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This paper reviews some of the theoretical and econometric issues involved in estimating growth models that include military spending. While the mainstream growth literature has not found military expenditure to be a significant determinant of growth, much of the defence economics literature has found significant effects. The paper argues that this is largely the product of the particular specification, the Feder-Ram model, that has been used in the defence economics literature but not in the mainstream literature. The paper critically evaluates this model, detailing its problems and limitations and suggests that it should be avoided. It also critically evaluates two alternative theoretical approaches, the Augmented Solow and the Barro models, suggesting that they provide a more promising avenue for future research. It concludes with some general comments about modelling the links between military expenditure and growth.Military expenditure, Defence spending, Growth,

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This survey uses a number of recent developments in the analysis of cointegrating Vector Autoregressions (VARs) to examine their links to the older structural modelling traditions using Autoregressive Distributed Lag (ARDL), and Simultaneous Equations Models (SEMs). In particular, it emphasizes the importance of using judgement and economic theory to supplement the statistical information. After a brief historical review it sets out the statistical framework, discusses the identification of impulse responses using the Generalized Impulse Response functions, reviews the analysis of cointegrating VARs and highlights the large number of choices applied workers have to make in determining a specification. In particular, it considers the problem of specification of intercepts and trends and the size of the VAR in more detail, and examines the advantages of the use of exogenous variables in cointegration analysis. The issues are illustrated with a small U.S. Macroeconomic model.

Recently, the large T panel literature has emphasized unobserved, time-varying heterogeneity that may stem from omitted common variables or global shocks that a¤ect each individual unit di¤erently. These latent common factors induce cross-section dependence and may lead to inconsistent regression coe¢ cient estimates if they are correlated with the explanatory variables. Moreover, if the process underlying these factors is nonstationary, the individual regressions will be spurious but pooling or averaging across individual estimates still permits consistent estimation of a long-run coe¢ cient. The need to tackle both error cross-section dependence and persistent autocorrelation is motivated by the evidence of their pervasiveness found in three well-known, international …nance and macroeconomic examples. A range of estimators is surveyed and their …nite-sample properties are examined by means of Monte Carlo experiments. These reveal that a mean group version of the common-correlated-e¤ects estimator stands out as the most robust since it is the preferred choice in rather general (non) stationary settings where regressors and errors share common factors and their factor loadings are possibly dependent. Other approaches which perform reasonably well include the two-way …xed e¤ects, demeaned mean group and between estimators but they are less e¢ cient than the common-correlated-e¤ects estimator.