Appendix—Structural Break Analysis and Robustness Tests
As mentioned earlier, we split the sample from 1999:1 to see if the inflation dynamics have changed over time in SSA. In this Appendix, we (i) provide additional analysis to support the break point of our choice, and (ii) present robustness analysis taking 1997:1 as a break point.
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We thank Ari Aisen, Michael Atingi-Ego, Karim Barhoumi, Andrew Berg, Domenico Fanizza, Rafael Portillo, and other participants at the International Monetary Fund’s African Department’s Monetary Policy Network seminar and the Interdepartmental Coordinating Group on Monetary Policy’s workshop for useful comments and suggestions.
Thornton (2008) employs panel data analysis to examine the long-run relationship between money-inflation for 36 African countries during 1960–2007. He finds a weak (strong) long-run relation between money growth and inflation for countries with low (high) inflation of low (high) money growth. This type of work, however, is not very helpful in understanding dynamics of inflation over a meaningful policy horizon.
See Fielding, Lee, and Shields (2005) for an analysis of inflation persistence in CFA countries.
In this paper, we use SSA countries to refer to countries that are not in the African Financial Community (CFA) franc zone (NCFA-SSA), excluding South Africa. However, we present the results for CFA member countries in Box 1.
Trade shares are used as weights to construct country-specific foreign variables which sum up to one for a given country. In cases where the number of country-specific variables is not the same across countries, zero-weights are assigned to countries for which the corresponding domestic variables are not available, and then rescaled the weights to sum up to one. To calculate the PPP-GDP weights in the aggregation of countries into a group, we first averaged the GDP in PPP terms over the period 2000–10 for each country in the group and then compute the share of each.
Note that, unit root and cointegration properties between variables can be accommodated by allowing for the global and idiosyncratic factors to have unit roots. We will talk about the issue in more detail in the next section.
We also run the estimations using real GDP in levels for a robustness check. The results are available upon request.
As mentioned in Dee et al. (2007), dynamic factor models can be also accommodated by incorporating lagged values of dt and ft as additional factors via suitable extensions of dt and ft.
The lag orders, pi and qi, are respectively related to the domestic variables and to both the foreign-variables and the global variables. Following Dee et al. (2007), for each country i, they are selected by the Akaike information criterion, where the maximum lag order is set equal to 2 due to data limitations.
The rank of the cointegrating space for each country is computed using Johansen’s trace and maximal eigen value statistics. The final selection of the rank orders is determined by the trace statistic, because it is known to have better power properties than the maximal eigenvalue statistic in small samples.
Given the importance of the U.S. financial variables in driving the global financial variables, U.S. specific foreign variables would be unlikely to be weakly exogenous with respect to the U.S. domestic variables. The U.S. specific foreign output and inflation variables,
See Koop, Pesaran and Potter (1996) and Pesaran and Shin (1998) for a detail description of generalized FEVD. The advantage of generalized FEVD relative to orthogonalized FEVD is that the former does not require identification restrictions (or the ordering of the endogenous variables).
We bundle exchange rates and domestic monetary variables together for the ease of interpretation. Changes in the exchange rate could be related to changes in foreign money variables or terms of trade. However, such fluctuations are controlled for in the GVAR by including these variables in each country’s model in the form of trade-weighted foreign money variables and trade-weighted foreign consumer price inflation.
We take the data on disaster affected population (%) from EM-DAT as a proxy for a measure on vulnerability to shocks.
As expected, in most the cases, the response of output to the domestic supply shock is negative. The results are based on generalized impulse responses and are available upon request.
The data source is Penn World Tables, version 7.0.
We also estimate GARCH (1, 1) model to obtain conditional standard errors of inflation for each country, which also point out a fall in the volatility of inflation during the second sub-sample. See also the Appendix on the structural break analysis and robustness tests.
In some cases, conditionalities in IMF/WB accompanying new adjustment loan programs brought these reforms.