Endogeneity in panel stochastic frontier models: an application to the Japanese cotton spinning industry

We present a panel stochastic frontier model that handles the endogeneity problem. This model can treat the endogeneity of both frontier and inefficiency variables. We apply our method to examine the technical efficiency of Japanese cotton spinning industry. Our results indicate that market concentration is endogenous, and when its endogeneity is properly handled, it has a larger negative impact on the technical efficiency of cotton spinning plants. We find that the exogenous model substantially overestimates efficiency in concentrated markets.     

Estimating three-dimensional nonlinear panel data models with interactive effects

This article considers two three-step estimation approaches to three-dimensional nonlinear panel data models with interactive effects correlated with the independent variables. We propose a Nonlinear Least Squares-Principal Component Analysis-Grid Search iterated approach to estimate the static model, and a GMM-PCA-GS iterated method to the dynamic model. Monte Carlo experiments examine the finite sample performance of the methods.     

Grouped coefficients to reduce bias in heterogeneous dynamic panel models with small T

We propose the grouped coefficients estimator to reduce bias in dynamic panels with small T that have a multilevel structure to the coefficient and factor loading heterogeneity. If groups are chosen such that the within-group heterogeneity is small, then the grouped coefficients estimator can lead to substantial bias reduction compared to pooled GMM dynamic panel estimators. We also propose using a Wald test that can be used to assess whether pooled estimators suffer from heterogeneity bias. We illustrate the usefulness of grouped coefficients with an application to labour demand in which the coefficients are grouped by sub-sector. Our results suggest that the standard pooled estimates are substantially biased.     

都市圈经济增长的空间相依性研究——基于中国三大都市圈的实证研究

There are a number of theoretical reasons why cities interact with each other. Such spatial interdependence has been largely ignored by the empirical literature with only a couple of recent papers accounting for such issues in their estimation. This paper takes spatial dependence panel data models in specifying and testing to analyze three metropolitan growth behaviors in China. We find that controlling for fixed-effects allows us to disentangle the effect of spatial dependence from that of spatial heterogeneity and that of omitted variables. The estimated relationships of traditional determinants of urbanization are robust to inclusion of terms to capture spatial interdependence, even though such interdependence is estimated to be significant. Additionally, the three metropolitan areas might be said to represent three distinct stages during the urbanization of China.      

Nonlinearity in dynamic adjustment: Semiparametric estimation of panel labor supply

We estimate a semiparametric dynamic panel data model by the local linear kernel method and we interpret the slope of the nonparametric component function as a varying slope coefficient. Thus, the slope coefficient is a smooth, but otherwise unknown, function of some of the regressors. A Monte Carlo experiment is reported to examine the finite sample performance of the local linear estimator. We apply the estimation method to a labor supply equation for men from the triannual Survey of Income and Program Participation (SIPP). Specification tests based on the estimated labor supply elasticities, partial adjustment coefficients, and residuals demonstrate the improvements from a semiparametric partially linear model. Our empirical results point to a need by economists to revisit the issue of the speed of labor market adjustment to policy induced shifts in labor demand and to take more formal econometric account of heterogeneity in wage effects when studying the distributional consequences of tax reforms for labor supply earnings. First version received: July 2000/Final version received: January 2001     

Renewable energy innovations in Europe: a dynamic panel data approach

We investigate the determinants of renewable energy R&D intensity and the impact of renewable energy innovations on firm performance, using several dynamic panel data models. We estimate these models using a large data set of European firms from 19 different countries, with some patenting activity in areas related to renewable energies during the 1987 to 2007 period. Our results confirm our priors on the determinants of the rapid development of renewable energy R&D intensity during the past decades. Additionally, we find evidence that renewable patent intensity has a significant dynamic impact on the stock market value of firms.     

Dynamic panel data models: a guide to micro data methods and practice

This paper reviews econometric methods for dynamic panel data models, and presents examples that illustrate the use of these procedures. The focus is on panels where a large number of individuals or firms are observed for a small number of time periods, typical of applications with microeconomic data. The emphasis is on single equation models with autoregressive dynamics and explanatory variables that are not strictly exogenous, and hence on the Generalised Method of Moments estimators that are widely used in this context. Two examples using firm-level panels are discussed in detail: a simple autoregressive model for investment rates; and a basic production function.JEL Classification: C23This paper is prepared for a special issue of the Portuguese Economic Journal. My understanding of this subject has benefitted immeasurably from the input of colleagues, notably Manuel Arellano, Richard Blundell, Costas Meghir, Steve Nickell and Frank Windmeijer. I thank Joao Santos Silva, Frank Windmeijer and other participants at the Cemmap/ESRC Econometric Study Group workshop in London on 22 February 2002 for detailed comments on an earlier draft. Financial support from the ESRC Centre for the Microeconomic Analysis of Public Policy is gratefully acknowledged.     

Bayesian estimation of duration models: An application of the multiperiod probit model

The multiperiod probit model is presented and Bayesian estimation using the Gibbs sampler with data augmentation is described. As an empirical illustration, the multiperiod probit model is then used to estimate a duration model using employment duration data for the Canadian province of New Brunswick. Bayesian estimation with unobserved heterogeneity is shown to be a simple extension of estimation of a duration model with no unobserved heterogeneity. More importantly, perhaps, some numerical problems encountered by other authors using Maximum Likelihood are avoided.I would like to thank M. Baker, G. Koop, A. Melino and D. Poirier for helpful comments and discussions. I would also like to thank M. Baker for providing his data.     

The growth of university spin-offs: a dynamic panel data approach

Evidence suggests that a significant percentage of the university spin-offs (USOs) have a low growth capacity, putting in question the use of public funds to promote this kind of entrepreneurship. In addition, previous studies of the USOs’ growth have not dealt with the dynamic nature of firm growth, which might have led to questionable results. This paper analyses whether the university origin of a firm conditions its growth by applying a methodology which captures this dynamic nature of growth. Using a sample of 469 Spanish USOs and an equivalently matched group of 469 Spanish non-USOs over the period 2001–2010, we find that USOs have more growth than non-USOs, both in terms of sales and employment. In addition, the USOs’ growth is influenced by a set of determinants that differ from those of non-USOs. On the basis of the results, we propose some policies to foster the USOs.     

Measuring the persistence in innovation in Spanish manufacturing firms: empirical evidence using discrete-time duration models

This paper measures the level of persistence in innovation using a large representative sample of Spanish manufacturing firms for the period 1990–2008. We determine survival in innovation activities using discrete-time duration models, which control for some of the existing problems in the continuous-time duration models used in previous studies (namely, unobserved heterogeneity and the proportional hazards assumption). This paper examines the relationship between the firm-specific characteristics of technological regimes and the persistence measured by innovative spells at the firm level. The results show that high technological opportunities, patents, cumulativeness of learning based on previous experience and accumulated R&D, as well as the use of generic knowledge provided by universities enhance persistence in innovative activity.     

Capital adjustment cost and inconsistency in income‐based dynamic panel models with fixed effects

After the seminal work of Nickell (1981), a vast literature demonstrates the inconsistency of ‘conditional convergence’ estimator in income‐based dynamic panel models with fixed effects when the time horizon (T) is short but the sample of countries (N) is large. Less attention is given to the economic root of inconsistency of the fixed effects estimator when T is also large. Using a variant of the Ramsey growth model with long‐run adjustment cost of capital, we demonstrate that the fixed effects estimator of such models could be inconsistent when T is large. This inconsistency arises because of the long‐run adjustment cost of capital which gives rise to a negative moving average coefficient in the error term. Income convergence will be thus overestimated. We theoretically characterize the order of this inconsistency. Our Monte Carlo simulation demonstrates that the size of the bias is substantial and it is greater in economies with higher capital adjustment costs. We show that the use of instrumental variables that take into account the presence of the negative moving average term in the error will overcome this bias.     

Testing for structural breaks in panel varying coefficient models: with an application to OECD health expenditure

In this article, we propose a model selection approach for testing structural breaks in a semiparametric panel varying coefficient model. Monte Carlo evidence shows that the proposed model selection approach performs well in finite sample settings. Applying the method to an empirical data, we find evidence of structural breaks in Organisation for Economic Co-operation and Development (OECD) health expenditure data by allowing for income elasticity to be state (income)-dependent. The relationship between health expenditure and income is subject to two types of structural changes: smooth changes over income and structural breaks in the time dimension. The findings hold for both foreign exchange rate-converted and Purchasing Power Parity-converted expenditure and GDP.     

Estimating elasticities of residential energy demand from panel county data using dynamic random variables models with heteroskedastic and correlated error terms

A dynamic random variables model correcting for heteroskedastic and correlated error terms over time and space and dynamic demand and using panel county data offers consistent and efficient elasticity estimates of residential electricity and natural gas demands. The model developed by Swamy [Swamy, P.A.V.B., 1974. Linear models with random coefficients. In: P. Zarembka (Eds.), Frontiers in Econometrics, Academic Press, London, pp. 143–168.] with a modification suggested by Maddala et al. [Maddala, G.S., Trost, R.P., Li, H., Joutz, F., 1997. Estimation of short-run and long-run elasticities of energy demand from panel data using shrinkage estimators. Journal of Business and Economic Statistics 15, 90–100.] uses a panel of selected California counties for the years 1983–1997 to yield elasticity estimates that differ from those obtained from more standard panel data procedures.     

Local banking and local economic growth in Italy: some panel evidence

This article provides new evidence on the contribution of local banking to local economic growth (i.e. at county level – the Italian ‘province’) in Italy. A comprehensive data set is used, which includes control variables for social capital and human capital as well as indicators of the quality of local infrastructures and the production structure of the local economy. A linear within-estimator technique with fixed effects is applied to a modified version of the so-called Barro regression in order to address the well-known econometric issues of reverse causality and estimation bias resulting from unobserved district-specific influences.     

Engineering robust instruments for GMM estimation of panel data regression models with errors in variables: a note

Econometricians have long recognized the need to account in some way for measurement errors, specification errors and endogeneity to ensure that the ordinary least squares estimator is consistent. This article introduces a new generalized method of moments estimator that relies on robust instruments to estimate panel data regression models containing errors in variables. We show how this GMM approach can be generalized for the panel data framework using higher moments and cumulants as instruments. The new instruments, engineered for greater robustness, are proposed to tackle the pervasive problem of weak instruments.     

Robotization and labour dislocation in the manufacturing sectors of OECD countries: a panel VAR approach

Robots are the most important innovation which has affected the production process in the last three decades. Thanks to the latest advances in technology, they have been able to perform an ever-increasing number of tasks, eventually replacing human work within the whole production process. However, because of the scarcity of suitable data, the extent of this potentially disrupting process is not fully assessed. This paper makes up for the lack of empirical evidence on the effect of robotization on labour dislocation using data collected by the International Federation of Robotics (IFR) on the number of robots installed in the different manufacturing industries of 16 OECD countries over the period 2011–2016. We show that at the industry level a 1% growth in the number of robots reduces the growth rate of worked hours by 0.16, as well as the selling prices and the real values of the compensations of employees. Moreover, we show that a given sector is more likely to be robotized when it is expanding both in terms of relative prices and employee compensations. We conclude that, at least in the selected countries, the introduction of robots plays a key role in slowing down human labour and compensation growth.     

Modeling corner solutions with panel data: Application to the industrial energy demand in France

This paper provides an empirical application of Lee and Pitts (1986) approach to the problem of corner solutions in the case of panel data. This model deals with corner solutions in a manner consistent with the firm behavior theory while controlling for unobserved heterogeneity. In this model, energy demand at industrial plant level is the result of a discrete choice of the type of the energy to be consumed and a continuous choice that defines the level of demand. The econometric model is, essentially, an endogenous switching regime model which requires the evaluation of multivariate probability integrals. We estimate the random effect model by maximum likelihood using a panel of industrial French plants from the paper and pulp industry. We calculate empirical price elasticities of energy demand from the model. We also study the effects on energy demand of an environmental policy aimed at reducing CO₂ emissions. The authors are grateful to the Institut Français de eEnergie for its financial support and to the SESSI for providing the data. We would like to thank two anonymous referees for useful comments and suggestions. the usual disclaimer applies.     

Trade and regional growth in Spain: panel cointegration in a small sample

In this article, we test for the existence of a relationship between per capita Gross Domestic Product (GDP) and trade, for 15 Spanish Autonomous Communities between 1988 and 2004, using a panel cointegration methodology. In particular, we implement several panel unit root tests (Maddala and Wu, 1999; Levin et al., 2002; Im et al., 2003) and panel cointegration tests (Pedroni, 1999, 2004), with a special attention to their behaviour in a small sample. We also develop a Seemingly Unrelated Regression Estimation (SURE) residual based test, in order to explicitly take into account the cross regional correlation pattern. Appropriate confidence intervals are estimated with a sieve bootstrap designed for our small time sample, preserving the dependence structure among cross sectional units. Our cointegration tests reject the existence of a significant relationship between GDP per capita and exports. However, we do find some evidence of a significant relationship between GDP per capita and imports or with total trade.     

Locally adjusted LM test for spatial dependence in fixed effects panel data models

Lee and Yu (2010) propose spatial panel data models with one-way and two-way fixed effects. Debarsy and Ertur (2010) construct LM (Lagrange multiplier) and LR (likelihood ratio) tests in the one-way fixed effects model. He and Lin (2012) derive LM tests in the two-way fixed effects model. To guard against possible local misspecification, in this paper we apply Bera and Yoon (1993) principle, and construct locally adjusted (robust) LM tests for spatial dependence in both one-way and two-way fixed effects models. Monte Carlo experiment is carried out to show the advantage of using robust LM tests over the corresponding marginal and conditional versions.