Aggregate vs. disaggregate data analysis—a paradox in the estimation of a money demand function of Japan under the low interest rate policy

Hsiao, Chêng; Shen, Yan; Fujiki, Hiroshi

doi:10.1002/jae.806

Cited by 56 publications

(33 citation statements)

References 39 publications

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“…Although this method was initially developed based on the assumption that all data are stationary, Hsiao (1997a,b) has extended its usage to structural dynamic simultaneous equations models with non-stationary variables. Applications of the method with non-stationary data include Hsiao et al (2005), Golinell and Rovelli (2005), and Shrestha and Tan (2005).…”

Section: Literature Review and Theoretical Frameworkmentioning

confidence: 99%

U.S. softwood lumber demand and supply estimation using cointegration in dynamic equations

Song¹,

Chang²,

Aguilar³

2011

JFE

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a b s t r a c tThis research estimated dynamic supply and demand equations for the U.S. softwood lumber using two-stage least squares. Long-run and ECM equations were derived from the estimated coefficients. Empirical data included monthly observations from 1990 to late 2006. Stationarity of the residuals was explored using Augmented Dickey-Fuller statistics. Results suggest that demand and supply elasticities in both short and long-run are relatively small compared with past studies. The Canadian softwood lumber supply to the U.S. is more price elastic than the domestic softwood lumber supply. U.S. import tariffs have had limited impact on the amount of softwood lumber imported from Canada. Technological progress and end-ofyear seasonal effects on softwood lumber demand and supply were significant over this period.

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Section: Literature Review and Theoretical Frameworkmentioning

confidence: 99%

U.S. softwood lumber demand and supply estimation using cointegration in dynamic equations

Song¹,

Chang²,

Aguilar³

2011

JFE

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“…This clearly highlights an importance of applying the proposed modifications to improve the forecasting performance of the functional model in practice. Combining the forecasting evaluation results for both the cross‐sectional density and the mean national inflation together, we come to a conclusion that the use of aggregate data is likely to result in misleading forecasts as it ignores the underlying dynamics of the heterogeneous microunits (Pesaran and Smith, ; Hsiao et al ., ).…”

Section: Forecasting the Uk Inflation Ratesmentioning

confidence: 97%

“…It follows that analysis based on aggregate data may be severely biased given the presence of informational heterogeneity across the sectors as documented by Pesaran and Smith () and Hsiao et al . ().…”

Section: Introductionmentioning

confidence: 97%

Forecasting Distributions of Inflation Rates: the Functional Auto-Regressive Approach

Chaudhuri

Kim

Shin

2015

Journal of the Royal Statistical Society Series A: Statistics in Society

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In line with the recent developments on the statistical analysis of functional data, we develop the semiparametric functional autoregressive (FAR) modeling approach to the density forecasting analysis of national inflation rates using sectoral inflation rates in the UK over the period January 1997-September 2013. The pseudo out-of-sample forecasting evaluation and test results provide an overall support to superior performance of our proposed models over the aggregate autoregressive models and their statistical validity. The fan-chart analysis and the probability event forecasting exercise provide a further support for our approach in a qualitative sense, revealing that the modified FAR models can provide a complementary tool for generating the density forecast of inflation, and analyse the performance of the central bank in achieving announced inflation target. As inflation targeting monetary policies are usually set with recourse to the medium-term forecasts, our proposed work may provide policymakers with an invaluably enriched information set.JEL Classification: C14, C53, E31.

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“…Hsiao et al, 2005;Pesaran, 2003; for further discussion of aggregation bias). Since the latter pools households' disparate responses to a given change in the explanatory variables, it may be prone to aggregation bias (see, e.g.…”

Section: (Iii) Weighted Average Real Price Elasticitiesmentioning

confidence: 99%

An Econometric Assessment of Pricing Sydney’s Residential Water Use*

et al. 2011

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There is currently a high level of interest among policy review agencies in using water usage prices to balance the supply and demand for water (scarcity pricing). The potential effectiveness of scarcity pricing depends upon the price elasticity of demand. This study applies panel data analysis to household level data to obtain short-and long-run estimates of the price elasticity of demand in Sydney. The period of the analysis spans June 2004 to June 2009, during which water usage prices increased in real terms by over 45 per cent. The data are disaggregated into residential user groups of owner-occupied and tenanted houses that did and did not participate in a subsidised water appliance efficiency programme, and housing units. Owner-occupied and tenanted houses are further disaggregated through clustering analysis based on property size. The estimated real price elasticities vary both across and within user groups. The weighted average real price elasticities are small, particularly in the short run. As well as overall price elasticities, the grouping and clustering of households provides insights into the way different households respond to changes in water usage prices, weather conditions and participation in water appliance efficiency programmes. The heterogeneity in responses is found to generate statistically different results compared with an aggregate model of all residential households.

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Aggregate vs. disaggregate data analysis—a paradox in the estimation of a money demand function of Japan under the low interest rate policy

Cited by 56 publications

References 39 publications

U.S. softwood lumber demand and supply estimation using cointegration in dynamic equations

U.S. softwood lumber demand and supply estimation using cointegration in dynamic equations

Forecasting Distributions of Inflation Rates: the Functional Auto-Regressive Approach

An Econometric Assessment of Pricing Sydney’s Residential Water Use*

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