Back Matter

References

  • Fatás, Antonio, and Ilian Mihov, 2001, “Government Size and Automatic Stabilizers: International and International evidence,” Journal of International Economics, Vol. 55, No. 1 pp. 328.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barnett, Steven, and Rolando Ossowski, 2002, “Operational Aspects of Fiscal Policy in Oil-Producing Countries,” IMF Working Paper 02/177 (Washington: International Monetary Fund).

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Blanchard, Olivier, and Roberto Perotti, 1999, “An Empirical Characterization of the Dynamic Effects of Changes in Government Spending and Taxes on Output,” NBER Working Paper 7269 (Cambridge, Massachusetts: National Bureau of Economic Research).

    • Search Google Scholar
    • Export Citation
  • Bernanke, Ben, 1986, “Alternative Explanations of the Money-Income Correlation,” Carnegie-Rochester Conference Series on Public Policy, Vol. 25, pp. 49100.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clements, Michael P., and Graham E. Mizon, 1991, “Empirical Analysis of Macroeconomic Time Series: VAR and Structural Models,” European Economic Review, Vol. 35, pp. 887932.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dalsgaard, Thomas, and Alain de Serres, 1999, “Estimating Prudent Budget Margins for 11 EU Countries: A Simulated SVAR Model Approach, Working Paper 216 (Paris: Organisation for Economic Cooperation and Development).

    • Search Google Scholar
    • Export Citation
  • Deaton, Angus S., 1992, “Commodity Prices, Stabilization, and Growth in Africa,” Research Program in Development Studies, Discussion Paper 166 (Princeton: Princeton University)

    • Search Google Scholar
    • Export Citation
  • Engel, E., and R. Valdes, 2000, “Optimal Fiscal Strategy for Oil-Exporting Countries,” IMF Working Paper 00/118 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Jimenez-Rodriguez, Rebeca, and Marcello Sanchez, 2004, “Oil Price Shocks and Real GDP Growth: Empirical Evidence for Some OECD Countries,” Working Paper Series No. 362 (Frankfurt: European Central Bank).

    • Search Google Scholar
    • Export Citation
  • Kumah, Francis Y., 1996, “Common Stochastic Trends in the Current Account,” Discussion Paper No. 84 (Tilburg: Tilburg University: Center For Economic Research).

    • Search Google Scholar
    • Export Citation
  • Kumah, Francis Y., and Salifu B. Ibrahim, 1996, “Stochastic Trends and Fluctuations in the Interest Rate, Exchange Rate and the Current Account Balance,” Economic Modelling, Vol. 13, No. 3, pp. 383407.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Runkle, D., 1987, “Vector Autoregressions and Reality,” Journal of Business and Economic Statistics, Vol. 5, pp. 43742.

  • Sims, Christopher 1986, “Are Forecasting Models Usable for Policy Analysis?Quarterly Review of the Federal Reserve Bank of Minneapolis (Winter), pp. 216.

    • Search Google Scholar
    • Export Citation
  • Sims, Christopher., James H. Stock, and Mark W. Watson, 1990, “Inference in Times Series Models with Some Unit Roots,” Econometrica, Vol. 58, pp. 11344.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Talvi, Ernesto, and Carlos Vegh, 2000, “Tax Base Variability and Procyclical Fiscal Policy,” NBER Working Paper No. 7499 (Cambridge, Massachusetts: National Bureau of Economic Research)

    • Search Google Scholar
    • Export Citation

APPENDIX

Simulated α1 -significance Levels of Fiscal Aggregates and Probabilities of Exceeding Pre-Specified Fiscal Ratios

Figure 1.
Figure 1.

Kyrgyz Republic: Tax Regimes and Fiscal Performance

Citation: IMF Working Papers 2005, 171; 10.5089/9781451861907.001.A999

Source: Authors’ estimates. The legend in the first chart applies to all the other charts.
Figure 2.
Figure 2.

Tajikistan: Tax Regimes and Fiscal Performance

Citation: IMF Working Papers 2005, 171; 10.5089/9781451861907.001.A999

Source: Authors’ estimates. The legend in the first chart applies to all the other charts.
Figure 3.
Figure 3.

Kazakhstan: Tax Regimes and Fiscal Performance

Citation: IMF Working Papers 2005, 171; 10.5089/9781451861907.001.A999

Source: Authors’ estimates. The legend in the first chart applies to all the other charts.
Figure 4.
Figure 4.

Russia: Tax Regimes and Fiscal Performance

Citation: IMF Working Papers 2005, 171; 10.5089/9781451861907.001.A999

Source: Authors’ estimates. The legend in the first chart applies to all the other charts.
1

We thank our colleagues Juan Carlos Di Tata, Aasim Husain, Sam Ouliaris, Peter Winglee and Roman Zytek for useful discussions, comments and insights that helped enhance the quality of the paper. We are grateful to Malina Savova for commendable research assistance. We, however, remain fully responsible for any remaining errors or omissions.

2

As the focus of the paper is to assess the impact of commodity price changes on revenues, we include the price of oil in the index given the contribution of this item for import duties.

3

The approach used in this paper analyzes short-run movements in relevant fiscal variables in response to commodity price shocks. As such, we do not present the detailed co-integration results (which estimate long-run relationships among variables), nor do we emphasize nonstationarity of the data—the existence of co-integrating vectors (stationary long-run relationships) among the variables is sufficient for the structural VAR analysis in this paper. For a good example of the use of co-integration in a long-run analysis of co-movements among economic variables, see for example Kumah and Ibrahim (1996) and Kumah (1996). Moreover, despite the existence of unit roots in the data, Sims, Stock and Watson (1990) show that most standard, traditional asymptotic tests are still valid if the VAR is estimated in levels. For more discussions on the treatment of non-stationarities in VARs see Clements and Mizon (1991) who show that, when “cointegrated linear combinations of the elements of xt exist, the differenced model loses information” (page 895).

4

These results underscore the need for further research, especially on the relative importance of the channels through which symmetric and non-symmetric commodity price shocks affect output.

5

The average responses shown in the figure blurs the differences between responses across tax regimes. Country-specific responses, not reported in this paper, display more distinct variations across tax regimes. In Figure 2, the differences between responses across tax regimes are more pronounced for output than for the fiscal aggregates. Intuitively, this result is mainly due to the additional effects of changes in taxes and spending on output (through θ2 and θ3 in equation 7) following the commodity price shock.

6

The bootstrapping procedure draws error terms from the set of estimated residuals, generates the variables of the VAR using the estimated coefficients and re-runs the VAR a number of times (in our case, 2,000 times). From the results of the re-runs, we can derive distributions for the impulse-responses and make inferences about the dynamic behavior of the variables of the VAR following a shock to a pre-specified variable. For instance, the estimated impulse-responses reported in this paper are the median responses derived from 2,000 replications of the SVAR. In addition, we are able to infer from the re-runs the likelihood of estimated responses exceeding some pre-determined levels—this yields the probabilities that we discuss in the next section. We also infer the level of the responses at any given (say, α1) significance level, yielding the expression “α1 –significance level” of these responses.

7

This approach is widely used in the SVAR literature to estimate confidence bands for simulated impulse-responses. The approach assumes, however, that the impulse-responses are independently distributed through time, which in a strict econometric sense is not precise. Hence the confidence bands should not be interpreted as confidence intervals, but rather as indications of uncertainty around parameter estimates. This is precisely how we use the simulated bands in this paper—to derive probabilities of exceeding fiscal targets and the “α1 –significance levels” of the relevant fiscal variables.

8

The gradual increase in the deficit reflects the declining impact of the one-off effects of the commodity price increase on fiscal performance.

Commodity Price Shocks and the Oddson Fiscal Performance
Author: Mr. Francis Y Kumah and Mr. John J Matovu