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THE DEMAND FOR MONEY AND PRICE EXPECTATIONS IN AUSTRALIA
Price-Level Stickiness and the Feasibility of Monetary Stabilization Policy with Rational Expectations
This paper considers the validity of the Lucas-Sargent Proposition, which concerns the ineffectiveness of countercyclical monetary policy when expectations are rational, under the assumption that prices are "sticky." The model of Sargent and Wallace is modified so as to incorporate stickiness as follows: in each period the price adjusts to the market-clearing value only if the latter is far from the expected value (i.e., when the cost of maintaining an inappropriate price exceeds the lump-sum cost of a revision). Otherwise the price equals the value previously expected. Given this modification, the proposition remains valid.
Optimum Savings Under Uncertainty: A Correction
D. Levhari, T. N. Srinivasan; Optimum Savings Under Uncertainty: A Correction, The Review of Economic Studies, Volume 44, Issue 1, 1 February 1977, Pages 1
What Difference Did the Beginning Make?
A Matrix Measure of Multivariate Local Risk Aversion
By looking at approximate multivariate risk premiums a matrix measure of multivariate local risk aversion is introduced for a multi-attributed utility function u. This matrix function R(x) = [-uij(x)/ui(x)] generalizes the univariate measure of Pratt [11] and the conditional measure of Keeney [7]. It has particular advantages in assessing the attitude of a decision-maker toward correlated risks, a concern of Richard [13], and is more informative than the scalar measure proposed by Kihlstrom and Mirman [8]. Simple characteristics of the absolute risk aversion matrix R determine whether a utility function is additive or concave. Assumptions of either constancy or proportionality of R are shown to lead to specific restrictions on the form of u which are more stringent than those of Rothblum [15].
Urban Crime and Household Protective Measures
Berg, Sanford V., Increasing the Efficiency of the Journal Market, Journal of Economic Literature 9 (Sept. 1971), 798-813. Bronfenbrenner, Jean, Sources and Size of Least-Squares Bias in a Two-Equation Model, in William C. Hood and Tjalling C. Koopmans (eds.), Studies in Econometric Method (New Haven: Yale University Press, 1953), 221-235. Bush, Winston C., Paul W. Hamelman and Robert J. Staaf, Quality Index for Journals, this REVIEW 56 (Feb. 1974), 123-125. Eagly, Robert V., Economics Journals as a Communications Network, Journal of Economic Literature 13 (Sept. 1975), 878-888. Hawkins, Robert G., Lawrence S. Ritter, and Ingo Walter, What Economists Think of Their Journals, Journal of Political Economy 81, (July/Aug. 1973), 1017-1032. Johnston, John, Econometric Methods, second edition (New York: McGraw-Hill, 1972). Lovell, Michael C., The Production of Economic Literature: An Interpretation, Journal of Economic Literature 11 (Mar. 1973), 27-55. Moore, William J., The Relative Quality of Journals: A Suggested Rating System, Western Economic Journal 10 (June 1972), 156-169.
The Role of Speculation in the Canadian Forward Exchange Market: Some Estimates Assuming Rational Expectations
B. T. McCallum, The Role of Speculation in the Canadian Forward Exchange Market: Some Estimates Assuming Rational Expectations, The Review of Economics and Statistics, Vol. 59, No. 2 (May, 1977), pp. 145-151
Financial Management: A Capital Market Approach.
Income Inequality and City Size
The distribution of income in the urban context has received relatively little attention from economists. Limited information has been employed to assert an inverse relationship between city size and income inequality (Duncan and Reiss, 1956; Richardson, 1973). This relationship can be rationalized by the fact that both city size and inequality are related to the level of income. Kuznets (1955) hypothesized a negative relationship between income and inequality, and he is supported by the findings of Aigner and Heins (1967), Conlisk (1967), and Al-Samarrie and Miller (1967) using state data and by Frech and Burns (1971) using SMSA data. Sveikauskas (1975) has documented that incomes are higher in larger cities. In this paper we analyze the relationship between city size and income inequality. After controlling for other factors that influence income inequality by using regression analysis on cross section data for 79 U.S. metropolitan areas, we find that income inequality appears to increase with city size.