CROSS‐SECTIONAL DEPENDENCE AND SPILLOVERS IN SPACE AND TIME: WHERE SPATIAL ECONOMETRICS AND GLOBAL VAR MODELS MEET

To enhance the measurement of economic and financial spillovers, we bring together the spatial and global vector autoregressive (GVAR) classes of econometric models by providing a detailed methodological review where they meet in terms of structure, interpretation, and estimation. We discuss the structure of connectivity (weight) matrices used by these models and its implications for estimation. To anchor our work within the dynamic literature on spillovers, we define a general yet measurable concept of spillovers. We formalize it analytically through the indirect effects used in the spatial literature and impulse responses used in the GVAR literature. Finally, we propose a practical step‐by‐step approach for applied researchers who need to account for the existence and strength of cross‐sectional dependence in the data. This approach aims to support the selection of the appropriate modeling and estimation method and of choices that represent empirical spillovers in a clear and interpretable form.     

A nonlinear panel data model of cross-sectional dependence

This paper proposes a nonlinear panel data model which can endogenously generate both ‘weak’ and ‘strong’ cross-sectional dependence. The model’s distinguishing characteristic is that a given agent’s behaviour is influenced by an aggregation of the views or actions of those around them. The model allows for considerable flexibility in terms of the genesis of this herding or clustering type behaviour. At an econometric level, the model is shown to nest various extant dynamic panel data models. These include panel AR models, spatial models, which accommodate weak dependence only, and panel models where cross-sectional averages or factors exogenously generate strong, but not weak, cross sectional dependence. An important implication is that the appropriate model for the aggregate series becomes intrinsically nonlinear, due to the clustering behaviour, and thus requires the disaggregates to be simultaneously considered with the aggregate. We provide the associated asymptotic theory for estimation and inference. This is supplemented with Monte Carlo studies and two empirical applications which indicate the utility of our proposed model as a vehicle to model different types of cross-sectional dependence.     

THE PREDICTABILITY OF AGGREGATE CONSUMPTION GROWTH IN OECD COUNTRIES: A PANEL DATA ANALYSIS

We examine aggregate consumption growth predictability. We derive a dynamic consumption equation which encompasses relevant predictability factors: habit formation, intertemporal substitution, current income consumption and non‐separabilities between private consumption and both hours worked and government consumption. We estimate this equation for a panel of 15 OECD countries over the period 1972–2007, taking into account parameter heterogeneity, endogeneity and error cross‐sectional dependence using a GMM version of the common correlated effects mean group estimator. Small‐sample properties are demonstrated using Monte Carlo simulations. The estimation results support income growth as the only variable with significant predictive power for aggregate consumption growth. Copyright © 2013 John Wiley & Sons, Ltd.     

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.     

Private returns to R&D in the presence of spillovers,revisited

This is both a replication of Eberhardt et al. (Review of Economics and Statistics, 2013, 95(2), 436–448) using different software, and a critical extension and diagnostic reassessment of the original results. The main findings of the paper are confirmed and sometimes reinforced. We point out some inconsistencies, in particular in the calculation of standard errors for the common correlated effects pooled model; we extend the diagnostic checks; lastly, in the spirit of the original contribution, we show how local cross‐sectional dependence diagnostics can be used to provide a first assessment of the direction of spillovers. We provide complete replication code in open source R.     

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.     

No Mangoes in the Tundra: Spatial Heterogeneity in Agricultural Productivity Analysis*

In line with the wider macro productivity literature existing studies of agricultural production largely neglect technology heterogeneity, variable time‐series properties and the potential for heterogeneous but correlated total factor productivity (TFP) across countries. Our empirical approach accommodates these difficulties and seeks to model the nature of the cross‐section dependence in a sample of 128 countries (1961–2002). Our results suggest that agro‐climatic environment drives similarity in TFP evolution across countries with heterogeneous production technology. This provides a possible explanation for the failure of technology transfer from advanced countries of the temperate ‘North’ to arid and/or equatorial developing countries of the ‘South’.     

Spatial spillover effects and risk contagion around G20 stock markets based on volatility network

Employing the spatial econometric model as well as the complex network theory, this study investigates the spatial spillovers of volatility among G20 stock markets and explores the influential factors of financial risk. To achieve this objective, we use GARCH-BEKK model to construct the volatility network of G20 stock markets, and calculate the Bonacich centrality to capture the most active and influential nodes. Finally, we innovatively use the volatility network matrix as spatial weight matrix and establish spatial Durbin model to measure the direct and spatial spillover effects. We highlight several key observations: there are significant spatial spillover effects in global stock markets; volatility spillover network exists aggregation effects, hierarchical structure and dynamic evolution features; the risk contagion capability of traditional financial power countries falls, while that of “financial small countries” rises; stock market volatility, government debt and inflation are positively correlated with systemic risk, while current account and macroeconomic performance are negatively correlated; the indirect spillover effects of all explanatory variables on systemic risk are greater than the direct spillover effects.     

‘DUAL’ GRAVITY: USING SPATIAL ECONOMETRICS TO CONTROL FOR MULTILATERAL RESISTANCE

We derive a quantity‐based structural gravity equation system in which both trade flows and error terms are cross‐sectionally correlated. This system can be estimated using techniques borrowed from the spatial econometrics literature. To illustrate our methodology, we apply it to a well‐known Canada–US trade dataset. We find that border effects between the USA and Canada are smaller than suggested by previous studies: about 7.5 for Canadian provinces and about 1.3 for US states. Hence controlling directly for cross‐sectional interdependence among both trade flows and error terms reduces measured border effects by capturing ‘multilateral resistance’. Copyright © 2010 John Wiley & Sons, Ltd.     

Does Evidence on Regional Economic Convergence Depend on the Estimation Strategy? Outcomes from Analysis of a Set of NUTS2 EU Regions

Abstract

The regional economic convergence/divergence issue has been discussed extensively recently, but results obtained are not always interpretable unequivocally as a consequence of the different estimation strategies used. As it is widely recognized, the most common theoretical framework applied to measure the speed of economic convergence among countries or regions remains the β-convergence approach, linked to the neoclassical Solow model. There have been many attempts to consider variations of the basic cross-sectional specification ranging from panel data models to Bayesian spatial econometric techniques. The application of spatial econometric methodologies is an essential tool for proper statistical inference on regional data. In this context, the aim of this paper is to connect the different results obtained in the literature. More specifically, we address whether or not evidence on convergence depends upon the estimation strategy, by taking the same set of data and systematically comparing the results obtained from different estimation strategies. The results from a set of NUTS2 EU regions conclude that both the model implied by the cross-sectional analysis and the one referring to the space-time dynamics incorporated in the panel specification point to convergence. The concept of convergence implied is, however, quite different, as demonstrated throughout the paper.     

PANEL PROBIT WITH FLEXIBLE CORRELATED EFFECTS: QUANTIFYING TECHNOLOGY SPILLOVERS IN THE PRESENCE OF LATENT HETEROGENEITY

In this paper, we introduce a Bayesian panel probit model with two flexible latent effects: first, unobserved individual heterogeneity that is allowed to vary in the population according to a nonparametric distribution; and second, a latent serially correlated common error component. In doing so, we extend the approach developed in Albert and Chib (Journal of the American Statistical Association 1993; 88 : 669–679; in Bayesian Biostatistics, Berry DA, Stangl DK (eds), Marcel Dekker: New York, 1996), and in Chib and Carlin (Statistics and Computing 1999; 9 : 17–26) by releasing restrictive parametric assumptions on the latent individual effect and eliminating potential spurious state dependence with latent time effects. The model is found to outperform more traditional approaches in an extensive series of Monte Carlo simulations. We then apply the model to the estimation of a patent equation using firm‐level data on research and development (R&D). We find a strong effect of technology spillovers on R&D but little evidence of product market spillovers, consistent with economic theory. The distribution of latent firm effects is found to have a multimodal structure featuring within‐industry firm clustering. Copyright © 2012 John Wiley & Sons, Ltd.     

The Impact of Short‐ and Long‐run Exchange Rate Uncertainty on Investment: A Panel Study of Industrial Countries*

We examine the relationship between aggregate investment and exchange rate uncertainty in the G7, using panel estimation and decomposition of volatility derived from the components generalized autoregressive conditionally heteroscedastic (GARCH) model. Our dynamic panel approach takes account of potential cross‐sectional heterogeneity, which can lead to bias in estimation. We find that for a poolable subsample of European countries, it is the transitory and not the permanent component of volatility which adversely affects investment. To the extent that short‐run uncertainty in the CGARCH model characterizes higher frequency shocks generated by volatile short‐term capital flows, these are most deleterious for investment.     

Demographics and Business Cycle Volatility: A Spurious Relationship?

This paper replicates the estimation results of three studies on the impact of the age composition of the labor force on business cycle volatility and investigates whether they signal a meaningful long‐run relationship. We show that both the volatile‐age labor force share variable and the business cycle volatility measure exhibit non‐stationary behavior but find no robust evidence of cointegration. Hence the estimation results reported in the literature may be spurious. This conclusion is further supported by the finding that the strong relationship (i) disappears when cross‐sectional dependence is accounted for using the CCEP estimator and (ii) is highly sensitive to small changes in the composition of the sample, to data revisions, and to the exact definition of the volatile‐age labor share. Copyright © 2016 John Wiley & Sons, Ltd.     

Using the variance structure of the conditional autoregressive spatial specification to model knowledge spillovers

This study investigates the pattern of knowledge spillovers arising from patent activity between European regions. A Bayesian hierarchical model is developed that specifies region‐specific latent effects parameters modeled using a connectivity structure between regions that can reflect geographical proximity in conjunction with technological and other types of proximity. This approach exploits the fact that interregional relationships may exhibit industry‐specific technological linkages or transportation network linkages, which is in contrast to traditional studies relying exclusively on geographical proximity. We also allow for both symmetric and asymmetric knowledge spillovers between regions, and for heterogeneity across the regional sample. A series of formal Bayesian model comparisons provides support for a model based on technological proximity combined with spatial proximity, asymmetric knowledge spillovers, and heterogeneity in the disturbances. Estimates of region‐specific latent effects parameters structured in this fashion are produced by the model and used to draw inferences regarding the character of knowledge spillovers across the regions. The method is illustrated using sample data on patent activity covering 323 regions in nine European countries. Copyright © 2008 John Wiley & Sons, Ltd.     

Large panels with common factors and spatial correlation

This paper considers methods for estimating the slope coefficients in large panel data models that are robust to the presence of various forms of error cross-section dependence. It introduces a general framework where error cross-section dependence may arise because of unobserved common effects and/or error spill-over effects due to spatial or other forms of local dependencies. Initially, this paper focuses on a panel regression model where the idiosyncratic errors are spatially dependent and possibly serially correlated, and derives the asymptotic distributions of the mean group and pooled estimators under heterogeneous and homogeneous slope coefficients, and for these estimators proposes non-parametric variance matrix estimators. The paper then considers the more general case of a panel data model with a multifactor error structure and spatial error correlations. Under this framework, the Common Correlated Effects (CCE) estimator, recently advanced by Pesaran (2006), continues to yield estimates of the slope coefficients that are consistent and asymptotically normal. Small sample properties of the estimators under various patterns of cross-section dependence, including spatial forms, are investigated by Monte Carlo experiments. Results show that the CCE approach works well in the presence of weak and/or strong cross-sectionally correlated errors.     

Spatial analysis of liquidity risk in China

This study investigates the interaction of liquidity risk in Chinese banks through a spatial econometric method that includes geographical and economic relations. The former is defined as sharing the same border, and the latter considers both bank type and lending behavior. We find evidence of liquidity spillovers through varying spatial dependence based on geographical and economic closeness within banks. The results highlight the importance of liquidity management and provide evidence of risk co-movement for regulators taking a new viewpoint on liquidity regulation.     

SPATIAL LIMITED DEPENDENT VARIABLE MODELS: A REVIEW FOCUSED ON SPECIFICATION,ESTIMATION, AND HEALTH ECONOMICS APPLICATIONS

Modeling individual choices is one of the main aim in microeconometrics. Discrete choice models have been widely used to describe economic agents' utility functions and most of them play a paramount role in applied health economics. On the other hand, spatial econometrics collects a series of econometric tools, which are particularly useful when we deal with spatially distributed data sets. Accounting for spatial dependence can avoid inconsistency problems of the commonly used statistical estimators. However, the complex structure of spatial dependence in most of the nonlinear models still precludes a large diffusion of these spatial techniques. The purpose of this paper is then twofold. The former is to review the main methodological problems and their different solutions in spatial nonlinear modeling. The latter is to review their applications to health issues, especially those appeared in the last few years, by highlighting the main reasons why spatial discrete neighboring effects should be considered and suggesting possible future lines of development in this emerging field. Particular attention has been paid to cross‐sectional spatial discrete choice modeling. However, discussions on the main methodological advancements in other spatial limited dependent variable models and spatial panel data models are also included.     

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.     

Do Danes and Italians Rate Life Satisfaction in the Same Way? Using Vignettes to Correct for Individual‐Specific Scale Biases

Self‐reported life satisfaction is highly heterogeneous across similar countries, a phenomenon that may be explained by the different scales and benchmarks that people use to evaluate themselves. This study uses cross‐sectional data gathered from older populations in ten European countries to compare estimates from a model that assumes reporting styles are constant across respondents against estimates from a model in which anchoring vignettes help correct for individual‐specific scale biases. Variations in response scales explain much of the difference in the raw data. Moreover, the cross‐country ranking in life satisfaction depends significantly on scale biases.     

Estimating a common deterministic time trend break in large panels with cross sectional dependence

This paper develops an estimation procedure for a common deterministic time trend break in large panels. The dependent variable in each equation consists of a deterministic trend and an error term. The deterministic trend is subject to a change in the intercept, slope or both, and the break date is common for all equations. The estimation method is simply minimizing the sum of squared residuals for all possible break dates. Both serial and cross sectional correlations are important factors that decide the rate of convergence and the limiting distribution of the break date estimate. The rate of convergence is faster when the errors are stationary than when they have a unit root. When there is no cross sectional dependence among the errors, the rate of convergence depends on the number of equations and thus is faster than the univariate case. When the errors have a common factor structure with factor loadings correlated with the intercept and slope change parameters, the rate of convergence does not depend on the number of equations and thus reduces to the univariate case. The limiting distribution of the break date estimate is also provided. Some Monte Carlo experiments are performed to assess the adequacy of the asymptotic results. A brief empirical example using the US GDP price index is offered.