Monetary aggregation theory and statistical index numbers

This paper is the first of two from the Monetary Services Indices (MSI) Project at the Federal Reserve Bank of St. Louis. The second paper, Working Paper 96-008B, summarizes the methodology, construction and data sources for the an extensive new database of monetary services indices, often referred to as Divisia monetary aggregates, for the United States. This paper surveys the microeconomic theory of the aggregation of monetary assets, bringing together results that are not otherwise readily available in a single source. In addition to indices of the flow of monetary services, the Project's database contains dual user cost indices, measures of potential aggregation error in the monetary services indices, and measures of the stock of monetary wealth. An overview of the Project and the concept of monetary aggregation is included here as a preface. ; Earlier title: An introduction to monetary aggregation theory and statistical theory and statistical index numbersMonetary theory

Building new monetary services indices: methodology and source data

This paper is second of two from the Monetary Services Indices (MSI) Project at the Federal Reserve Bank of St. Louis. The first paper, Working Paper 96-007B, surveys the microeconomic theory of the aggregation of monetary assets. This paper describe a new database of monetary services indices (MSI) for the United States. The MSI measure the flow of monetary services received each period by households from their holdings of monetary assets; the levels of the indices are often also referred to as Divisia monetary aggregates. In addition to indices of the flow of monetary services, the database contains dual user cost indices, measures of potential aggregation error in the monetary services indices, and measures of the stock of monetary wealth. An overview of the Project and the concept of monetary aggregation is included here as a preface.Monetary theory

Divisia Second Moments: An Application of Stochastic Index Number Theory

W. A. Barnett originated the Divisia monetary aggregates, using Diewert's results on superlative index numbers and Barnett's derivation of the user cost of monetary asset services. The resulting Divisia index can be interpreted as a first moment aggregating over growth rates with expenditure shares serving as probabilities. But Theil showed that there are analogous higher order Divisia moments providing distributional information. In this paper we use the Divisia second moments to investigate distributional information in the monetary aggregate growth rates and to measure aggregation error in the Divisia first moments

Divisia Second Moments

W. A. Barnett originated the Divisia monetary aggregates, using Diewert's results on superlative index numbers and Barnett's derivation of the user cost of monetary asset services. The resulting Divisia index can be interpreted as a first moment aggregating over growth rates with expenditure shares serving as probabilities. But Theil showed that there are analogous higher order Divisia moments providing distributional information. In this paper we use the Divisia second moments to investigate distributional information in the monetary aggregate growth rates

Divisia Second Moments: An Application of Stochastic Index Number Theory

W. A. Barnett originated the Divisia monetary aggregates, using Diewert's results on superlative index numbers and Barnett's derivation of the user cost of monetary asset services. The resulting Divisia index can be interpreted as a first moment aggregating over growth rates with expenditure shares serving as probabilities. But Theil showed that there are analogous higher order Divisia moments providing distributional information. In this paper we use the Divisia second moments to investigate distributional information in the monetary aggregate growth rates and to measure aggregation error in the Divisia first moments

Solving stochastic money-in-the-utility-function models

This paper analyzes the necessary and sufficient conditions for solving money-in-the-utility-function models when contemporaneous asset returns are uncertain. A unique solution to such models is shown to exist under certain measurability conditions. Stochastic Euler equations, whose existence is normally assumed in these models, are then formally derived. The regularity conditions are weak, and economically innocuous. The results apply to the broad range of discrete-time monetary and financial models that are special cases of the model used in this paper. The method is also applicable to other dynamic models that incorporate contemporaneous uncertainty.Capital assets pricing model ; Stochastic analysis ; Econometric models ; Uncertainty ; Money

Rational seasonality

Seasonal adjustment usually relies on statistical models of seasonality that treat seasonal fluctuations as noise corrupting the `true' data. But seasonality in economic series often stems from economic behavior such as Christmas-time spending. Such economic seasonality invalidates the separability assumptions that justify the construction of aggregate economic indexes. To solve this problem, Diewert(1980,1983,1998,1999) incorporates seasonal behavior into aggregation theory. Using duality theory, I extend these results to a larger class of decision problems. I also relax Diewert's assumption of homotheticity. I provide support for Diewert's preferred seasonally-adjusted economic index using weak separability assumptions that are shown to be sufficient.Seasonal variations (Economics) ; Consumer behavior

Endogenous Risks in Central Clearing

Parallel Sessions A: Risk Measurement Partie Deu