scholarly journals A Stochastic Model with Penalized Coefficients for Spatial Price Comparisons: An Application to Regional Price Indexes in Italy

2019 ◽  
Vol 66 (3) ◽  
pp. 512-533 ◽  
Author(s):  
José‐María Montero ◽  
Tiziana Laureti ◽  
Román Mínguez ◽  
Gema Fernández‐Avilés
Keyword(s):  
Stochastic Model ◽  
Price Indexes ◽  
Spatial Price ◽  
Price Comparisons

scholarly journals Measuring spatial price differentials at the basic heading level: a comparison of stochastic index number methods

2021 ◽  
Author(s):  
Sebastian Weinand
Keyword(s):  
Price Level ◽  
Consumer Price Index ◽  
Index Number ◽  
Price Data ◽  
Estimation Efficiency ◽  
Spatial Price ◽  
Price Comparisons ◽  
High Degree ◽  
The Impact

AbstractSpatial price comparisons rely to a high degree on the quality of the underlying price data that are collected within or across countries. Below the basic heading level, these price data often exhibit large gaps. Therefore, stochastic index number methods like the Country–Product–Dummy (CPD) method and the Gini–Eltetö–Köves–Szulc (GEKS) method are utilised for the aggregation of the price data into higher-level indices. Although the two index number methods produce differing price level estimates when prices are missing, the present paper demonstrates that both can be derived from exactly the same stochastic model. For a specific case of missing prices, it is shown that the formula underlying these price level estimates differs between the two methods only in weighting. The impact of missing prices on the efficiency of the price level estimates is analysed in two simulation studies. It can be shown that the CPD method slightly outperforms the GEKS method. Using micro data of Germany’s Consumer Price Index, it can be observed that more narrowly defined products improve estimation efficiency.

Welfare Comparisons with Heterogeneous Prices, Consumption, and Preferences

2016 ◽  
Author(s):  
Prasada Rao
Keyword(s):  
Price Change ◽  
Index Number ◽  
Welfare Change ◽  
Spatial Price ◽  
Temporal And Spatial ◽  
Price Comparisons ◽  
Temporal Measures ◽  
Cost Of Living Index ◽  
Money Metric Utility

The chapter provides an overview of the methods and techniques employed by economic statisticians in compiling measures of real expenditure for use in making temporal and spatial comparisons of economic welfare. The role of money-metric utility in making price and welfare comparisons is explored. Temporal measures of price change based on the Konus cost-of-living index and the associated measures of welfare change for individuals and groups of individuals are discussed. Links between the commonly used Laspeyres, Paasche, Fisher, and Tornqvist index numbers and the Konus index-based measures of price and real expenditure change are established. A section of the chapter is devoted to spatial price comparisons where heterogeneity in prices, consumption, and preferences poses challenges for statisticians. Multilateral index number methods based on the money-metric utility used in spatial and cross-country price and welfare comparisons including the Geary, Gini-Éltetö-Köves-Szulc, and spatial chaining methods are canvassed.

A Fruitful Stochastic Model

1964 ◽  
Vol 9 (7) ◽  
pp. 273-276
Author(s):  
ANATOL RAPOPORT
Keyword(s):  
Stochastic Model

A Stochastic Model for Evolution of Altruistic Genes

1996 ◽  
Vol 6 (4) ◽  
pp. 445-453 ◽  
Author(s):  
Roberta Donato
Keyword(s):  
Stochastic Model

A Stochastic Model for the Inheritance of the Cancer Proneness Phenotype

1987 ◽  
Vol 26 (03) ◽  
pp. 117-123
Author(s):  
P. Tautu ◽  
G. Wagner
Keyword(s):  
Stochastic Model ◽  
Gaussian Process ◽  
Covariance Function ◽  
Neoplastic Disease ◽  
Disease Phenotype ◽  
Probabilistic Representation ◽  
Temporal Behaviour ◽  
Continuous Trait ◽  
Continuous Parameter ◽  
Level Zero

SummaryA continuous parameter, stationary Gaussian process is introduced as a first approach to the probabilistic representation of the phenotype inheritance process. With some specific assumptions about the components of the covariance function, it may describe the temporal behaviour of the “cancer-proneness phenotype” (CPF) as a quantitative continuous trait. Upcrossing a fixed level (“threshold”) u and reaching level zero are the extremes of the Gaussian process considered; it is assumed that they might be interpreted as the transformation of CPF into a “neoplastic disease phenotype” or as the non-proneness to cancer, respectively.

Sign in / Sign up

Export Citation Format

Share Document