* POOLING TIME-SERIES CROSS-SECTION DATA * Example from Judge p. 476 + * The data set has 4 cross-sections observed for 10 time periods. * Consider an estimation period that uses 8 time periods. * The forecasting exercise is to forecast for periods 9 and 10. SAMPLE 1 10 READ C1 C2 C3 C4 Q1 Q2 Q3 Q4 43.72 51.03 43.90 64.29 38.46 32.52 32.86 41.86 45.86 27.75 23.77 42.16 35.32 18.71 18.52 28.33 4.74 35.72 28.60 61.99 3.78 27.01 22.93 34.21 40.58 35.85 27.71 34.26 35.34 18.66 25.02 15.69 25.86 43.28 40.38 47.67 20.83 25.58 35.13 29.70 36.05 48.52 36.43 45.14 36.72 39.19 27.29 23.03 50.94 64.18 19.31 35.31 41.67 47.70 16.99 14.80 42.48 38.34 16.55 35.43 30.71 27.01 12.56 21.53 25.60 45.39 30.97 54.33 23.70 33.57 26.76 32.86 49.81 43.69 46.60 59.23 39.53 27.32 41.42 42.25 * Stack the columns into a long vector MATRIX C=(C1'|C2'|C3'|C4')' MATRIX Q=(Q1'|Q2'|Q3'|Q4')' * Set the number of time periods GEN1 NT=10 * Generate an index for each cross-section SAMPLE 1 40 GENR CSINDEX=SUM(SEAS(NT)) * Generate a time index for each cross-section GENR TINDEX=TIME(0)-NT*(CSINDEX-1) * Estimate over the time period 1 to 8 SET NOWARNSKIP SKIPIF (TINDEX.GT.8) * The model estimation assumes AR(1) errors for each cross-section * and cross-section heteroskedasticity. POOL C Q / NCROSS=4 COEF=BETA SET NOSKIP * Forecasting * Cross-section 1 FC / BLUP LIST MODEL=POOL NCROSS=4 CSNUM=1 BEG=8 END=10 * Cross-section 2 FC / BLUP LIST MODEL=POOL NCROSS=4 CSNUM=2 BEG=18 END=20 * Cross-section 3 FC / BLUP LIST MODEL=POOL NCROSS=4 CSNUM=3 BEG=28 END=30 * Cross-section 4 FC / BLUP LIST MODEL=POOL NCROSS=4 CSNUM=4 BEG=38 END=40 STOP