe large deviations from the conditional mean – thus leading to greater dispersion – when the expected value of y_{i} is far away from its lower bound. The residual term ε_{i} is likely heteroskedastic and its variance will depend on
3. Using the Poisson pseudomaximumlikelihood estimator
A possible way of obtaining a more efficient estimator without resorting to nonparametric regression is to estimate the parameters of interest using a pseudomaximumlikelihood estimator based on some assumption of the functional form of Var(y_{i}  x_{i}) (Manning & Mullahy, 2001; Papke & Wooldridge, 1996). Among possible specifications, under the assumption that the conditional variance is proportional to the conditional mean,
The estimator defined below is numerically equal to the Poisson pseudomaximumlikelihood (PPML), often used for count data. The form of the equation implies that the correct specification of the conditional mean,
The implementation of the pseudomaximumlikelihood is estimated via Poisson regression even when the dependent variable is not an integer. However, because the assumption Var(y_{i}x_{i}) ∝ E{y_{i}x_{i}} is unlikely to hold, this estimator does not take full account of the heteroskedasticity in the model. As a result, the inference has to be based on an Eicker–White robust covariance estimator (Eicker, 1963; White, 1980).
The Poisson regression model is defined by:
Poisson regression is not only the most widely used model for count data (Cameron & Trivedi, 1986), but it is also becoming increasingly popular to estimate multiplicative models for other kinds of data (Blackburn, 2007; Manning & Mullahy, 2001).
The reasons that make this estimator popular can be clearly understood by inspecting the corresponding score vector and Hessian matrix, given respectively below:
The form of the score vector makes it possible that β will be consistently estimated as long as
Running a Poisson regression with robust standard errors may be preferred to estimating a loglinear model by OLS. First, Poisson handles zero outcomes that arise in correspondence to the model. However, Poisson regression does not handle cases where some individuals participate, and others do not, and among the nonparticipating ones, they would likely product an outcome greater than 0 had they participated. For instance, Poisson does not handle zeros in a Mincerian income model (Mincer, 1958) since those that earned 0 did not participate in the labor force. Had they participated, their earnings might have been low, but they would be positive. More recent studies using the Poisson model with robust standard errors rather than loglinear regression have examined the impact of medical marijuana laws on addictionrelated to pain killers (Powell, Pacula, & Jacobson, 2018), medical care spending and labor market outcomes (Powell & Seabury, 2018), innovation and production expenditure (Arkolakis et al., 2018) and tourism and economic development (Faber & Gaubert, 2019), among many other studies.
4. Commands using Stata
This section briefly describes the Poisson commands in Stata, including some of its shortcomings.
OLS regressions of the algebraic form ln y_{i} = β_{0} + β_{1}x_{1}_{i} + β_{2}x_{2}_{i} + ⋯ + β_{k}x_{ki} is usually coded using the following Stata command:
generate lny = ln(y); and
regress lny x1 x2 […] xk.
Rather than estimating this loglinear model, we would instead fit a Poisson regression using the HuberWhiteSandwich linearized estimator of variance. In Stata this is done with the following command:
poisson y x1 x2 … xk, vce(robust).
Note that there is no need to take the natural log of the dependent variable. The Poisson regression with robust standard errors specify that the variancecovariance matrix neither assumes E(y_{i}) = Var(y_{i}), nor requires Var(y_{i}) to be constant across all i. Therefore, the Poisson regression with robust standard errors (HuberWhiteSandwich linearized estimator of variance) is an alternative to loglinear regressions.
The estimator is also wellbehaved since the Hessian is negative definite for all x and β. This facilitates the estimation and ensures the uniqueness of a maximum, conditional on its existence. As a result, estimation of β converges in a few iterations. However, the parameters in β are not identified by PPML for certain data configurations because they do not exist. The nonexistence of PPML estimates are more likely when the data have a large number of zeros, such as the number of crimes committed, volume of trade between pairs of countries, among others (Silva & Tenreyro, 2011a). Since this type of identification problem has not been widely recognized as a major issue in count data models, Stata’s Poisson command does not check for its presence.
In such cases, checking whether or not the results obtained actually correspond to a maximum of the loglikelihood function is recommended. We can check for this through the overfitting of the observation with y_{i} = 0 by computing descriptive statistics for the fitted values of y for the relevant sub sample. Silva and Tenreyro (2011b) identify and illustrate some shortcomings of the Poisson command in Stata. More specifically, they point out that the command fails to check for the existence of estimates and show that it is sensitive to numerical problems. The Poisson command does not check for the existence of the estimates and therefore, it is unable to identify whether convergence is not achieved or spurious.
In addition, even if maximum likelihood estimates of the Poisson regression exist, Stata may not correctly identify them due to its sensitivity to numerical problems of the algorithms available in the Poisson command in three situations: when the dependent variable has some very large values, when regressors are highly collinear and have different magnitudes, and when the covariates are highly (although no perfectly) collinear. A potential solution to explore when the maximum likelihood estimates exist but convergence is not achieved is to use different optimization methods offered in the Poisson command, such as the NR, BHHH, DFP and BFGS. One can also relax the convergence criteria and ensure convergence, by the algorithm may not deliver the desired maximum likelihood estimates.
A simple way to deal with the shortcomings of Stata’s Poisson command is to use the glm command for the generalized linear model with the options family (Poisson) link(log) IRLS. The iterated reweighted least squares (IRLS) algorithm provided by the GLM command seems to be more stable than the algorithms in Poisson command and give the correct results, overcoming the command’s limitations. To facilitate the estimation of Poisson regressions, the existence of the pseudo maximum likelihood estimates can be checked through the PPML command, offering methods to drop regressors that may cause the nonexistence of the estimates. The command also warns if the variables have large values likely to create numerical problems. Estimation can be then implemented using the generalized linear model (GLM) method.
5. RAND health insurance experiment (RHIE health expenditure dataset
To illustrate the use of Poisson pseudo maximum likelihood rather than loglinear models, use data from the RAND Health Insurance Experiment (RHIE). The experiment, conducted by the RAND corporation from 1974 to 1982, has been the longest running and largest controlled social experiment in medical care research. The main goal of the experiment to assess how the patient’s use of health services is affected by types of randomly assigned health insurance, including both feeforservice and health maintenance organizations (HMOs). In the experiment, the data were collected from about 8,000 enrollees in 2,823 families, from six sites across the USA. Each family was enrolled in one of 14 different health insurance plans for either three or five years. The plans ranged from free care to 95 per cent coinsurance below a maximum dollar expenditure (MDE), and also included an assignment in a prepaid group practice. RHIE dataset consists of utilization, expenditures, demographic characteristics, health status and insurance status variables. The final sample consists of 20190 observations; each observation represents data for an experimental subject in a given year.
Several of the RHIE studies on health expenditures relies on regression models with logged dependent variables. With standard deviations two to four times the mean, the log transformation was essential to finding estimates of the response of health care expenditures that were robust to the skewness in the data (Duan, 1983). In several analyses, the residual errors indicated the presence of heteroskedasticity by insurance plan, the main covariate of interest.
The central point here is that we do not face the problem of endogenous treatment effect – the central causal parameter of interest in the study – since insurance plans are randomly assigned, not freely chosen by the participant. Data were collected from the enrollee’s use of medical care services and health status throughout the randomly assigned term of enrollment for either three or five years. For additional details of the data, see Manning et al. (1987) and Deb and Trivedi (2002). The sample used in this study consists of secondyear data for individuals in the feeforservice plans only.
To illustrate the main issues, Table I reports the first four moment generating functions, mean, variance, skewness and kurtosis, as well as the percentiles. Medical expenditure is heavily skewed to the right and kurtotic. The standard deviation is four times the mean. In addition, the mean of $169.70 is much larger than the median of $32.38. As a result, using a natural logarithmic transformation of the dependent variable, medical expenditure, to perform a loglinear model has become the standard in both business and applied microeconomic work. Once the estimates from such a model are obtained, the usual practice is to interpret the response to a particular covariate as being the exponential of the coefficient of that variable in the model. As regressors, we include health insurance variables, socioeconomic characteristics and heathstatus variables. Table II contains the list of all regressors in our model.
Table III displays the descriptive statistics of the logtransformed medical expenses. The logarithmic transformation eliminates this skewness, with a mean of 4.07 close to the median of 3.96, and the skewness statistics falls from 27.03 to 0.35. The kurtosis is 3.29, close to the normal value of 3. Table IV displays the estimation outcomes resulting from various techniques. The first column reports OLS estimates using the logarithm of medical expenses as the dependent variable. As noted before, this regression leaves out individuals with no medical expenditure (about 23 per cent of the observations). The second column reports the OLS estimates using the logarithm transformation of 1 plus medical expenses, ln(1 + meddol), as the dependent variable to deal with the zeros. The fourth column uses reports Poisson estimates using only the subsample of positive medical expenditure while the last column shows the Poisson results for the whole sample (including observations with zero medical expenditure).
The main point to notice is that the Poisson estimated coefficients are similar using the entire sample and using the positive expenditure sample only. However, most coefficients differ from those obtained using a loglinear model. This suggests that in this case, heteroskedasticity may be responsible for the differences in the results between Poisson with robust standard errors and those of OLS (Wooldridge, 2010). Further evidence using the Breusch–Pagan/Cook–Weisberg test for heteroscedasticity, rejects the hull hypothesis of homoskedasticity (χ^{2} = 17.81 pvalue = 0.0000).
In addition, we have included the distribution of residuals for all four models. Figures 1 and 2 display the quantiles of residuals against the quantiles of the normal distribution. For both Poisson models, we used deviance residuals since they have the best properties for examining the goodness of fit of Generalized Linear Models, such as a Poisson family. The results indicate that Poisson provides a better fit.
5.1 Alternative models: Tobit and twopart models
Alternatively, other models could be considered, such as the Tobit, and twopart or hurdle models. The Tobit model could be considered since medical expenditures are leftcensored at zero. For instance, 23 per cent of the observations had no medical expenditure for year 2. A potential approach would be to put a small number a for every zero (smaller than the smallest observed positive y), take the log and then specify ln a as the leftcensoring point (Cameron & Trivedi, 2005). However, the choice of a is arbitrary and affects the estimation. For instance, choosing a = 0.01 results in lny˜ = −4.6 and choosing a = 0.000001 results in ln y = –13.8, and there is not any clear reason to prefer one over the other when the smallest positive y is 1. In addition, the Tobit model has strong assumptions of normality and homoscedasticity. If these assumptions fail, then the Tobit maximum likelihood estimator is not robust. Tobit also assumes that a single mechanism drives the two dimensions of the expenditure data.
To relax the latter assumption and to investigate if there is indeed a single mechanism we can use hurdle or twopart models, described by Mullahy (1986). The model involves estimating two separate regressions: the first models the probability that y is positive, while the second models the amount of y if y is positive. Using our RHIE dataset, for example, the idea is that a person decides whether to go to the doctor and then the doctor decides the expenditure conditional on y > 0. As a result, the first model can be fitted using a probit (or logit, complementary log log, etc.) using 1 (y > 0) as a dummy outcome, then run OLS regression ln y on the vector of regressors, or a truncated regression of y on the vector of regressors (Cragg, 1971; McDowell, 2003).
Unlike the Tobit model, the twopart model features two residuals: v, which impacts the decision to set y > 0 instead of y = 0, and u, which impacts y conditional on positive y. An important assumption underlying the twopart model is that v and u are independent. In other words, the unobservables which affect the decision to go to the doctor are independent of the unobservables that affect the decision of how much to spend. A potential drawback in using twopart models is that it may be difficult to include endogenous explanatory variables without strong maximum likelihood assumptions. In addition, a twostep assumption, in this case, may not be all too realistic since one may find herself getting medical care without any decision on her part, or one can also end her medical care provided she chose too. As a result, we would need more than two steps of the model to be correctly specified, or all the estimates would be inconsistent.
6. Conclusion
Coefficients from the logtransformed ordinary least squares (OLS) model are often retransformed to unlogged terms to make inferences in their natural units. Failure to account for adjustments for heteroskedasticity and normality of residuals may lead to biased estimates of the conditional mean and the slope on its original scale. This suggests that inferences drawn on loglinear regressions may produce misleading conclusions. Among the several models used to correct the issues of coefficient biasedness and heteroskedasticity in loglinear models, the Poisson pseudomaximumlikelihood. This study drew from the applied microeconometric literature in favor of fitting Poisson regression with robust standard errors rather than the OLS linear regression of a logtransformed dependent variable. We applied both models in a health expenditure dataset to show the main differences.
Figures
Descriptive statistics of medical expenditure
Medical exp excl outpatient men  

(%)  Percentiles  Smallest  
1  0  0  
5  0  0  
10  0  0  Obs  5,575 
25  3.849658  0  Sum of wgt.  5,575 
50  32.37693  Mean  169.7003  
Largest  SD  802.7604  
75  101.2285  12044.11  
90  330.9775  17465.98  Variance  644424.2 
95  732.6303  18641.98  Skewness  27.03142 
99  2232.54  39182.02  Kurtosis  1113.741 
List of explanatory variables
Explanatory variable  Definition 

logc  ln(coinsurance + 1), 0 ≤ coinsurance ≤ 100 
idp  1 if individual deductible plan, 0 otherwise 
lpi  ln(max(1, annual participation incentive payment)) 
fmde  0 if idp = 1,

linc  ln(family income) 
lfam  ln (family size) 
female  1 if person is a woman 
child  1 if age is less than 18 
fchild  Female*child 
black  1 if race of household head is black 
educdec  Education of the household head in years 
physlim  1 if the person has a physical limitation 
disea  Number of chronic diseases 
hlthg  1 if selfrated health is good 
hlthf  1 if selfrated health is fair 
hlthp  1 if selfrated health is poor 
Omitted category is excellent selfrated health 
Descriptive statistics of the logtransformed medical expenditure
Lnmeddol  

(%)  Percentiles  Smallest  
1  0.746548  −0.5343859  
5  1.749707  −0.4108706  
10  2.238203  −0.3899609  Obs  4,282 
25  3.059381  −0.3899609  Sum of Wgt.  4,282 
50  3.963396  Mean  4.069336  
Largest  SD  1.499219  
75  4.915971  9.396331  
90  6.11767  9.76801  Variance  2.247659 
95  6.807192  9.833171  Skewness  0.347961 
99  7.888451  10.57597  Kurtosis  3.28978 
Estimation outcomes from various techniques
Variable list  OLS  OLS2  Poisson y > 0  Poisson 

logc  −0.0190 (0.0313)  −0.144*** (0.0371)  0.00791 (0.0563)  −0.0205 (0.0562) 
idp  −0.0777 (0.0618)  −0.200*** (0.0721)  −0.0200 (0.141)  −0.0704 (0.132) 
lpi  0.00433 (0.00970)  0.0344*** (0.0118)  0.0289 (0.0176)  0.0382** (0.0177) 
fmde  −0.0297 (0.0181)  −0.0118 (0.0219)  −0.0290 (0.0339)  −0.0276 (0.0348) 
linc  0.101*** (0.0216)  0.125*** (0.0238)  0.133 (0.0847)  0.168* (0.0928) 
lfam  −0.159*** (0.0456)  −0.146*** (0.0554)  −0.213 (0.169)  −0.223 (0.170) 
female  0.334*** (0.0570)  0.732*** (0.0708)  −0.0660 (0.178)  0.0652 (0.179) 
child  −0.416*** (0.0676)  −0.186** (0.0813)  −0.767*** (0.182)  −0.731*** (0.183) 
fchild  −0.340*** (0.0896)  −0.738*** (0.108)  0.153 (0.236)  0.0202 (0.240) 
black  −0.194*** (0.0677)  −0.853*** (0.0758)  −0.100 (0.141)  −0.284** (0.144) 
educdec  −0.00265 (0.00820)  0.0353*** (0.0101)  0.0284 (0.0275)  0.0376 (0.0281) 
disea  0.0215*** (0.00339)  0.0395*** (0.00430)  0.0122** (0.00592)  0.0172*** (0.00612) 
physlm  0.276*** (0.0685)  0.461*** (0.0886)  0.477*** (0.141)  0.513*** (0.140) 
hlthg  0.151*** (0.0483)  0.160*** (0.0588)  0.198* (0.119)  0.224* (0.120) 
hlthf  0.383*** (0.0878)  0.497*** (0.108)  0.522** (0.224)  0.588*** (0.227) 
hlthp  0.817*** (0.170)  1.221*** (0.223)  1.579*** (0.529)  1.711*** (0.541) 
_cons  3.242*** (0.211)  1.496*** (0.243)  3.863*** (0.858)  3.123*** (0.926) 
N  4281  5574  4281  5574 
***Significance at 0.01; **significance at 0.05 and *significance at 0.1
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Acknowledgements
Victor Motta was the only contributor to this paper.