Stephen Bond

Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations

This paper presents specification tests that are applicable after estimating a dynamic model from panel data by the generalized method of moments (GMM), and studies the practical performance of these procedures using both generated and real data. Our GMM estimator optimally exploits all the linear moment restrictions that follow from the assumption of no serial correlation in the errors, in an equation which contains individual effects, lagged dependent variables and no strictly exogenous variables. We propose a test of serial correlation based on the GMM residuals and compare this with Sargan tests of over-identifying restrictions and Hausman specification tests.

Financial Factors and Investment in Belgium, France, Germany, and the United Kingdom: A Comparison Using Company Panel Data

We construct company panel data sets for manufacturing firms in Belgium, France, Germany, and the United Kingdom, covering the period 19781989. These data sets are used to estimate empirical investment equations, and to investigate the role played by financial factors in each country. A robust finding is that cash flow and profits terms appear to be both statistically and quantitatively more significant in the United Kingdom than in the three continental European countries. This is consistent with the suggestion that financial constraints on investment may be relatively severe in the more market-oriented U.K. financial system.

Investment and Tobin's Q: Evidence from company panel data

Abstract A Q model of investment is estimated using data for an unbalanced panel of UK companies over the period 1975-86. Correlated firm-specific effects and the endogeneity of Q are allowed for using a Generalised Method of Moments estimator. In the calculation of Q we estimate the tax incentives available to individual companies. Q is found to be a significant factor in the explanation of company investment, although its effect is small and a careful treatment of the dynamic structure of Q models appears critical. In addition to Q, both cash flow and output variables are found to play an independent and significant role.

Dynamic investment models and the firm's financial policy

In this paper we investigate the sensitivity of investment to the availability of internal funds using the hierarchy of finance approach to corporate finance. We characterize the empirical implications of this approach for dynamic investment models and test these implications using firm-level data. The model we estimate is based on the Euler equation for optimal capital accumulation in the presence of convex adjustment costs. The theoretical model explicitly allows for debt finance and financial assets. The empirical investigation uses U.K. company panel data to estimate dynamic investment models using GMM and tests the derived implications.

Technology and financial structure: are innovative firms different?

We use data on publicly traded U.K. firms to investigate whether financing choices differ systematically with R&D intensity. As well as looking at a balance sheet measure of the debt/assets ratio, we also consider the probability of raising finance by issuing new equity, and the shares of bank debt and secured debt in total debt. We find a nonlinear relationship with the debt/assets ratio: firms that report positive but low R&D use more debt finance than firms that report no R&D, but the use of debt finance falls with R&D intensity among those firms that report R&D. We find a simpler relationship with the probability of issuing new equity: Firms that report R&D are more likely to raise funds by issuing shares than firms that report no R&D, and this probability increases with R&D intensity. The shares of bank debt and secured debt in total debt are both lower for firms that report R&D compared to those that do not, and tend to fall as R&D intensity rises. We discuss possible explanations for these patterns.

Finite Sample Inference for GMM Estimators in Linear Panel Data Models

We compare the finite sample performance of a range of tests of linear restrictions for linear panel data models estimated using Generalised Method of Moments (GMM). These include standard asymptotic Wald tests based on one-step and two-step GMM estimators; two bootstrapped versions of these Wald tests; a version of the two-step Wald test that uses a more accurate asymptotic approximation to the distribution of the estimator; the LM test; and three criterion-bases tests that have recently been proposed. We consider both the AR(1) panel model, and a design with predetermined regressors. The corrected two-step Wald test performs similarly to the standard one-step Wald test, whilst the bootstrapped one-step Wald test, the LM test, and a simple criterion-difference test can provide more reliable finite sample inference in some cases.

Reliable Inference for GMM Estimators? Finite Sample Properties of Alternative Test Procedures in Linear Panel Data Models

ABSTRACT We compare the finite sample performance of a range of tests of linear restrictions for linear panel data models estimated using the generalized method of moments (GMM). These include standard asymptotic Wald tests based on one-step and two-step GMM estimators; two bootstrapped versions of these Wald tests; a version of the two-step Wald test that uses a finite sample corrected estimate of the variance of the two-step GMM estimator; the LM test; and three criterion-based tests that have recently been proposed. We consider both the AR(1) panel model and a design with predetermined regressors. The corrected two-step Wald test performs similarly to the standard one-step Wald test, whilst the bootstrapped one-step Wald test, the LM test, and a simple criterion-difference test can provide more reliable finite sample inference in some cases.

Econometric Models of Company Investment

In this chapter we outline the economic theory and econometric methods that have been applied in recent years to the modelling of company investment. Our focus is on empirical research that uses panel data on individual firms. The principal source of this data is published company accounts, and typically the number of individual firms is large and the number of time periods covered is small. We therefore concentrate on the measurement and estimation issues that are raised by data of this kind. Panel data has also been used to estimate models of investment using national accounts data at the industry level, where the number of individual industries is typically small and the number of time periods often much larger. This type of model requires a different approach more akin to that used in time series modelling, and will not be discussed in detail here.

Chapter 65 Microeconometric Models of Investment and Employment

Abstract We survey recent microeconometric research on investment and employment that has used panel data on individual firms or plants. We focus on model specification and econometric estimation issues, but we also review some of the main empirical findings. We discuss advantages and limitations of microeconomic data in this context. We briefly review the neoclassical theory of the demand for capital and labour, on which most of the econometric models of investment and employment that we consider are based. We pay particular attention to dynamic factor demand models, based on the assumption that there are costs of adjustment, which have played a prominent role especially in the microeconometric literature on investment. With adjustment costs, current choices depend on expectations of future conditions. We discuss the challenges that this raises for econometric model specification, and some of the solutions that have been adopted. We also discuss estimation issues that arise for dynamic factor demand equations in the context of micro panel data for firms or plants. We then discuss a number of topics that have been the focus of recent microeconometric research on investment and employment. In particular, we review the literatures on investment and financing constraints, relative price effects on investment and employment, investment and uncertainty, investment in research and development (R&D), elasticities of substitution and complementarity between technology, capital and skilled and unskilled labour, and recent work on models with non-convex adjustment costs.

Reuse, repurposing and learning design - Lessons from the DART project

Digital Anthropological Resources for Teaching (DART) is a major project examining ways in which the use of online learning activities and repositories can enhance the teaching of anthropology and, by extension, other disciplines. This paper reports on one strand of DART activity, the development of customisable learning activities that can be repurposed for use in multiple contexts. Three examples of these activities are described and, based on their use and reuse, some key lessons for the learning technology community are identified. In particular, it is argued that repurposing is a route to successful reuse, and that engaging the teacher in a participative design process is an essential part of the repurposing process.