This paper compares the empirical performance of Canadian weighted monetary aggregates (in particular, Fisher ideal aggregates) with the current summation aggregates, for their information content and forecasting performance in terms of prices, real output and nominal spending for the period 1971Q1 to 1989Q3. The properties of money-demand equations for these aggregates, particularly their temporal stability, are also examined. The major aggregates considered are M1, M2, M3, M2+, and their Fisher ideal counterparts. Also considered are M3+ (which adds near-bank deposits to M3) and two liquidity aggregates, as well as their Fisher ideal counterparts. Over all, on the basis of the in-sample fit of indicator models, the out-of-sample forecasts by indicator models, the specification of money-demand functions, and the temporal stability of money-demand functions, Canadian simple-sum monetary aggregates appear to be empirically superior to Fisher ideal aggregates. Specifically, broad monetary aggregates are generally the best in predicting inflation, M1 works well in predicting nominal spending, and real M1 is the best predictor of real output. These conclusions generally agree with earlier studies, which have shown that weighted monetary aggregates rarely do better than simple-sum aggregates in predicting major Canadian macroeconomic variables.
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Paper provided by EconWPA in its series Econometrics with number
9511001.
Length: 65 pages Date of creation: 16 Nov 1995 Date of revision: Handle: RePEc:wpa:wuwpem:9511001
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Find related papers by JEL classification: C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics C5 - Mathematical and Quantitative Methods - - Econometric Modeling C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
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