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Power Argument (power + argument)

Distribution by Scientific Domains

Business, Economics, Finance and Accounting	75%

Selected Abstracts

Can panel data really improve the predictability of the monetary exchange rate model?

JOURNAL OF FORECASTING, Issue 5 2007
Joakim Westerlund
Abstract A common explanation for the inability of the monetary model to beat the random walk in forecasting future exchange rates is that conventional time series tests may have low power, and that panel data should generate more powerful tests. This paper provides an extensive evaluation of this power argument to the use of panel data in the forecasting context. In particular, by using simulations it is shown that although pooling of the individual prediction tests can lead to substantial power gains, pooling only the parameters of the forecasting equation, as has been suggested in the previous literature, does not seem to generate more powerful tests. The simulation results are illustrated through an empirical application. Copyright © 2007 John Wiley & Sons, Ltd. [source]

Wage Hikes as Supply and Demand Shock

METROECONOMICA, Issue 4 2003
Jürgen Jerger
ABSTRACT Wage hikes affect production costs and hence are usually analysed as supply shocks. There is a long-standing debate, however, about demand effects of wage variations. In this paper, we bring together these two arguments in a Kaldorian model with group-specific saving rates and a production technology that allows for redistribution between workers and entrepreneurs following a wage hike. We thereby pinpoint the conditions under which (a) wage variations affect aggregate demand and (b) the positive demand effects of wage hikes may even overcompensate the negative supply effects on aggregate employment (,purchasing power argument'). We conclude by noting that, whereas demand effects are very likely to occur, the conditions under which the purchasing power argument does indeed hold are very unrealistic. [source]

Interim Analysis and Sample Size Reassessment

BIOMETRICS, Issue 4 2000
Martin Posch
Summary. This article deals with sample size reassessment for adaptive two-stage designs based on conditional power arguments utilizing the variability observed at the first stage. Fisher's product test for the p -values from the disjoint samples at the two stages is considered in detail for the comparison of the means of two normal populations. We show that stopping rules allowing for the early acceptance of the null hypothesis that are optimal with respect to the average sample size may lead to a severe decrease of the overall power if the sample size is a priori underestimated. This problem can be overcome by choosing designs with low probabilities of early acceptance or by midtrial adaptations of the early acceptance boundary using the variability observed in the first stage. This modified procedure is negligibly anticonservative and preserves the power. [source]