Dynamic Effects of Monetary Policy Shocks in Malawi* - African ...

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APPENDIX AInterpolation of GDP using Related Series 2While this study sets out to use monthly GDP time series, among other variables, the series is notavailable in monthly frequency. For this reason, we employ the Friedman method ofinterpolating time series by related series to obtain the required monthly GDP series from annualdata. The method employs log-linear interpolations of a vector of variables X t , which areavailable in both annual and monthly frequency, that explain the variable of interest , tocompute actual errors in the trend interpolation for the elements of X t . These errors are then usedto adjust the linear trend interpolation for by the weighted individual error in trendinterpolation for each regressor, where the weights are given by the respective coefficients on theX jt variable in the annual regression of X t on .To illustrate the mechanics of the approach, suppose variable is available at annual but notmonthly frequency and a vector of related variables is available at both annual and monthlyfrequency. The criteria for the selection of variables is that the intra-yearly movements ofare highly correlated with the intra-yearly movements of . This may be based on nonquantitativeconsiderations; on observed high correlation but for a different time period otherthan that earmarked for the interpolation; on co-movements between and for different timeunits for the same period; or on co-movements between series other than and butanalogous to them for the same time units and for the same period. We further assume that thevector does not contain any variables that will be included in the analysis of the study. Theinterpolation process is carried out in the following four steps:(1) Using annual observations only, compute the mean monthly growth for and . Use this toderive log-linear interpolations for both and . The linear interpolations are given by:, where and (A1)2 This methodology is adopted from Friedman, M. (1962). “The Interpolation of Time Series by Related Series”.Journal of the American Statistical Association, Vol. 57, No. 300 (Dec. 1962), pp. 729-757.i | P a g e
, where and (A2)(2) Using the annual observations only, run the regression:(A3)(3) From the actual monthly values of the variables and their interpolated values ,compute the actual error in the trend interpolation for the elements of, expressed as:(A4)(4) This error term is then used to adjust the simple linear trend interpolation for by theweighted individual error in trend interpolation for each regressor where the weights aregiven by the coefficient on the variable in the annual regression estimated in (A3). Thefinal interpolation of is:(A5)Clearly, this method results in every twelfth observation being exactly correct (since the error inthe interpolation of the variables is identically equal to zero for each variable), so that theinterpolated series always returns to the known annual observations for the series. In addition, itprovides a robust and transparent way of weighting the error term.In the interpolation of GDP for Malawi, we used actual errors in the trend interpolation oftobacco exports to adjust the linear trend interpolation for GDP. The choice of tobacco exports isbased on the fact that agriculture is the mainstay of the Malawi economy, accounting for about36 percent (2007 estimate) of the country‟s GDP and nearly 80 percent (2006 estimate) of thecountry‟s exports; employing an estimated 84.5% of the labour force; and responsible for about82.5% of foreign exchange earnings (Malawi, 2004). In addition, the economy is monoculture,with tobacco accounting for 60% of total export earnings followed by tea and sugar, contributingii | P a g e
Page 1 and 2: University of Cape TownSchool of ec
Page 3 and 4: Among the few studies undertaken on
Page 5 and 6: Interest Rate (Percent)In line with
Page 7 and 8: 3.2. Identification of Structural S
Page 9 and 10: The non-zero coefficients and in ma
Page 11 and 12: only the price level in the long ru
Page 13: long-run relationships in a linear
Page 16 and 17: Percent(RM-Innovations)Percent (BR-
Page 18 and 19: Figure 4.4 also shows that an expan
Page 20 and 21: Table 4.1 also reveals that the dif
Page 22 and 23: To determine the importance of the
Page 24 and 25: FIGURE 4.7: Impulse Responses for t
Page 26 and 27: Figure 4.9:Responses to Structural
Page 28 and 29: ates) is re-estimated with the samp
Page 30 and 31: TABLE 4.2: Variance Decomposition -
Page 32 and 33: authorities pursue both price stabi
Page 34 and 35: Dabla-Norris, E., & Floerkemeier, H
Page 38 and 39: GDPMalawi Kwacha (millions)about 10
Page 40 and 41: APPENDIX BB-1.0. Data PropertiesCoi
Page 42 and 43: Table B1: Stationarity ResultsVaria
Page 44 and 45: Table B3: Johansen Cointegration Te
Page 46 and 47: APPENDIX CTABLE C1: Roots of Charac
Page 48 and 49: APPENDIX CTABLE C3: Structural VAR
Page 50 and 51: APPENDIX CTABLE C5: Roots of Charac
Page 52 and 53: APPENDIX DFIGURE D1:Impulse Respons

Dynamic Effects of Monetary Policy Shocks in Malawi* - African ...

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