Macroeconomic Theory: A Survey by Douglas Fisher (auth.)

By Douglas Fisher (auth.)

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This appears to work well, and more provocatively, also provides an explanation of the Kuznets-Goldsmith paradox (see note 2). 3) Yl where is previous peak income. This is the ratchet model used ori American data recently by Smyth and Jackson (1978). 2) is taken to be Y4t• rather than actual disposable income. 3) we introduce a shift parameter (y~) which could explain the upward drift in the short-run consumption function with an * economically-inspired variable. 3) were tested as a simple linear function.

In Grossman's words, A p~o~, the dynamic loanable-funds, liquidity-preference, quantity, and expenditure theories all turn out to be consistent with the structures of exchange in a monetary economy. Which better approximates reality depends upon choice-theoretic behavioral parameters which determine the pattern of spillover (1971, p. 946). The following represent the standard (textbook) positions, expressed in terms of adjustment coefficients (K~). Dynamic Classical Model (loanable funds) 30 MACROECONOMIC THEORY: A SURVEY Dynamic Keynesian Model (liquidity preference) Dynamic Neoclassical Synthesis The foregoing assume that individuals are actually able to buy and sell any quantity they wish at prevailing prices; but in disequilibrium (as in Keynes, 1936, or Clower, 1965) they cannot - they must, for example, "false trade" - and spillovers occur between markets as dissatisfied customers seek to spend all their funds.

The consequence is, even with all micro-parameters equal, we will tend to claim a significance which is not there. What one can do in this event is to calculate our original regression in doublelogarithmic form, in which case the correlation (p) will be pulled into the constant. This is another popular transformation in empirical macroeconomics whose best rationale, again, may well be to deal with an aggregation problem. Note, though, that one should base any stati~tical tests of significance on the log-normal rather than the normal distribution, if this is the rationale behind the logarithmic transformation.

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