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Gemmell, Misch, and Moreno-Dodson
The SVAR method essentially treats all variables—GDP, fiscal variables, technology, factor inputs, and so forth—as endogenous. However, by making assumptions about the lags with which different elements of the government budget will affect output, it can test for the temporal precedence of these different fiscal elements and allow for feedback effects among them. A common assumption in SVAR approaches is that an exogenous public expenditure change affects output only with a lag, not contemporaneously. The approach is especially suited to examining the immediate effects of a spending stimulus or withdrawal, such as that occurring during and after the 2008–09 global crisis.9 This allows the initial “shock” effect to be identified and the responses (including any tendency for changes in spending) to feed through to subsequent changes in budget deficits or tax revenues. However, SVAR methods have weaknesses. First, their specification often dictates that there are no long-run fiscal impacts on output, and, second, it can be difficult to disentangle the spending from tax effects that, in combination, feed through to output. A third consequence of the SVAR approach is that even though SVARs typically include both tax and spending variables, the government budget constraint is not explicitly taken into account. As a result, for example, the sustainability of implied changes in long-run public debt levels and the resulting growth impacts are rarely considered. This implies that even if the effects of taxes and spending were all exhausted within a few periods, public spending—through its impact on public debt in the short run—could have longer-run growth consequences. For these and other reasons, SVAR approaches to testing may be best thought of as measuring mainly the demand-side stimuli associated with new Keynesian models. Long-run regression models. As noted, most empirical tests of the longrun effects of fiscal policy on growth have used cross-section or panel regression methods applied to annual or period-averaged data. Unlike vector autoregression models, these regressions often are limited in their recognition of the joint determination of or interactions among taxes, public spending, and budget deficits, or between different spending compositions—see the review and meta-analysis of Nijkamp and Poot (2004). To see how the government budget constraint affects these regression models, consider a simplified government budget decomposition in which expenditures (E) must be financed by revenues (R) and a budget surplus/deficit (D), each of which has potential growth effects. Following the Devarajan, Swaroop, and Zou (1996) model, growth may be affected by both the level of total expenditures (E) and the share of each