1. 1 Chapter 4 Sources of Macroeconomic Fluctuations © Pierre-Richard Agénor and Peter J. Montiel

2. 2 l Macroeconomic shocks and their propagation mechanisms are likely to differ in developing countries. l This chapter based on Agénor, McDermott and Prasad (1997). l Analysis of business cycle regularities is based on quarterly data for a group of twelve middle-income countries. l These are Colombia, Chile, India, Korea, Malaysia, Mexico, Morocco, Nigeria, the Philippines, Tunisia, Turkey, and Uruguay.

3. 3 l The Data. l Detrending Techniques. l Assessing Macroeconomic Fluctuations. l Summary of the Findings.

4. The Data

5. 5 l Figure 4.1: information on a number of key economic characteristics of the sample of countries. l Most of these countries could be characterized as middle-income countries. l Urbanization rates and the proportions of agricultural output as a share of total GDP indicate that agriculture is an important, but not dominant, sector. l Indices of industrial output are used to measure business cycle fluctuations. l Manufacturing sector accounts for a significant fraction of total GDP.

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12. 12 l This variable is a reasonable proxy for measuring the aggregate cycle, because it è corresponds to output in the traded goods sector, è is closely related to business cycle shocks, either exogenous or policy-determined. l None of these countries had sustained episodes of hyperinflation during this period. l Export growth is an important contributor to overall GDP growth.

13. Detrending Techniques

14. 14 l Economic fluctuations at business cycle frequencies are examined by decomposing all the macroeconomic series into è nonstationary (trend); è stationary (cyclical) components. l Reason: certain empirical characterizations of the data are valid only if the data are stationary. l Stationary components obtained using different filters display different time series properties. l In order to examine the robustness of their results, three alternative filters have been used.

15. 15 l The Hodrick-Prescott Filter. l The Band-Pass Filter. l The Nonparametric Method.

16. 16 The Hodrick-Prescott Filter l Seasonally adjusted variable x t can be written as the sum of an unobserved trend component, x t *, and a residual cyclical component, x t c : x t = x t * + x t c. l Standard HP filter: trend component moves continuously and adjusts gradually. l x t * is extracted by solving the following minimization problem: min xt*xt*  t=1 T (x t –x t *) 2 +  t=2 T-1 [(x t+1 *–x t *) – (x t *–x t-1 *)] 2

17. 17 l Lagrange multiplier is a positive number that penalizes changes in the trend component. l The larger is, the smoother is the resulting trend series. Criticisms: l It removes valuable information from time series, and imparts spurious cyclical patterns to the data. l Choice of the value of : set to 1600 for quarterly time series. l This may reflect an overly stringent implicit assumption about the degree of persistence in x t. l Agénor, McDermott and Prasad (1997) choose a value of for each individual series generalized cross-validation.

18. 18 How is it applied? l Leave the data points out one at a time. l Choose the value of the smoothing parameter under which the missing data points are best predicted by the remainder of the data. l Estimates of the smoothing parameter showed a wide range of variation across countries and across data series.

19. 19 The Band-Pass Filter l Developed by Baxter and King (1995). l It is a moving average that filters both high frequency “noise” and low frequency “trends”. l It is constructed by è combining a low-pass filter and a high-pass filter; è imposing constraints that eliminate fluctuations at frequencies higher and lower than those corresponding to typical business cycle frequencies. l Frequency cut-offs correspond to 6 quarters and 32 quarters.

20. 20 The Nonparametric Method l No specification of functional form of the trend component of the underlying series or degree of smoothing applied to the actual data. l It permits the modeling of trends that involve higher-order polynomials without imposing a particular functional form on the trend component.

21. Assessing Macroeconomic Fluctuations

22. 22 l Measure the degree of comovement of a series y t with industrial output x t by the magnitude of the correlation coefficient  (j), j  {0,  1,  2, …}. l These correlations are between the stationary components of y t and x t, with both components derived using the same filter. l y t is procyclical, acyclical, or countercyclical, depending on whether the contemporaneous correlation coefficient  (0) is positive, zero, or negative. l y t is strongly contemporaneously correlated if 0.26  |  (0)| < 1. l y t is weakly contemporaneously correlated if 0.13  |  (0)| < 0.26. l y t is contemporaneously uncorrelated with the cycle if 0  |  (0)| < 0.13.

23. 23 l  (j) indicates the phase-shift of y t relative to the cycle in industrial output. l y t leads the cycle by j period(s) if |  (j)| is maximum for a positive j, is synchronous if |  (j)| is maximum for j = 0, and lags the cycle if |  (j)| is maximum for a negative j. Cross-correlations between domestic output and the following variables: l variables that represent economic activity in the main industrial countries and the world real interest rate; l public sector expenditure and revenues; l real wages; l prices and inflation; l monetary aggregates, monetary velocity, and domestic credit to the private sector;

24. 24 l foreign trade variables (fluctuations in merchandise trade and terms of trade); l nominal and real effective exchange rates.

25. Summary of the Findings

26. 26 l Cross-correlations over the period 1978:Q1 to 1995:Q4. Main findings can be summarized as follows: l Output volatility (standard deviations of the filtered cyclical component of industrial production), varies substantially across developing countries. è On average, it is higher than the level observed in industrial countries. è There is also considerable persistence in output fluctuations in developing countries. l Activity in industrial countries has a positive but relatively weak influence on output in developing countries. l Real interest rates in industrial countries are positively associated with output fluctuations.

27. 27 l Government expenditure is countercyclical. è Government revenues are acyclical in some countries, and significantly countercyclical in others. è Fiscal impulse is negatively correlated with the business cycle. l Cyclical behavior of nominal wages varies across countries and is not robust across filters. l Evidence strongly supports the assumption of procyclical real wages. l There is no consistent relationship between the stationary components of the levels of output and prices, or the levels of output and inflation. l Contemporaneous correlations between money and output are positive, but not strong.

28. 28 l Contemporaneous correlations between the velocity of broad money and industrial output are strongly negative across all filters for almost all the countries. l Domestic credit and industrial output are positively associated for some countries. l No robust correlation between merchandise trade movements and output. l Cyclical movements in the terms of trade are strongly and positively correlated with output fluctuations. l No systematic patterns in the contemporaneous correlations between nominal effective exchange rates and industrial output. l Overall result: Importance of supply-side shocks in driving business cycles in developing countries.

29. 29 Problems: l Using cross-correlation coefficients as indicators for evaluating the empirical relevance of demand-oriented, versus supply-oriented, macroeconomic theories can be problematic. l Results are not uniform across countries.