The New Divisia Monetary Aggregates

Barnett, William A.; Offenbacher, Edward K.; Spindt, Paul A.

doi:10.1086/261275

Cited by 147 publications

(55 citation statements)

References 5 publications

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“…Cockerline and Murray (1981) compare Divisia with simple sum aggregates for Canada and conclude that Divisia aggregates display greater parameter stability. Barnett et al (1984) show that Divisia money velocity M 3 is more stable than the other monetary indicators in the USA within the period 1968-1978. In another study, these authors use many criteria (stability of demand function, controllability of money supply, causality between money and income) to compare Divisia monetary aggregates and traditional ones.…”

Section: Divisia Versus Simple Sum Monetary Aggregates: a Review Of Ementioning

confidence: 91%

“…Following Barnett (1980) and Barnett et al (1984), the Tornquist-Theil discrete time approximation to the Divisia quantity index is used to compute each Divisia monetary aggregate (DM): Figure 3 and figure 4 give the evolution of expenditure shares in BEAC and BCEAO. S1, S2 and S3 are the weighted coefficient on currency, demand deposits and quasi money respectively.…”

Section: Aggregation and Weighted Methodsmentioning

confidence: 99%

“…In another study, these authors use many criteria (stability of demand function, controllability of money supply, causality between money and income) to compare Divisia monetary aggregates and traditional ones. With regard to the criteria aforementioned, Barnett et al (1984) find that none of monetary aggregates have better empirical performances. However, the study demonstrates that at the high level of aggregation, Divisia monetary aggregates are better than simple sum in the conduct of monetary policy.…”

Section: Divisia Versus Simple Sum Monetary Aggregates: a Review Of Ementioning

confidence: 96%

“…Otherwise, the majority of studies comparing the simple sum and Divisia monetary aggregates consider their relative in sample performance using alternative models except the studies of Barnett et al (1984), Swofford and Whitney (1991), and Schunk (2001) which focus on out-of-sample forecasting. The main innovation of the paper is that we evaluate the empirical performances of alternative monetary aggregates using both in-sample and out-of-sample forecasting.…”

Section: Divisia Versus Simple Sum Monetary Aggregates: a Review Of Ementioning

confidence: 99%

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Empirical Performance of Monetary Aggregates in BEAC and BCEAO

Ndjokou¹

2013

RAE

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The aim of this study is to compare empirical performance of traditional as well as Divisia monetary aggregates. This evidence comes from the comparison of their relative performance in terms of real GDP and inflation within 1992.1 and 2007.4. For this purpose, the study uses in-sample and out-of-sample approaches. The in-sample approach focuses on variance decomposition analysis. Concerning out-of-sample analysis, a forecasting model is estimated through 2003:4 and forecast are generated for the period 2004:1-2007:4. The study leads to the following results. Inflation and real GDP are better explained respectively by Divisia M2 and simple sum M2 in BEAC. In BCEAO, the contribution of each monetary aggregate to the fluctuations of inflation and real GDP is very worst except simple sum M2 in explaining price level. Considering the forecasting ability, it appears that at the narrowest level of aggregation, there is no difference between the two types of monetary aggregates in the two Central Banks. At the broader level, Divisia M2 has a slight edge over traditional M2 in predicting real GDP in BEAC. In BCEAO, simple sum M2 dominates Divisia M2 in explaining inflation. JEL Classification: B22 C32 C43 C53 E52

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Section: Divisia Versus Simple Sum Monetary Aggregates: a Review Of Ementioning

confidence: 91%

Section: Aggregation and Weighted Methodsmentioning

confidence: 99%

Section: Divisia Versus Simple Sum Monetary Aggregates: a Review Of Ementioning

confidence: 96%

Section: Divisia Versus Simple Sum Monetary Aggregates: a Review Of Ementioning

confidence: 99%

See 2 more Smart Citations

Empirical Performance of Monetary Aggregates in BEAC and BCEAO

Ndjokou¹

2013

RAE

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“…Empirical studies on money demand were mostly conducted within the frameworks of the inventory-theoretic transactions approach [12,21] and the asset approach [3]. Before the 1970s, the focus of most time-series studies was on the relative importance of the scale variables and interest rate [13].…”

Section: Introductionmentioning

confidence: 99%

A Longitudinal Analysis of the Stability of Household Money Demand

Tin¹

2011

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Past aggregate time-series studies, conducted under the assumption of a representative economic agent, frequently show that the demand for narrowly defined M1, especially non-interest-yielding demand deposit, is unstable during periods of financial innovations. Whether this is longitudinally the case among life-cycle savers is unclear. This study utilizes longitudinal data to take another look and find that volatility in the demand for non-interest-earning checking accounts in the mid and late 1990s is attributable solely to the portion held for the transactions motive. When the conventional Baumol-Tobin model is extended to include human capital and family formation variables representing the life-cycle motive, equilibrium money demand is a stable function of both economic and demographic variables.

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Forecasting inflation and output growth with credit‐card‐augmented Divisia monetary aggregates

Barnett

Park

2022

Journal of Forecasting

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This paper investigates the performance of the Credit-Card-Augmented Divisia monetary aggregates in forecasting U.S. inflation and output growth at the 12-month horizon. We compute recursive and rolling out-of-sample forecasts using an Autoregressive Distributed Lag (ADL) model based on Divisia monetary aggregates. We use the three available versions of those monetary aggregate indices, including the original Divisia aggregates, the credit cardaugmented Divisia, and the credit-card-augmented Divisia inside money aggregates. The source of each is the Center for Financial Stability (CFS). We find that the smallest Root Mean Square Forecast Errors (RMSFE) are attained with the credit-card-augmented Divisia indices used as the forecast indicators. We also consider Bayesian vector autoregression (BVAR) for forecasting annual inflation and output growth.

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The New Divisia Monetary Aggregates

Cited by 147 publications

References 5 publications

Empirical Performance of Monetary Aggregates in BEAC and BCEAO

Empirical Performance of Monetary Aggregates in BEAC and BCEAO

A Longitudinal Analysis of the Stability of Household Money Demand

Forecasting inflation and output growth with credit‐card‐augmented Divisia monetary aggregates

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