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Journal of Econometrics-Volume 141, Issue 2, Pages 323-1420 (December 2007)
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1. Editorial Board
2. Realized range-based estimation of integrated variance
Pages 323-349
Kim Christensen and Mark Podolskij
We provide a set of probabilistic laws for estimating the quadratic variation of continuous semimartingales with the realized range-based variance—a statistic
that replaces every squared return of the realized variance with a normalized squared range. If the entire sample path of the process is available, and under a
set of weak conditions, our statistic is consistent and has a mixed Gaussian limit, whose precision is five times greater than that of the realized variance. In
practice, of course, inference is drawn from discrete data and true ranges are unobserved, leading to downward bias. We solve this problem to get a
consistent, mixed normal estimator, irrespective of non-trading effects. This estimator has varying degrees of efficiency over realized variance, depending on
how many observations that are used to construct the high–low. The methodology is applied to TAQ data and compared with realized variance. Our findings
suggest that the empirical path of quadratic variation is also estimated better with the realized range-based variance.
3. Instrumental variable estimation based on conditional median restriction
Pages 350-382
Shinichi Sakata
We develop a method, named the IV estimator, to estimate structural equations based on the conditional median restriction imposed on the error terms. We
study its asymptotic behavior and show how to estimate its asymptotic covariance matrix. We also discuss the point identification in the IV estimation and
propose an over-identifying restriction test. We further demonstrate the performance of the IV estimator in comparison with the familiar two-stage least
squares estimator. The proposed method is applied to estimate the labor supply and wage offer functions for working, married women.
4. Generalized R-estimators under conditional heteroscedasticity
Pages 383-415
Kanchan Mukherjee
In this paper, we extend the classical idea of Rank estimation of parameters from homoscedastic problems to heteroscedastic problems. In particular, we
define a class of rank estimators of the parameters associated with the conditional mean function of an autoregressive model through a three-steps
procedure and then derive their asymptotic distributions. The class of models considered includes Engel's ARCH model and the threshold heteroscedastic
model. The class of estimators includes an extension of Wilcoxon-type rank estimator. The derivation of the asymptotic distributions depends on the uniform
approximation of a randomly weighted empirical process by a perturbed empirical process through a very general weight-dependent partitioning argument.
5. Incidental trends and the power of panel unit root tests
Pages 416-459
Hyungsik Roger Moon, Benoit Perron and Peter C.B. Phillips
The asymptotic local power of various panel unit root tests is investigated. The (Gaussian) power envelope is obtained under homogeneous and
heterogeneous alternatives. The envelope is compared with the asymptotic power functions for the pooled t-test, the Ploberger and Phillips [2002. Optimal
testing for unit roots in panel data. Mimeo] test, and a point optimal test in neighborhoods of unity that are of order n-1/4T-1 and n-1/2T-1, depending on
whether or not incidental trends are extracted from the panel data. In the latter case, when the alternative hypothesis is homogeneous across individuals, it is
shown that the point optimal test and the Ploberger–Phillips test both achieve the power envelope and are uniformly most powerful, in contrast to point optimal
unit root tests for time series. Some simulations examining the finite sample performance of the tests are reported.
6. Non-parametric estimation of sequential english auctions
Pages 460-481
Bjarne Brendstrup
I propose an empirical strategy to identify and to estimate non-parametrically the distribution and the density of latent valuations from the winning bids at
sequential oral, ascending-price (hereafter English) auctions within the independent private-values paradigm. I evaluate the asymptotic and finite-sample
properties of my approach, and the estimation strategy is applied to daily data from a fish auction held in Grenaa, Denmark, between January 2, 2000 and
March 31, 2004.
7. On the uniqueness of optimal prices set by monopolistic sellers
Pages 482-491
Gerard J. van den Berg
This paper considers price determination by monopolistic sellers who know the distribution of valuations among the potential buyers. We derive a novel
condition under which the optimal price set by the monopolist is unique. In many settings, this condition is easy to interpret, and it is valid for a very wide range
of distributions of valuations. The results carry over to the optimal minimum price in independent private value auctions. In addition, they can be fruitfully
applied in the analysis of quantity discount price policies.
8. On the second-order properties of empirical likelihood with moment restrictions
Pages 492-516
Song Xi Chen and Hengjian Cui
This paper considers the second-order properties of empirical likelihood (EL) for a parameter defined by moment restrictions, which is the inferential
framework of the generalized method of moments. It is shown that the EL defined for this general framework still admits the delicate second-order property of
Bartlett correction. This represents a substantial extension of all the established cases of Bartlett correction for the EL. An empirical Bartlett correction is
proposed, which is shown to work effectively in improving the coverage accuracy of confidence regions for the parameter.
9. Contemporaneous threshold autoregressive models: Estimation, testing and forecasting
Pages 517-547
Michael J. Dueker, Martin Sola and Fabio Spagnolo
This paper proposes a contemporaneous smooth transition threshold autoregressive model (C-STAR) as a modification of the smooth transition threshold
autoregressive model surveyed in Teräsvirta [1998. Modelling economic relationships with smooth transition regressions. In: Ullah, A., Giles, D.E.A. (Eds.),
Handbook of Applied Economic Statistics. Marcel Dekker, New York, pp. 507–552.], in which the regime weights depend on the ex ante probability that a latent
regime-specific variable will exceed a threshold value. We argue that the contemporaneous model is well suited to rational expectations applications (and
pricing exercises), in that it does not require the initial regimes to be predetermined. We investigate the properties of the model and evaluate its finite-sample
maximum likelihood performance. We also propose a method to determine the number of regimes based on a modified Hansen [1992. The likelihood ratio test
under nonstandard conditions: testing the Markov switching model of GNP. Journal of Applied Econometrics 7, S61–S82.] procedure. Furthermore, we
construct multiple-step ahead forecasts and evaluate the forecasting performance of the model. Finally, an empirical application of the short term interest rate
yield is presented and discussed.
10. Efficient tests of the seasonal unit root hypothesis
Pages 548-573
Paulo M.M. Rodrigues and A.M. Robert Taylor
In this paper we derive, under the assumption of Gaussian errors with known error covariance matrix, asymptotic local power bounds for unit root tests at the
zero and seasonal frequencies for both known and unknown deterministic scenarios and for an arbitrary seasonal aspect. We demonstrate that the optimal
test of a unit root at a given spectral frequency behaves asymptotically independently of whether unit roots exist at other frequencies or not. Optimal tests for
unit roots at multiple frequencies are also developed. We also detail modified versions of the optimal tests which attain the asymptotic Gaussian power bounds
under much weaker conditions. We further propose near-efficient regression-based seasonal unit root tests using local GLS de-trending which, in the case of
single frequency unit root tests, are shown to have limiting null distributions and asymptotic local power functions of a known form. Monte Carlo simulations
indicate that these tests perform well in finite samples.
11. Determining the cointegrating rank in nonstationary fractional systems by the exact local Whittle approach
Pages 574-596
Morten Ørregaard Nielsen and Katsumi Shimotsu
We propose to extend the cointegration rank determination procedure of Robinson and Yajima [2002. Determination of cointegrating rank in fractional
systems. Journal of Econometrics 106, 217–242] to accommodate both (asymptotically) stationary and nonstationary fractionally integrated processes as the
common stochastic trends and cointegrating errors by applying the exact local Whittle analysis of Shimotsu and Phillips [2005. Exact local Whittle estimation of
fractional integration. Annals of Statistics 33, 1890–1933]. The proposed method estimates the cointegrating rank by examining the rank of the spectral
density matrix of the dth differenced process around the origin, where the fractional integration order, d, is estimated by the exact local Whittle estimator.
Similar to other semiparametric methods, the approach advocated here only requires information about the behavior of the spectral density matrix around the
origin, but it relies on a choice of (multiple) bandwidth(s) and threshold parameters. It does not require estimating the cointegrating vector(s) and is easier to
implement than regression-based approaches, but it only provides a consistent estimate of the cointegration rank, and formal tests of the cointegration rank
or levels of confidence are not available except for the special case of no cointegration. We apply the proposed methodology to the analysis of exchange rate
dynamics among a system of seven exchange rates. Contrary to both fractional and integer-based parametric approaches, which indicate at most one
cointegrating relation, our results suggest three or possibly four cointegrating relations in the data.
12. Asymptotic properties of a robust variance matrix estimator for panel data when T is large
Pages 597-620
Christian B. Hansen
I consider the asymptotic properties of a commonly advocated covariance matrix estimator for panel data. Under asymptotics where the cross-section
dimension, n, grows large with the time dimension, T, fixed, the estimator is consistent while allowing essentially arbitrary correlation within each individual.
However, many panel data sets have a non-negligible time dimension. I extend the usual analysis to cases where n and T go to infinity jointly and where T→∞
with n fixed. I provide conditions under which t and F statistics based on the covariance matrix estimator provide valid inference and illustrate the properties of
the estimator in a simulation study.
13. Online forecast combinations of distributions: Worst case bounds
Pages 621-651
Alessio Sancetta
This paper considers forecasts with distribution functions that may vary through time. The forecast is achieved by time varying combinations of individual
forecasts. We derive theoretical worst case bounds for general algorithms based on multiplicative updates of the combination weights. The bounds are useful
for studying properties of forecast combinations when data are non-stationary and there is no unique best model.
14. Nonparametric tests for conditional symmetry in dynamic models
Pages 652-682
Miguel A. Delgado and J. Carlos Escanciano
This article proposes omnibus tests for conditional symmetry around a parametric function in a dynamic context. Conditional moments may not exist or may
depend on the explanatory variables. Test statistics are suitable functionals of the empirical process of residuals and explanatory variables, whose limiting
distribution under the null is nonpivotal. The tests are implemented with the assistance of a bootstrap method, which is justified assuming very mild regularity
conditions on the specification of the center of symmetry and the underlying serial dependence structure. Finite sample properties are examined by means of
a Monte Carlo experiment.
15. Masking identification of discrete choice models under simulation methods
Pages 683-703
Lesley Chiou and Joan L. Walker
We present examples based on actual and synthetic datasets to illustrate how simulation methods can mask identification problems in the estimation of
discrete choice models such as mixed logit. Simulation methods approximate an integral (without a closed form) by taking draws from the underlying
distribution of the random variable of integration. Our examples reveal how a low number of draws can generate estimates that appear identified, but in fact,
are either not theoretically identified by the model or not empirically identified by the data. For the particular case of maximum simulated likelihood estimation,
we investigate the underlying source of the problem by focusing on the shape of the simulated log-likelihood function under different conditions.
16. A smoothed least squares estimator for threshold regression models
Pages 704-735
Myung Hwan Seo and Oliver Linton
We propose a smoothed least squares estimator of the parameters of a threshold regression model. Our model generalizes that considered in Hansen [2000.
Sample splitting and threshold estimation. Econometrica 68, 575–603] to allow the thresholding to depend on a linear index of observed regressors, thus
allowing discrete variables to enter. We also do not assume that the threshold effect is vanishingly small. Our estimator is shown to be consistent and
asymptotically normal thus facilitating standard inference techniques based on estimated standard errors or standard bootstrap for the slope and threshold
parameters.
17. Can the random walk model be beaten in out-of-sample density forecasts? Evidence from intraday foreign exchange rates
Pages 736-776
Yongmiao Hong, Haitao Li and Feng Zhao
It has been documented that random walk outperforms most economic structural and time series models in out-of-sample forecasts of the conditional mean
dynamics of exchange rates. In this paper, we study whether random walk has similar dominance in out-of-sample forecasts of the conditional probability
density of exchange rates given that the probability density forecasts are often needed in many applications in economics and finance. We first develop a
nonparametric portmanteau test for optimal density forecasts of univariate time series models in an out-of-sample setting and provide simulation evidence on
its finite sample performance. Then we conduct a comprehensive empirical analysis on the out-of-sample performances of a wide variety of nonlinear time
series models in forecasting the intraday probability densities of two major exchange rates—Euro/Dollar and Yen/Dollar. It is found that some sophisticated
time series models that capture time-varying higher order conditional moments, such as Markov regime-switching models, have better density forecasts for
exchange rates than random walk or modified random walk with GARCH and Student-t innovations. This finding dramatically differs from that on mean
forecasts and suggests that sophisticated time series models could be useful in out-of-sample applications involving the probability density.
18. Endogenous selection or treatment model estimation
Pages 777-806
Arthur Lewbel
In a sample selection or treatment effects model, common unobservables may affect both the outcome and the probability of selection in unknown ways. This
paper shows that the distribution function of potential outcomes, conditional on covariates, can be identified given an observed variable V that affects the
treatment or selection probability in certain ways and is conditionally independent of the error terms in a model of potential outcomes. Selection model
estimators based on this identification are provided, which take the form of simple weighted averages, GMM, or two stage least squares. These estimators
permit endogenous and mismeasured regressors. Empirical applications are provided to estimation of a firm investment model and a schooling effects on
wages model.
19. A consistent characteristic function-based test for conditional independence
Pages 807-834
Liangjun Su and Halbert White
Y is conditionally independent of Z given X if Pr{f(yX,Z)=f(yX)}=1 for all y on its support, where f(··) denotes the conditional density of Y given (X,Z) or X. This
paper proposes a nonparametric test of conditional independence based on the notion that two conditional distributions are equal if and only if the
corresponding conditional characteristic functions are equal. We extend the test of Su and White (2005. A Hellinger-metric nonparametric test for conditional
independence. Discussion Paper, Department of Economics, UCSD) in two directions: (1) our test is less sensitive to the choice of bandwidth sequences; (2)
our test has power against deviations on the full support of the density of (X,Y,Z). We establish asymptotic normality for our test statistic under weak data
dependence conditions. Simulation results suggest that the test is well behaved in finite samples. Applications to stock market data indicate that our test can
reveal some interesting nonlinear dependence that a traditional linear Granger causality test fails to detect.
20. A goodness-of-fit test for ARCH(∞) models
Pages 835-875
Javier Hidalgo and Paolo Zaffaroni
A goodness-of-fit test in the class of conditional heteroscedastic time series models is examined. Due to the nonstandard limiting distribution of the test, we
propose to bootstrap the test, showing its asymptotic validity. Moreover, we illustrate the finite sample performance of the test by a small Monte Carlo study.
21. Modelling security market events in continuous time: Intensity based, multivariate point process models
Pages 876-912
Clive G. Bowsher
A continuous time econometric modelling framework for multivariate financial market event (or ‘transactions’) data is developed in which the model is specified
via the vector conditional intensity. Generalised Hawkes models are introduced that incorporate inhibitory events and dependence between trading days.
Novel omnibus specification tests based on a multivariate random time change theorem are proposed. A bivariate point process model of the timing of trades
and mid-quote changes is then presented for a New York Stock Exchange stock and related to the market microstructure literature. The two-way interaction of
trades and quote changes in continuous time is found to be important empirically.
22. Asymptotics for duration-driven long range dependent processes
Pages 913-949
Meng-Chen Hsieh, Clifford M. Hurvich and Philippe Soulier
We consider processes with second order long range dependence resulting from heavy tailed durations. We refer to this phenomenon as duration-driven
long range dependence (DDLRD), as opposed to the more widely studied linear long range dependence based on fractional differencing of an i.i.d. process.
We consider in detail two specific processes having DDLRD, originally presented in Taqqu and Levy [1986. Using renewal processes to generate long-range
dependence and high variability. Dependence in Probability and Statistics. Birkhauser, Boston, pp. 73–89], and Parke [1999. What is fractional integration?
Review of Economics and Statistics 81, 632–638]. For these processes, we obtain the limiting distribution of suitably standardized discrete Fourier transforms
(DFTs) and sample autocovariances. At low frequencies, the standardized DFTs converge to a stable law, as do the standardized sample autocovariances at
fixed lags. Finite collections of standardized sample autocovariances at a fixed set of lags converge to a degenerate distribution. The standardized DFTs at
high frequencies converge to a Gaussian law. Our asymptotic results are strikingly similar for the two DDLRD processes studied. We calibrate our asymptotic
results with a simulation study which also investigates the properties of the semiparametric log periodogram regression estimator of the memory parameter.
23. An adaptive empirical likelihood test for parametric time series regression models
Pages 950-972
Song Xi Chen and Jiti Gao
We propose an adaptive empirical likelihood (EL) test for a parametric regression model against a class of alternatives for weakly dependent time series
observations. The test is formulated by maximizing a standardized version of the EL statistic over a set of smoothing bandwidths. It is demonstrated that the
proposed test is able to distinguish the null hypothesis from a series of local alternatives at an optimal rate.
24. A goodness-of-fit test for ARCH(∞) models
Pages 973-1013
Javier Hidalgo and Paolo Zaffaroni
A goodness-of-fit test in the class of conditional heteroscedastic time series models is examined. Due to the nonstandard limiting distribution of the test, we
propose to bootstrap the test, showing its asymptotic validity. Moreover, we illustrate the finite sample performance of the test by a small Monte Carlo study.
25. Discrete time duration models with group-level heterogeneity
Pages 1014-1043
Anders Frederiksen, Bo E. Honoré and Luojia Hu
Dynamic discrete choice panel data models have received a great deal of attention. In those models, the dynamics is usually handled by including the lagged
outcome as an explanatory variable. In this paper we consider an alternative model in which the dynamics is handled by using the duration in the current state
as a covariate. We propose estimators that allow for group-specific effect in parametric and semiparametric versions of the model. The proposed method is
illustrated by an empirical analysis of job durations allowing for firm-level effects.
26. Income distribution and inequality measurement: The problem of extreme values
Pages 1044-1072
Frank A. Cowell and Emmanuel Flachaire
We examine the statistical performance of inequality indices in the presence of extreme values in the data and show that these indices are very sensitive to
the properties of the income distribution. Estimation and inference can be dramatically affected, especially when the tail of the income distribution is heavy,
even when standard bootstrap methods are employed. However, use of appropriate semiparametric methods for modelling the upper tail can greatly improve
the performance of even those inequality indices that are normally considered particularly sensitive to extreme values.
27. A zero-inflated ordered probit model, with an application to modelling tobacco consumption
Pages 1073-1099
Mark N. Harris and Xueyan Zhao
Data for discrete ordered dependent variables are often characterised by “excessive” zero observations which may relate to two distinct data generating
processes. Traditional ordered probit models have limited capacity in explaining this preponderance of zero observations. We propose a zero-inflated ordered
probit model using a double-hurdle combination of a split probit model and an ordered probit model. Monte Carlo results show favourable performance in finite
samples. The model is applied to a consumer choice problem of tobacco consumption indicating that policy recommendations could be misleading if the
splitting process is ignored.
28. Estimating a generalized correlation coefficient for a generalized bivariate probit model
Pages 1100-1114
Songnian Chen and Yahong Zhou
In this paper we consider semiparametric estimation of a generalized correlation coefficient in a generalized bivariate probit model. The generalized
correlation coefficient provides a simple summary statistic measuring the relationship between the two binary decision processes in a general framework. Our
semiparametric estimation procedure consists of two steps, combining semiparametric estimators for univariate binary choice models with the method of
maximum likelihood for the bivariate probit model with nonparametrically generated regressors. The estimator is shown to be consistent and asymptotically
normal. The estimator performs well in our simulation study.
29. Nonstationary discrete choice: A corrigendum and addendum
Pages 1115-1130
Peter C.B. Phillips, Sainan Jin and Ling Hu
We correct the limit theory presented in an earlier paper by Hu and Phillips [2004a. Nonstationary discrete choice. Journal of Econometrics 120, 103–138] for
nonstationary time series discrete choice models with multiple choices and thresholds. The new limit theory shows that, in contrast to the binary choice model
with nonstationary regressors and a zero threshold where there are dual rates of convergence (n1/4 and n3/4), all parameters including the thresholds
converge at the rate n3/4. The presence of nonzero thresholds therefore materially affects rates of convergence. Dual rates of convergence reappear when
stationary variables are present in the system. Some simulation evidence is provided, showing how the magnitude of the thresholds affects finite sample
performance. A new finding is that predicted probabilities and marginal effect estimates have finite sample distributions that manifest a pile-up, or increasing
density, towards the limits of the domain of definition.
30. Endogeneity in quantile regression models: A control function approach
Pages 1131-1158
Sokbae Lee
This paper considers a linear triangular simultaneous equations model with conditional quantile restrictions. The paper adjusts for endogeneity by adopting a
control function approach and presents a simple two-step estimator that exploits the partially linear structure of the model. The first step consists of estimation
of the residuals of the reduced-form equation for the endogenous explanatory variable. The second step is series estimation of the primary equation with the
reduced-form residual included nonparametrically as an additional explanatory variable. This paper imposes no functional form restrictions on the stochastic
relationship between the reduced-form residual and the disturbance term in the primary equation conditional on observable explanatory variables. The paper
presents regularity conditions for consistency and asymptotic normality of the two-step estimator. In addition, the paper provides some discussions on related
estimation methods in the literature.
31. Time and causality: A Monte Carlo assessment of the timing-of-events approach
Pages 1159-1195
Simen Gaure, Knut Røed and Tao Zhang
We present new Monte Carlo evidence regarding the feasibility of separating causality from selection within non-experimental duration data, by means of the
non-parametric maximum likelihood estimator (NPMLE). Key findings are: (i) the NPMLE is extremely reliable, and it accurately separates the causal effects of
treatment and duration dependence from sorting effects, almost regardless of the true unobserved heterogeneity distribution; (ii) the NPMLE is normally
distributed, and standard errors can be computed directly from the optimally selected model; and (iii) unjustified restrictions on the heterogeneity distribution,
e.g., in terms of a pre-specified number of support points, may cause substantial bias.
32. Confidence sets for the date of a single break in linear time series regressions
Pages 1196-1218
Graham Elliott and Ulrich K. Müller
This paper considers the problem of constructing confidence sets for the date of a single break in a linear time series regression. We establish analytically
and by small sample simulation that the current standard method in econometrics for constructing such confidence intervals has a coverage rate far below
nominal levels when breaks are of moderate magnitude. Given that breaks of moderate magnitude are a theoretically and empirically relevant phenomenon,
we proceed to develop an appropriate alternative. We suggest constructing confidence sets by inverting a sequence of tests. Each of the tests maintains a
specific break date under the null hypothesis, and rejects when a break occurs elsewhere. By inverting a certain variant of a locally best invariant test, we
ensure that the asymptotic critical value does not depend on the maintained break date. A valid confidence set can hence be obtained by assessing which of
the sequence of test statistics exceeds a single number.
33. Finite sample multivariate structural change tests with application to energy demand models
Pages 1219-1244
Jean-Thomas Bernard, Nadhem Idoudi, Lynda Khalaf and Clément Yélou
This paper considers finite sample motivated structural change tests in the multivariate linear regression model with application to energy demand models, in
which case commonly used structural change tests remain asymptotic. As in Dufour and Kiviet [1996. Exact tests for structural change in first-order dynamic
models. Journal of Econometrics 70, 39–68], we account for intervening nuisance parameters through a two-stage maximized Monte Carlo test procedure. Our
contributions can be classified into five categories: (i) we extend tests for which a finite-sample theory has been supplied for Gaussian distributions to the non
-Gaussian context; (ii) we show that Bai et al. [1998. Testing and dating common breaks in multi-variate time series. The Review of Economic Studies 65 (3),
395–432] test severely over-rejects and propose exact variants of this test; (iii) we consider predictive break test approaches which generalize tests in Dufour
[1980. Dummy variables and predictive tests for structural change. Economics Letters 6, 241–247] and Dufour and Kiviet [1996. Exact tests for structural
change in first-order dynamic models. Journal of Econometrics 70, 39–68]; (iv) we propose exact (non-Bonferonni based) extensions of the multivariate
outliers test from Wilks [1963. Multivariate statistical outliers. Sankhya Series A 25, 407–426] to models with covariates; (v) we apply these tests to the energy
demand system analyzed by Arsenault et al. [1995. A total energy demand model of Québec: forecasting properties. Energy Economics 17 (2), 163–171]. For
two out of the six industrial sectors analyzed over the 1962–2000 period, break and further goodness-of-fit and diagnostic tests allow to identify (and correct)
specification problems arising from historical regulatory changes or (possibly random) industry-specific effects. The procedures we propose have potential
useful applications in statistics, econometrics and finance (e.g. event studies).
34. Closed-form likelihood approximation and estimation of jump-diffusions with an application to the realignment risk of the Chinese Yuan
Pages 1245-1280
Jialin Yu
This paper provides closed-form likelihood approximations for multivariate jump-diffusion processes widely used in finance. For a fixed order of
approximation, the maximum-likelihood estimator (MLE) computed from this approximate likelihood achieves the asymptotic efficiency of the true yet
uncomputable MLE as the sampling interval shrinks. This method is used to uncover the realignment probability of the Chinese Yuan. Since February 2002,
the market-implied realignment intensity has increased fivefold. The term structure of the forward realignment rate, which completely characterizes future
realignment probabilities, is hump-shaped and peaks at mid-2004. The realignment probability responds quickly to economic news releases and government
interventions.
35. Inverse probability weighted estimation for general missing data problems
Pages 1281-1301
Jeffrey M. Wooldridge
I study inverse probability weighted M-estimation under a general missing data scheme. Examples include M-estimation with missing data due to a censored
survival time, propensity score estimation of the average treatment effect in the linear exponential family, and variable probability sampling with observed
retention frequencies. I extend an important result known to hold in special cases: estimating the selection probabilities is generally more efficient than if the
known selection probabilities could be used in estimation. For the treatment effect case, the setup allows a general characterization of a “double robustness”
result due to Scharfstein et al. [1999. Rejoinder. Journal of the American Statistical Association 94, 1135–1146].
36. A simple, robust and powerful test of the trend hypothesis
Pages 1302-1330
David I. Harvey, Stephen J. Leybourne and A.M. Robert Taylor
In this paper we develop a simple test procedure for a linear trend which does not require knowledge of the form of serial correlation in the data, is robust to
strong serial correlation, and has a standard normal limiting null distribution under either I(0) or I(1) shocks. In contrast to other available robust linear trend
tests, our proposed test achieves the Gaussian asymptotic local power envelope in both the I(0) and I(1) cases. For near-I(1) errors our proposed procedure
is conservative and a modification for this situation is suggested. An estimator of the trend parameter, together with an associated confidence interval, which
is asymptotically efficient, again regardless of whether the shocks are I(0) or I(1), is also provided.
37. A theory of robust long-run variance estimation
Pages 1331-1352
Ulrich K. Müller
Long-run variance estimation can typically be viewed as the problem of estimating the scale of a limiting continuous time Gaussian process on the unit
interval. A natural benchmark model is given by a sample that consists of equally spaced observations of this limiting process. The paper analyzes the
asymptotic robustness of long-run variance estimators to contaminations of this benchmark model. It is shown that any equivariant long-run variance estimator
that is consistent in the benchmark model is highly fragile: there always exists a sequence of contaminated models with the same limiting behavior as the
benchmark model for which the estimator converges in probability to an arbitrary positive value. A class of robust inconsistent long-run variance estimators is
derived that optimally trades off asymptotic variance in the benchmark model against the largest asymptotic bias in a specific set of contaminated models.
38. Nonstationarity-extended local Whittle estimation
Pages 1353-1384
Karim M. Abadir, Walter Distaso and Liudas Giraitis
This paper extends the classical local Whittle estimation procedure of the memory parameter to fractionally integrated I(d) processes for , covering stationary
and nonstationary regions. We introduce the concepts of fully extended discrete Fourier transform and periodogram. We investigate the properties of our fully
extended local Whittle (FELW) estimator, which is applicable not only for the traditional cases but also for nonlinear and non-Gaussian processes. For a wide
class of processes, we show that the estimator is consistent and we derive its asymptotic expansion. In addition, when the generating process is linear, we
show that the estimator satisfies the same normal CLT as in the stationary case. The performance of the estimator is illustrated by a simulation.
39. Efficient high-dimensional importance sampling
Pages 1385-1411
Jean-Francois Richard and Wei Zhang
The paper describes a simple, generic and yet highly accurate efficient importance sampling (EIS) Monte Carlo (MC) procedure for the evaluation of high-
dimensional numerical integrals. EIS is based upon a sequence of auxiliary weighted regressions which actually are linear under appropriate conditions. It can
be used to evaluate likelihood functions and byproducts thereof, such as ML estimators, for models which depend upon unobservable variables. A dynamic
stochastic volatility model and a logit panel data model with unobserved heterogeneity (random effects) in both dimensions are used to provide illustrations of
EIS high numerical accuracy, even under small number of MC draws. MC simulations are used to characterize the finite sample numerical and statistical
properties of EIS-based ML estimators.
40. Corrigendum to “The pseudo-true score encompassing test for non-nested hypotheses”: [Journal of Econometrics 106, 271–295]
Pages 1412-1417
Yi-Ting Chen and Chung-Ming Kuan
41. Corrigendum to: “The large sample behaviour of the generalized method of moments estimator in misspecified models”: [Journal of Econometrics 114
(2003) 361–394]
Page 1418
Alastair R. Hall and Atsushi Inoue
42. Erratum to “Generalizing the standard product rule of probability theory and Bayes's Theorem”: [J. Econometrics 138 (1) (2007) 14–23]
Page 1419
Arnold Zellner
43. Error in contents listing of Special issue
Page 1420
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