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A Simple GMM Estimator for the Semiparametric Mixed Proportional Hazard Model

Govert E. Bijwaard, Geert Ridder and Tiemen Woutersen


Ridder and Woutersen (Ridder, G., and T. Woutersen. 2003. “The Singularity of the Efficiency Bound of the Mixed Proportional Hazard Model.” Econometrica 71: 1579–1589) have shown that under a weak condition on the baseline hazard, there exist root-N consistent estimators of the parameters in a semiparametric Mixed Proportional Hazard model with a parametric baseline hazard and unspecified distribution of the unobserved heterogeneity. We extend the linear rank estimator (LRE) of Tsiatis (Tsiatis, A. A. 1990. “Estimating Regression Parameters using Linear Rank Tests for Censored Data.” Annals of Statistics 18: 354–372) and Robins and Tsiatis (Robins, J. M., and A. A. Tsiatis. 1992. “Semiparametric Estimation of an Accelerated Failure Time Model with Time-Dependent Covariates.” Biometrika 79: 311–319) to this class of models. The optimal LRE is a two-step estimator. We propose a simple one-step estimator that is close to optimal if there is no unobserved heterogeneity. The efficiency gain associated with the optimal LRE increases with the degree of unobserved heterogeneity.

Corresponding author: Govert E. Bijwaard, Netherlands Interdisciplinary Demographic Institute (NIDI), PO Box 11650, NL-2502, AR, The Hague, The Netherlands

We thank Kei Hirano and Nicole Lott for very helpful comments. We also thank seminar participants at the University of Western Ontario, and the Netherlands Interdisciplinary Demographic Institute. This paper replaces the paper Method of Moments Estimation of Duration Models with Exogenous Regressors (2003). Financial support from NORFACE research programme on Migration in Europe – Social, Economic, Cultural and Policy Dynamics is gratefully acknowledged.

  1. 1

    Horowitz (2001, theorem 2.2) averages gn (Xi); the STATA program on our website is sufficiently fast to apply the bootstrap to most survey datasets.

  2. 2

    The Brent’s method combines the bisection method, the secant method and inverse quadratic interpolation. The idea is to use the secant method or inverse quadratic interpolation if possible, because they converge faster, but to fall back to the more robust bisection method if necessary. The secant method can be thought of as a finite difference approximation of the Newton-Raphson method. The Powell method extends the Brent method by searching in a specific direction, rather than changing one parameter at the time.

  3. 3
  4. 4

    In the MLE for models with duration dependence, we do not need the standard identification restriction that the unobserved heterogeneity term has mean one because the baseline hazard is normalized to be equal to 1 in the first interval.

  5. 5

    The Gâteaux derivative is a directional derivative; let

    and η>0 then df(x, a)=limη0[{f(x+)–f(x)}/η].

  6. 6

    Our calculations were done in Gauss 6.0 on 3 parallel computers: a Pentium 2.1 PC, a Pentium 2.8 PC and a Pentium 2.0 laptop. The calculations took about 9 weeks of CPU time.

  7. 7

    The LRE with a duration dependence on 10 intervals for a sample size of 500 did not converge in seven of the experiments. The average is therefore base on 93 experiments instead of 100.

  8. 8

    The results for the parameters of the piecewise constant duration dependence, α2 and α3, are given in Tables A3 and A4 in Appendix A.

  9. 9

    The Doob-Meyer decomposition theorem is a theorem in stochastic calculus stating the conditions under which a submartingale may be decomposed in a unique way as the sum of a martingale and a continuous increasing process, see Meyer (1963) and Protter (2005).

Appendix A: Additional tables

Table A1

Average Bias of Estimates of the Log α’s Across the Experiments with a Piecewise Constant Duration Dependence on 4 Intervals.

Estimation methodSample size
MLE no heteroα2–0.0480*–0.0319*–0.0095*
MLE 2 pointsα20.02820.02570.0140*


Table A2

Average Bias of Estimates of the Log α’s Across the Experiments with a Piecewise Constant Duration Dependence on 10 Intervals.

Sample sizeSample size
MLE no heteroMLE 2 points

For sample size of 500 based on 93 experiments, because in seven experiments the estimation procedure did not convergence . *p<0.05.

Table A3

Average Bias, Standard error and RMSE of Estimates of Parameters of Piecewise Constant Baseline Hazard Across the Experiments, Second set of Monte Carlo experiments.

Duration dependenceEstimation methodBiasStd errorRMSE
Positive duration dependenceMLE gammaα20.00690.00960.0118
Negative duration dependenceMLE gammaα20.02110.01110.0239
U-shaped duration dependenceMLE gammaα2–0.00090.00970.0097
Inverse U duration dependenceMLE gammaα20.01020.01040.0146

For each DGP (gamma mixture) 100 simulations with 1000 observations each. *p<0.05

Table A4

Average Bias, Standard error and RMSE of Estimates of Parameters of Piecewise Constant Baseline Hazard Across the Experiments, Second set of Monte Carlo Experiments, Censored Sample.

Duration dependenceEstimation methodBiasStd errorRMSE
Positive duration dependenceMLE gammaα20.00100.01350.0135
Negative duration dependenceMLE gammaα20.0347*0.01310.0371
U-shaped duration dependenceMLE gammaα20.00520.01330.0143
Inverse U duration dependenceMLE gammaα20.01370.01230.0184

For each DGP (gamma mixture) 100 simulations with 1000 observations each. *p<0.05

Appendix B: Proofs and Technical Details

Technical Details Section 2: A Counting Process Approach

The counting process approach is a very useful framework for analyzing duration data since an indicator can be used to denote whether a transition happened or not. Andersen et al. (1993) have provided an excellent survey of counting processes. Less technical surveys have been given by Klein and Moeschberger (1997), Therneau and Grambsch (2000), and Aalen et al. (2009). The main advantage of this framework is that it allows us to express the duration distribution as a regression model with an error term that is a martingale difference. Regression models with martingale difference errors are the basis for inference in time series models with dependent observations. Hence, it is not surprising that inference is much simplified by using a similar representation in duration models.

To start the discussion, we first introduce some notation. A counting process {N(t)|t≥0} is a stochastic process describing the number of events in the interval [0, t] as time proceeds. The process contains only jumps of size +1. For single duration data, the event can only occur once because the units are observed until the event occurs. Therefore we introduce the observation indicator Y(t)=I(Tt) that is equal to one if the unit is under observation at time t and zero after the event has occurred. The counting process is governed by its random intensity process, Y(t)κ(t), where κ(t) is the hazard in (2). If we consider a small interval (tdt] of length dt, then Y(t)κ(t) is the conditional probability that the increment dN(t)=N(t)–N(t–) jumps in that interval given all that has happened until just before t. By specifying the intensity as the product of this observation indicator and the hazard rate, we effectively limit the number of occurrences of the event to one. It is essential that the observation indicator only depends on events up to time t.

Usually we do not observe T directly. Instead we observe

with g a known function and C a random vector. The most common example is right censoring, where g(T, C)=min (T, C). By defining the observation indicator as the product of the indicator I(tT) and, if necessary, an indicator of the observation plan, we capture when a unit is at risk for the event. In the case of right censoring Y(t)=I(tT)I(tC), and in all cases of interest we have Y(t)=I(tT)IA(t) with A a random set that may depend on random variables. We assume that C and T are conditionally independent given X. The history up to and including t, Yh(t) is assumed to be a left continuous function of t. The history of the whole process also includes the history of the covariate process, Xh(t), and V. Thus, we have

The sample paths of the conditioning variables should be up to t–, but because these paths are left continuous we can take them up to t. A fundamental result in the theory of counting processes, the Doob-Meyer decomposition,9 allows us to write

where M(t), t≥0 is a martingale with conditional mean and variance given by

The (conditional) mean and variance of the counting process are equal, so the disturbances in (B.2) are heteroscedastic. The probability in (B.1) is zero, if the unit is no longer under observation. A counting process can be considered as a sequence of Bernoulli experiments because if dt is small, (B.3) and (6) give the mean and variance of a Bernoulli random variable. The relation between the counting process and the sequence of Bernoulli experiments given in (B.2) can be considered as a regression model with an additive error that is a martingale difference. This equation resembles a time-series regression model. The Doob-Meyer decomposition is very helpful to the derivation of the distribution of the estimators because the asymptotic behavior of partial sums of martingales is well-known.

Technical Details Section 3: Assumptions 1–4

To simplify the expressions, we use the notation hi(t, θ)= hi(t, Xh,I (t), θ).

  1. The conditional distribution of T given X(‧) and V has hazard rate

    with X(‧) a K covariate bounded stochastic process that is independent of V and such that if the probability of the event

    some set S with positive measure and for some constants c1, c2, then c1=c2=0. For the baseline hazard, 0<limt0λ(t, α0)<∞.

  2. For the covariate process X(t), t≥0, we assume that the sample paths are piecewise constant, i.e., its derivative with respect to t is 0 almost everywhere, and left continuous. The hazard that is not conditional on V is

    The observation process is Y(t), t≥0 with Y(t)=I9(tT)I(tC) and we assume

    The support of C is bounded.

  3. The parameter vector θ=(β′, α′)′ is an M vector with β a K vector and α an L vector. The parameter space Θ is convex. The baseline hazard λ(t, α)>0 and is twice differentiable and the second derivative is bounded in α (in the parameter space) and t.

  4. The weight function

    is an M vector of bounded and left continuous functions. If

    then there are functions μ(u, θ) (an M vector), Vβ (u, s, θ) (an M×K matrix), and Vα (u, s, θ) (an M×L matrix) such that




We assume that the M×M matrix [B(θ0) A(θ0)] is nonsingular.

The restriction on the baseline hazard in Assumption A1 ensures identification (see Section 3) and guarantees that the semiparametric information bound is nonsingular (see below). Assumption A2 states that the covariates and the observation indicator are predetermined. Assumption A4 is about smoothness: Suppose that one censors all the data at u=τ+ψ then the expressions in equation (30) and (31) do not change if the value of ψ varies. The derivation of the asymptotic distribution of the LR estimator follows the proof in Tsiatis (1990). Tsiatis requires that the density of U0 is bounded. For the MPH model, this density is

If E(V)=∞, this density is not bounded at u0=0. Inspection of Tsiatis’ proof shows that this does not change the result, and we do not need to impose the restriction that E(V) is finite. The transformed durations are observed up to τ with τ<∞ such that for some ψ,η>0

Pr[min (U0, C) > τ+ψ]≥η.

In the MPH model, this is just an assumption on the distribution of C because for U0 it is satisfied for all τ<∞.

Technical Details Section 4: Lemma 2–3

Lemma 2: If the derivative of κ is bounded on [0, τ] then for ε>0 with


we have

for u1, u2 with 0<u1<u2<τ.

If Yh,N(t) is bounded away from zero on [0, τ] for large N, then (B.14) and (B.15) imply that if bN=Nc for

Note that the uniform convergence holds on a compact subset of [0, τ]. Although this can be generalized to uniform convergence on [0, τ], the variable kernels that are needed for this generalization complicate the asymptotic analysis. In practice, estimation of the hazard is inaccurate near the endpoints, and it may be preferable to exclude observations that are close to the endpoints. Note that the observations near the endpoints are used in the estimation of the hazard. Also, using a bandwidth proportional to N–1/5 and
satisfies all the assumptions of this paper.

We do not observe the transformed duration

but rather an estimate
of this transformed duration, and hence we consider the kernel estimator

Lemma 3: The kernel K is positive and bounded on [–1, 1] (and zero elsewhere) and satisfies a Lipschitz condition on this interval. The covariate process X(t) is bounded on [0, τ] and so is

for all α in an open neighborhood of α0. Moreover

uniformly for 0≤uτ, θ∈N(θ0) and H has derivatives that are bounded for 0≤uτ, θ∈N(θ0). Then for ε>0 such that

we have

Proof: See below.

Note that the conditions on bN are determined in Lemma 2 and that a bandwidth proportional to N–1/5 and

satisfies all the assumptions of this paper. The fact that we use estimated transformed durations does not change the restrictions on the bandwidth choice.

At this point we consider the condition in (B.18) more closely. With

if the duration T is (right) censored at C, Y(t)=I(Tt)I(Ct), so

YU (u, θ)=I(h(T, θ)≥u)‧I(h(C, θ)≥u).

If the censoring time and the duration are conditionally independent given the history up to t, i.e.,


If N(θ0) is an open neighborhood of θ0, Xi and Ci are i.i.d., and


and by the uniform law of large numbers

uniformly for θN(θ0) and 0≤uτ. Because by (B.23) the limit is bounded away from zero, we have

uniformly for θN(θ0) and 0≤uτ with

Because h(T,θ0)=U0, (B.19) holds for θ=θ0 if κ0(u) is bounded for 0≤uτ. From the expression for κU (u, θ) in (9), a sufficient condition for κU(u, θ) to be bounded for all θ in a neighborhood of θ0 and 0≤tτ is that λ(t, α)>0 for all t and on a neighborhood of α0. In the same way, (B.20) holds if the hazard of C is bounded and λ(t, α) is bounded away from zero in a neighborhood around α0.

Proof of Lemma 1

is a linearization of
Because SN(θ) is not continuous in θ, it is not possible to linearize this function by a first order Taylor series expansion. Instead we linearize the hazard rate of the transformed durations U(θ). From (4) and (5) we obtain

This relates the hazard of the distribution of U(θ) to that of U0

Because h(h–1(u, θ), θ)=u, we have

The derivatives of κU(u, θ) with respect to θ are

where the last equality follows from a change of variables in the integral. In the same way, we obtain with a change of variable in the integral

The proof consists of checking the conditions for asymptotic linearity of SN(θ) in Tsiatis (1990) and a computation of the coefficients in the linear approximation. In Tsiatis’ proof the covariate in the estimating equation is Xi. We have

and hence the requirement that this is a vector of bounded functions. The equations (9), (10) and (11) are stability conditions [see also Andersen et al. (1993)]. Instead of a mean and variance condition as in Tsiatis (1990), we have a mean and two covariance conditions. Note that by setting s=u, we obtain conditions for uniform convergence to Vα (u, u) and Vβ (u, u). The final condition for linearization is that for u≤τ

The assumptions that λ(t,α) is bounded away from zero for all t≥0 and α in the parameter space, that

for all t≥0 and α in the parameter space, and that X(t) is bounded, imply that the second derivative of κU(u, θ) with respect to θ is bounded for all uτ and θ∈Θ. This is sufficient for (B.31) if the parameter space is convex.

Next we linearize SN(θ). Because

we have if |θθ0| is small

The second term is after substitution of (B.29), and (B.30)

The normalized vectors of coefficients converge to (B.12) and (B.13) if (B.10) and (11) hold. This proves the lemma.

Proof of Theorem 1

By van der Vaart (1998) Theorem 5.45, we have from Lemma 1

with M0 the martingale associated with the counting process N0 for U0. By the central limit theorem for integrals of predetermined functions with respect to a martingale, [see e.g., Anderson et al. (1993)], the sum on the right-hand side converges to a normal distribution with the variance matrix in (24).

Proof of Lemma 2 and 3

We have

We first consider the second term. Because K is Lipschitz this is bounded by

Moreover by the mean value theorem, we have that for some intermediate

Because Xi(t) is bounded on [0, τ] and so is

for all α in an open neighborhood of α0, (B.36) is bounded by
and substitution in (B.35) gives the upper bound

Because the estimator

consistent, the upper bound converges to 0 in probability if

Next we consider the first term in (B.34). By subtraction and addition of expected values, this term is bounded by

The first and second terms converge to 0 in probability if

Because of (B.18) the final term converges in probability to

This expression is bounded (both H and K are bounded) by

The first term goes to 0 in probability if

and the second if
This completes the proof.


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Published Online: 2013-05-28
Published in Print: 2013-07-01

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