General‐to‐Specific Model Selection Procedures for Structural Vector Autoregressions*

Structural vector autoregressive (SVAR) models have emerged as a dominant research strategy in empirical macroeconomics, but suffer from the large number of parameters employed and the resulting estimation uncertainty associated with their impulse responses. In this paper, we propose general‐to‐specific (Gets) model selection procedures to overcome these limitations. It is shown that single‐equation procedures are generally efficient for the reduction of recursive SVAR models. The small‐sample properties of the proposed reduction procedure (as implemented using PcGets) are evaluated in a realistic Monte Carlo experiment. The impulse responses generated by the selected SVAR are found to be more precise and accurate than those of the unrestricted VAR. The proposed reduction strategy is then applied to the US monetary system considered by Christiano, Eichenbaum and Evans (Review of Economics and Statistics, Vol. 78, pp. 16–34, 1996) . The results are consistent with the Monte Carlo and question the validity of the impulse responses generated by the full system.     

Half‐panel jackknife fixed‐effects estimation of linear panels with weakly exogenous regressors

This paper considers estimation and inference in linear panel regression models with lagged dependent variables and/or other weakly exogenous regressors when N (the cross‐section dimension) is large relative to T (the time series dimension). It allows for fixed and time effects (FE‐TE) and derives a general formula for the bias of the FE‐TE estimator which generalizes the well‐known Nickell bias formula derived for the pure autoregressive dynamic panel data models. It shows that in the presence of weakly exogenous regressors inference based on the FE‐TE estimator will result in size distortions unless N/T is sufficiently small. To deal with the bias and size distortion of the FE‐TE estimator the use of a half‐panel jackknife FE‐TE estimator is considered and its asymptotic distribution is derived. It is shown that the bias of the half‐panel jackknife FE‐TE estimator is of order T^?2, and for valid inference it is only required that N/T³→0, as N,T→∞ jointly. Extension to unbalanced panel data models is also provided. The theoretical results are illustrated with Monte Carlo evidence. It is shown that the FE‐TE estimator can suffer from large size distortions when N>T, with the half‐panel jackknife FE‐TE estimator showing little size distortions. The use of half‐panel jackknife FE‐TE estimator is illustrated with two empirical applications from the literature.     

Weak Identification of Forward‐looking Models in Monetary Economics*

Recently, single‐equation estimation by the generalized method of moments (GMM) has become popular in the monetary economics literature, for estimating forward‐looking models with rational expectations. We discuss a method for analysing the empirical identification of such models that exploits their dynamic structure and the assumption of rational expectations. This allows us to judge the reliability of the resulting GMM estimation and inference and reveals the potential sources of weak identification. With reference to the New Keynesian Phillips curve of Galí and Gertler [Journal of Monetary Economics (1999) Vol. 44, 195] and the forward‐looking Taylor rules of Clarida, Galí and Gertler [Quarterly Journal of Economics (2000) Vol. 115, 147], we demonstrate that the usual ‘weak instruments’ problem can arise naturally, when the predictable variation in inflation is small relative to unpredictable future shocks (news). Hence, we conclude that those models are less reliably estimated over periods when inflation has been under effective policy control.     

Relative Risk Aversion and Power‐Law Distribution of Macroeconomic Disasters

The coefficient of relative risk aversion is notoriously difficult to estimate. Recently, Barro and Jin (On the size distribution of macroeconomic disasters, Econometrica 2011; 79 (3): 434–455) have come up with a new estimation approach that fits a power‐law model to the tail of distribution of macroeconomic disasters. We show that their results can be successfully replicated using a more refined power‐law fitting methodology and a more comprehensive dataset. Copyright © 2014 John Wiley & Sons, Ltd.     

Efficient estimation of factor models with time and cross‐sectional dependence

This paper studies the efficient estimation of large‐dimensional factor models with both time and cross‐sectional dependence assuming (N,T) separability of the covariance matrix. The asymptotic distribution of the estimator of the factor and factor‐loading space under factor stationarity is derived and compared to that of the principal component (PC) estimator. The paper also considers the case when factors exhibit a unit root. We provide feasible estimators and show in a simulation study that they are more efficient than the PC estimator in finite samples. In application, the estimation procedure is employed to estimate the Lee–Carter model and life expectancy is forecast. The Dutch gender gap is explored and the relationship between life expectancy and the level of economic development is examined in a cross‐country comparison.     

Modelling Group Heterogeneity for Small Area Estimation Using M‐Quantiles

Small area estimation typically requires model‐based methods that depend on isolating the contribution to overall population heterogeneity associated with group (i.e. small area) membership. One way of doing this is via random effects models with latent group effects. Alternatively, one can use an M‐quantile ensemble model that assigns indices to sampled individuals characterising their contribution to overall sample heterogeneity. These indices are then aggregated to form group effects. The aim of this article is to contrast these two approaches to characterising group effects and to illustrate them in the context of small area estimation. In doing so, we consider a range of different data types, including continuous data, count data and binary response data.     

Bootstrap inference for impulse response functions in factor‐augmented vector autoregressions

In this study, we consider residual‐based bootstrap methods to construct the confidence interval for structural impulse response functions in factor‐augmented vector autoregressions. In particular, we compare the bootstrap with factor estimation (Procedure A) with the bootstrap without factor estimation (Procedure B). Both procedures are asymptotically valid under the condition , where N and T are the cross‐sectional dimension and the time dimension, respectively. However, Procedure A is also valid even when with 0 ≤ c < ∞ because it accounts for the effect of the factor estimation errors on the impulse response function estimator. Our simulation results suggest that Procedure A achieves more accurate coverage rates than those of Procedure B, especially when N is much smaller than T. In the monetary policy analysis of Bernanke et al. (Quarterly Journal of Economics, 2005, 120(1), 387–422), the proposed methods can produce statistically different results.     

Common correlated effect cross‐sectional dependence corrections for nonlinear conditional mean panel models

This paper provides an approach to estimation and inference for nonlinear conditional mean panel data models, in the presence of cross‐sectional dependence. We modify Pesaran's (Econometrica, 2006, 74(4), 967–1012) common correlated effects correction to filter out the interactive unobserved multifactor structure. The estimation can be carried out using nonlinear least squares, by augmenting the set of explanatory variables with cross‐sectional averages of both linear and nonlinear terms. We propose pooled and mean group estimators, derive their asymptotic distributions, and show the consistency and asymptotic normality of the coefficients of the model. The features of the proposed estimators are investigated through extensive Monte Carlo experiments. We also present two empirical exercises. The first explores the nonlinear relationship between banks' capital ratios and riskiness. The second estimates the nonlinear effect of national savings on national investment in OECD countries depending on countries' openness.     

R&D,Innovation and Knowledge Spillovers: A Reappraisal of Bottazzi and Peri (2007) in the Presence of Cross‐Sectional Dependence

Bottazzi and Peri (Economic Journal 2007; 117 : 486–511) show the existence of a cointegrating relationship between the domestic stock of knowledge, domestic R&D and the international knowledge stock for a panel of OECD countries and interpret it as evidence supporting the semi‐endogenous versus the endogenous growth theory. We replicate the baseline specification of their study and we show that main results are robust to the use of a different estimation strategy (Bai et al., Journal of Econometrics 2009; 149 : 82–99) that duly takes into account cross‐sectional correlation: interestingly, in this case we also find a larger role for knowledge spillovers. Copyright © 2014 John Wiley & Sons, Ltd.     

Sustainability‐oriented capabilities for eco‐innovation: Meeting the regulatory,technology, and market demands

Despite consensus in the literature that regulation, technology push, and market pull drive eco‐innovation (EI), evidence remains limited on the diverse firm capabilities needed to boost EI. Building on the natural‐resource‐based view of the firm and the EI literature, this paper posits that firms need to renew and realign their capabilities, and ultimately develop distinctive sustainability‐oriented capabilities, in order to meet the rapidly changing regulatory, technology, and market demands. Results of the analysis, based on a survey of U.K. firms, reveal that EIs are more likely to arise when firms (a) build capabilities on voluntary self‐regulation (i.e., executive driven environmental management system and corporate social responsibility) because such organizational capabilities allow them to address increasing regulatory pressures; (b) invest in environmental research and development (i.e., eco‐R&D)—instead of generic research and development—because it provides them with the relevant and specific technological capabilities to tackle technology shifts towards sustainability; and (c) develop capabilities in green market sensing as such capabilities allow them to address green consumption needs.     

k‐Nearest neighbors local linear regression for functional and missing data at random

We combine the k‐Nearest Neighbors (kNN) method to the local linear estimation (LLE) approach to construct a new estimator (LLE‐kNN) of the regression operator when the regressor is of functional type and the response variable is a scalar but observed with some missing at random (MAR) observations. The resulting estimator inherits many of the advantages of both approaches (kNN and LLE methods). This is confirmed by the established asymptotic results, in terms of the pointwise and uniform almost complete consistencies, and the precise convergence rates. In addition, a numerical study (i) on simulated data, then (ii) on a real dataset concerning the sugar quality using fluorescence data, were conducted. This practical study clearly shows the feasibility and the superiority of the LLE‐kNN estimator compared to competitive estimators.     

High‐Performance Work Systems in Professional Service Firms: Examining the Practices‐Resources‐Uses‐Performance Linkage

Professional service firms (PSFs) play an important role in the knowledge‐based economy. Their success is highly dependent on their people, the knowledge resources they possess, and how they use these resources. However, how to systematically manage human resources to attain high performance is not fully understood. This study addresses this issue by investigating the linkage mechanisms through which high‐performance work systems (HPWS) influence the performance of PSFs. We integrate resource‐based and dynamic capability theories in order to identify and investigate two intervening mechanisms that link HR practices to firm performance. The first mechanism is the intellectual capital resources comprising the human, social, and organizational capital that HPWS create. The second mechanism is the uses to which both HPWS and resources can be applied, operationalized as organizational ambidexterity, the simultaneous exploitation of existing knowledge and exploration of new knowledge. These mechanisms are hypothesized to link HPWS to firm performance in the form of a practices‐resources‐uses‐performance linkage model. Results from a longitudinal study of 93 accounting firms support this linkage model. © 2015 Wiley Periodicals, Inc.     

Financial Distress Prediction in an International Context: A Review and Empirical Analysis of Altman's Z‐Score Model

This paper assesses the classification performance of the Z‐Score model in predicting bankruptcy and other types of firm distress, with the goal of examining the model's usefulness for all parties, especially banks that operate internationally and need to assess the failure risk of firms. We analyze the performance of the Z‐Score model for firms from 31 European and three non‐European countries using different modifications of the original model. This study is the first to offer such a comprehensive international analysis. Except for the United States and China, the firms in the sample are primarily private, and include non‐financial companies across all industrial sectors. We use the original Z′′‐Score model developed by Altman, Corporate Financial Distress: A Complete Guide to Predicting, Avoiding, and Dealing with Bankruptcy (1983) for private and public manufacturing and non‐manufacturing firms. While there is some evidence that Z‐Score models of bankruptcy prediction have been outperformed by competing market‐based or hazard models, in other studies, Z‐Score models perform very well. Without a comprehensive international comparison, however, the results of competing models are difficult to generalize. This study offers evidence that the general Z‐Score model works reasonably well for most countries (the prediction accuracy is approximately 0.75) and classification accuracy can be improved further (above 0.90) by using country‐specific estimation that incorporates additional variables.     

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.     

Regression Models with Data‐based Indicator Variables*

Ordinary least squares estimation of an impulse‐indicator coefficient is inconsistent, but its variance can be consistently estimated. Although the ratio of the inconsistent estimator to its standard error has a t‐distribution, that test is inconsistent: one solution is to form an index of indicators. We provide Monte Carlo evidence that including a plethora of indicators need not distort model selection, permitting the use of many dummies in a general‐to‐specific framework. Although White's (1980) heteroskedasticity test is incorrectly sized in that context, we suggest an easy alteration. Finally, a possible modification to impulse ‘intercept corrections’ is considered.     

Correcting Standard Errors in Two‐stage Estimation Procedures with Generated Regressands*

Feenstra and Hanson [NBER Working Paper No. 6052 (1997)] propose a procedure to correct the standard errors in a two‐stage regression with generated dependent variables. Their method has subsequently been used in two‐stage mandated wage models [Feenstra and Hanson, Quarterly Journal of Economics (1999) Vol. 114, pp. 907–940; Haskel and Slaughter, The Economic Journal (2001) Vol. 111, pp. 163–187; Review of International Economics (2003) Vol. 11, pp. 630–650] and for the estimation of the sector bias of skill‐biased technological change [Haskel and Slaughter, European Economic Review (2002) Vol. 46, pp. 1757–1783]. Unfortunately, the proposed correction is negatively biased (sometimes even resulting in negative estimated variances) and therefore leads to overestimation of the inferred significance. We present an unbiased correction procedure and apply it to the models reported by Feenstra and Hanson (1999) and Haskel and Slaughter (2002) .     

Residual‐based IV estimation of dynamic panel data models with fixed effects

In dynamic panel regression, when the variance ratio of individual effects to disturbance is large, the system‐GMM estimator will have large asymptotic variance and poor finite sample performance. To deal with this variance ratio problem, we propose a residual‐based instrumental variables (RIV) estimator, which uses the residual from regressing Δy_i,t?1 on as the instrument for the level equation. The RIV estimator proposed is consistent and asymptotically normal under general assumptions. More importantly, its asymptotic variance is almost unaffected by the variance ratio of individual effects to disturbance. Monte Carlo simulations show that the RIV estimator has better finite sample performance compared to alternative estimators. The RIV estimator generates less finite sample bias than difference‐GMM, system‐GMM, collapsing‐GMM and Level‐IV estimators in most cases. Under RIV estimation, the variance ratio problem is well controlled, and the empirical distribution of its t‐statistic is similar to the standard normal distribution for moderate sample sizes.     

Social Intermediation in Base‐of‐the‐Pyramid Markets

Our study explores the structuring decisions made by intermediaries seeking to alleviate poverty by connecting base‐of‐the‐pyramid markets with more developed markets. Using intermediation theory to ground our study, we collected qualitative data on 29 social intermediation projects located within Latin America, Africa, and Asia. Our findings suggest that ‘socializing’ intermediation theory to more accurately explain and predict structural outcomes across more diverse contexts requires three key modifications: (1) the attenuation of opportunism, which creates an internalizing social force; (2) the accommodation of non‐monetary objectives, which creates an externalizing social force; and (3) the perception of transaction capabilities as tractable, which serves as a guidepost for reconciling these two opposing social forces.     

ESTIMATION OF CENSORED PANEL‐DATA MODELS WITH SLOPE HETEROGENEITY

This paper considers estimation of censored panel‐data models with individual‐specific slope heterogeneity. The slope heterogeneity may be random (random slopes model) or related to covariates (correlated random slopes model). Maximum likelihood and censored least‐absolute deviations estimators are proposed for both models. The estimators are simple to implement and, in the case of maximum likelihood, lead to straightforward estimation of partial effects. The rescaled bootstrap suggested by Andrews (Econometrica 2000; 68 : 399–405) is used to deal with the possibility of variance parameters being equal to zero. The methodology is applied to an empirical study of Dutch household portfolio choice, where the outcome variable (portfolio share in safe assets) has corner solutions at zero and one. As predicted by economic theory, there is strong evidence of correlated random slopes for the age profiles, indicating a heterogeneous age profile of portfolio adjustment that varies significantly with other household characteristics. Copyright © 2013 John Wiley & Sons, Ltd.