Calibration Weighting Methods for Complex Surveys

This paper presents a careful investigation of the three popular calibration weighting methods: (i) generalised regression; (ii) generalised exponential tilting and (iii) generalised pseudo empirical likelihood, with a major focus on computational aspects of the methods and some empirical evidences on calibrated weights. We also propose a simple weight trimming method for range‐restricted calibration. The finite sample behaviour of the weights obtained by the three calibration weighting methods and the effectiveness of the proposed weight trimming method are examined through limited simulation studies.     

Estimation and comparison of treasury auction formats when bidders are asymmetric

The structural parameters of a share‐auction model accounting for asymmetries across bidders, as well as supply uncertainty, are estimated with a sample of French Treasury auctions. We find evidence of both informational and risk aversion asymmetries across bidders. A counter‐factual analysis also suggests that, in the context of the French Treasury auctions, a shift from the discriminatory to the uniform‐price format would simultaneously benefit the French Treasury and the auctions' participants. Copyright © 2006 John Wiley & Sons, Ltd.     

Does Forecast Combination Improve Norges Bank Inflation Forecasts?*

We develop a system that provides model‐based forecasts for inflation in Norway. We recursively evaluate quasi out‐of‐sample forecasts from a large suite of models from 1999 to 2009. The performance of the models are then used to derive quasi real time weights that are used to combine the forecasts. Our results indicate that a combination forecast improves upon the point forecasts from individual models. Furthermore, a combination forecast outperforms Norges Bank's own point forecast for inflation. The beneficial results are obtained using a trimmed weighted average. Some degree of trimming is required for the combination forecasts to outperform the judgmental forecasts from the policymaker.     

Smooth estimation of a monotone hazard and a monotone density under random censoring

We consider kernel smoothed Grenander‐type estimators for a monotone hazard rate and a monotone density in the presence of randomly right censored data. We show that they converge at rate n^2/5 and that the limit distribution at a fixed point is Gaussian with explicitly given mean and variance. It is well known that standard kernel smoothing leads to inconsistency problems at the boundary points. It turns out that, also by using a boundary correction, we can only establish uniform consistency on intervals that stay away from the end point of the support (although we can go arbitrarily close to the right boundary).     

Inference on endogenously censored regression models using conditional moment inequalities

Under a quantile restriction, randomly censored regression models can be written in terms of conditional moment inequalities. We study the identified features of these moment inequalities with respect to the regression parameters where we allow for covariate dependent censoring, endogenous censoring and endogenous regressors. These inequalities restrict the parameters to a set. We show regular point identification can be achieved under a set of interpretable sufficient conditions. We then provide a simple way to convert conditional moment inequalities into unconditional ones while preserving the informational content. Our method obviates the need for nonparametric estimation, which would require the selection of smoothing parameters and trimming procedures. Without the point identification conditions, our objective function can be used to do inference on the partially identified parameter. Maintaining the point identification conditions, we propose a quantile minimum distance estimator which converges at the parametric rate to the parameter vector of interest, and has an asymptotically normal distribution. A small scale simulation study and an application using drug relapse data demonstrate satisfactory finite sample performance.     

Variable selection via composite quantile regression with dependent errors

We propose composite quantile regression for dependent data, in which the errors are from short‐range dependent and strictly stationary linear processes. Under some regularity conditions, we show that composite quantile estimator enjoys root‐n consistency and asymptotic normality. We investigate the asymptotic relative efficiency of composite quantile estimator to both single‐level quantile regression and least‐squares regression. When the errors have finite variance, the relative efficiency of composite quantile estimator with respect to the least‐squares estimator has a universal lower bound. Under some regularity conditions, the adaptive least absolute shrinkage and selection operator penalty leads to consistent variable selection, and the asymptotic distribution of the non‐zero coefficient is the same as that of the counterparts obtained when the true model is known. We conduct a simulation study and a real data analysis to evaluate the performance of the proposed approach.     

Too Good to be True: Board Structural Independence as a Moderator of CEO Pay‐for‐Firm‐Performance

Whether voluntary or mandatory in nature, most recent corporate governance codes of best practice assume that board structural independence, and the application by boards of outcome‐based incentive plans, are important boundary conditions for the enforcement of Chief Executive Officer (CEO) pay‐for‐firm‐performance; that is, for optimal contracting between owners and executive agents. We test this logic on a large Australian sample using a system Generalized Method of Moments (GMM) approach to dynamic panel data estimation. We find that Australian boards exhibiting best practice structural arrangements – those chaired by non‐executives and dominated by non‐executive directors at the full board and compensation committee levels – are no more adept at enforcing CEO pay‐for‐firm‐performance than are executive‐dominated boards. These findings suggest that policy makers' faith in incentive plans and the moderating influence of structural independence per se may be misplaced. Our findings also hold significant implications for corporate governance theory. Specifically, the findings lend further support to a contingency‐based understanding of board composition, reward choice and monitoring; an approach integrating the insights afforded by behavioural approaches to Agency Theory and by social‐cognitive and institutional understandings of director outlook, decision‐making and behaviour.     

An empirical model of the multi‐unit,sequential, clock auction

We construct a model of participation and bidding at multi‐unit, sequential, clock auctions when bidders have multi‐unit demand. We describe conditions sufficient to characterize a symmetric, perfect‐Bayesian equilibrium and then demonstrate that this equilibrium induces an efficient allocation. We propose an algorithm, based on the generalized Vickrey auction, to calculate the expected winning bid for each unit sold. This algorithm allows us to construct a simulation‐based estimator of the parameters for both the participation process and the distribution of latent valuations. We apply our method to data from 37 multi‐lot, sequential, English auctions of export permits for timber held in Russia. Copyright © 2006 John Wiley & Sons, Ltd.     

Anticipation of Auction Fever: Entry Decision,Reserve Price and the Choice of Auction Design

This paper analyzes the entry decision of rational bidders who expect to experience auction fever in English auctions. It shows that nonparticipation decision reduces seller's expected profit and this effect may outweigh the positive effect of the auction fever. We analyze the choice between English and second‐price sealed‐bid auctions and the optimal reserve price in English auctions. We show that it might be optimal for the seller to set a reserve price below her own valuation of the object. Finally, we show that the order in which bidders place their bids matters and the first bidder always has an advantage. Copyright © 2011 John Wiley & Sons, Ltd.     

Innovation Portfolio Management as a Subset of Dynamic Capabilities: Measurement and Impact on Innovative Performance

Innovation portfolio management has been touted as a new dynamic capability following the evolution of team‐ and project‐based organizational forms. In this article, we conceptualize innovative dynamic capabilities as a multidimensional construct that comprises distinct but related aspects in managing innovation. We test our model, which links this capability to innovative performance by using survey data from a sample of 923 firms. We find empirical support for our conceptualization and its impact on firm innovative performance.     

Asymmetry in first‐price auctions with affiliated private values

Collusion and heterogeneity across firms may introduce asymmetry in bidding games. A major difficulty in asymmetric auctions is that the Bayesian Nash equilibrium strategies are solutions of an intractable system of differential equations. We propose a simple method for estimating asymmetric first‐price auctions with affiliated private values. Considering two types of bidders, we show that these differential equations can be rewritten using the observed bid distribution. We establish the identification of the model, characterize its theoretical restrictions, and propose a two‐step non‐parametric estimation procedure for estimating the private value distributions. An empirical analysis of joint bidding in OCS auctions is provided. Copyright © 2003 John Wiley & Sons, Ltd.     

On Maximum Likelihood Estimation of Dynamic Panel Data Models

We analyse the finite sample properties of maximum likelihood estimators for dynamic panel data models. In particular, we consider transformed maximum likelihood (TML) and random effects maximum likelihood (RML) estimation. We show that TML and RML estimators are solutions to a cubic first‐order condition in the autoregressive parameter. Furthermore, in finite samples both likelihood estimators might lead to a negative estimate of the variance of the individual‐specific effects. We consider different approaches taking into account the non‐negativity restriction for the variance. We show that these approaches may lead to a solution different from the unique global unconstrained maximum. In an extensive Monte Carlo study we find that this issue is non‐negligible for small values of T and that different approaches might lead to different finite sample properties. Furthermore, we find that the Likelihood Ratio statistic provides size control in small samples, albeit with low power due to the flatness of the log‐likelihood function. We illustrate these issues modelling US state level unemployment dynamics.     

Finite sample evidence of IV estimators under weak instruments

We present finite sample evidence on different IV estimators available for linear models under weak instruments; explore the application of the bootstrap as a bias reduction technique to attenuate their finite sample bias; and employ three empirical applications to illustrate and provide insights into the relative performance of the estimators in practice. Our evidence indicates that the random‐effects quasi‐maximum likelihood estimator outperforms alternative estimators in terms of median point estimates and coverage rates, followed by the bootstrap bias‐corrected version of LIML and LIML. However, our results also confirm the difficulty of obtaining reliable point estimates in models with weak identification and moderate‐size samples. Copyright © 2007 John Wiley & Sons, Ltd.     

Capacity constraints and the winner's curse in multi-unit common value auctions

We explore the role of capacity constraints in establishing efficient pricing in multi-unit common value auctions in a setting relevant to auction-based equity IPOs. The method of inquiry is experimental economics. We find that sufficiently large capacity constraints mitigate the overbidding that plagues single-unit auctions and is one of the most robust laboratory findings. We also uncover a puzzling propensity for most bidders to place a portion of their bids at prices above their signals. This disequilibrium behavior persists with experience and in cases with substantial losses in previous auctions. Our results suggest caution is warranted in promoting auction based IPOs that allow unrestricted access by the non-professional investing public.     

Spline Regression in the Presence of Categorical Predictors

We consider the problem of estimating a relationship nonparametrically using regression splines when there exist both continuous and categorical predictors. We combine the global properties of regression splines with the local properties of categorical kernel functions to handle the presence of categorical predictors rather than resorting to sample splitting as is typically done to accommodate their presence. The resulting estimator possesses substantially better finite‐sample performance than either its frequency‐based peer or cross‐validated local linear kernel regression or even additive regression splines (when additivity does not hold). Theoretical underpinnings are provided and Monte Carlo simulations are undertaken to assess finite‐sample behavior; and two illustrative applications are provided. An implementation in R is available; see the R package ‘crs’ for details. Copyright © 2014 John Wiley & Sons, Ltd.     

Identification of first-price auctions with non-separable unobserved heterogeneity

We propose a novel methodology for identification of first-price auctions, when bidders’ private valuations are independent conditional on one-dimensional unobserved heterogeneity. We extend the existing literature ( and ) by allowing the unobserved heterogeneity to be non-separable from bidders’ valuations. Our central identifying assumption is that the distribution of bidder values is increasing in the state. When the state-space is finite, such monotonicity implies the full-rank condition needed for identification. Further, we extend our approach to the conditionally independent private values model of Li et al. (2000), as well as to unobserved heterogeneity settings in which the implicit reserve price or the cost of bidding varies across auctions.     

Empirical implementation of nonparametric first-price auction models

Nonparametric estimators provide a flexible means of uncovering salient features of auction data. Although these estimators are popular in the literature, many key features necessary for proper implementation have yet to be uncovered. Here we provide several suggestions for nonparametric estimation of first-price auction models. Specifically, we show how to impose monotonicity of the equilibrium bidding strategy; a key property of structural auction models not guaranteed in standard nonparametric estimation. We further develop methods for automatic bandwidth selection. Finally, we discuss how to impose monotonicity in auctions with differing numbers of bidders, reserve prices, and auction-specific characteristics. Finite sample performance is examined using simulated data as well as experimental auction data.     

Mixed signals among tests for cointegration

This paper illustrates that, under the null hypothesis of no cointegration, the correlation of p‐values from a single‐equation residual‐based test (i.e., ADF or ) with a system‐based test (trace or maximum eigenvalue) is very low even as the sample size gets large. With data‐generating processes under the null or ‘near’ it, the two types of tests can yield virtually any combination of p‐values regardless of sample size. As a practical matter, we also conduct tests for cointegration on 132 data sets from 34 studies appearing in this Journal and find substantial differences in p‐values for the same data set. Copyright © 2004 John Wiley & Sons, Ltd.     

Monetary Policy Indeterminacy and Identification Failures in the U.S.: Results from A Robust Test

We propose a novel identification‐robust test for the null hypothesis that an estimated New Keynesian model has a reduced form consistent with the unique stable solution against the alternative of sunspot‐driven multiple equilibria. Our strategy is designed to handle identification failures as well as the misspecification of the relevant propagation mechanisms. We invert a likelihood ratio test for the cross‐equation restrictions (CER) that the New Keynesian system places on its reduced‐form solution under determinacy. If the CER are not rejected, sunspot‐driven expectations can be ruled out from the model equilibrium and we accept the structural model. Otherwise, we move to a second‐step and invert an Anderson and Rubin‐type test for the orthogonality restrictions (OR) implied by the system of structural Euler equations. The hypothesis of indeterminacy and the structural model are accepted if the OR are not rejected. We investigate the finite‐sample performance of the suggested identification‐robust two‐step testing strategy by some Monte Carlo experiments and then apply it to a New Keynesian AD/AS model estimated with actual US data. In spite of some evidence of weak identification as for the ‘Great Moderation’ period, our results offer formal support to the hypothesis of a switch from indeterminacy to a scenario consistent with uniqueness occurring in the late 1970s. Our identification‐robust full‐information confidence set for the structural parameters computed on the ‘Great Moderation’ regime turns out to be more precise than the intervals previously reported in the literature through ‘limited‐information’ methods. Copyright © 2014 John Wiley & Sons, Ltd.     

Dynamic Panel Data Models With Irregular Spacing: With an Application to Early Childhood Development

With the increased availability of longitudinal data, dynamic panel data models have become commonplace. Moreover, the properties of various estimators of such models are well known. However, we show that these estimators break down when the data are irregularly spaced along the time dimension. Unfortunately, this is an increasingly frequent occurrence as many longitudinal surveys are collected at non‐uniform intervals and no solution is currently available when time‐varying covariates are included in the model. In this paper, we propose two new estimators for dynamic panel data models when data are irregularly spaced and compare their finite‐sample performance to the näive application of existing estimators. We illustrate the practical importance of this issue in an application concerning early childhood development. Copyright © 2016 John Wiley & Sons, Ltd.