On the estimation of dynamic relations from combined cross section time series data =
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On the estimation of dynamic relations from combined cross section time series data = Om estimering av dynamiske relasjonar frå tverrsnitts-tidsrekkedata by Vidar Ringstad

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Published by [H. Aschehoug] in Oslo .
Written in English

Subjects:

Places:

  • Norway

Subjects:

  • Labor supply -- Norway -- Mathematical models.,
  • Econometrics.

Book details:

Edition Notes

Other titlesScandinavian journal of economics., Om estimering av dynamiske relasjonar frå tverrsnitts-tidsrekkedata.
Statementby Vidar Ringstad.
SeriesArtikler frå Statistisk sentralbyrå ; nr. 87, Artikler fra Statistisk sentralbyrå ;, nr. 87.
Classifications
LC ClassificationsHA1503 .A45 Nr. 87, HD5800.A6 .A45 Nr. 87
The Physical Object
Paginationp. 27-37 ;
Number of Pages37
ID Numbers
Open LibraryOL4474339M
ISBN 108253706200
LC Control Number79301723

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Such situations arise naturally in the context of time series data, where structural changes can occur over time, but random coefficient models have also been found useful when using cross-sectional data and individual decision making units are thought to respond differently to changes in independent : Thomas B. Fomby, Stanley R. Johnson, R. Carter Hill. Combining time series and cross sectional data for the analysis of dynamic marketing systems Article (PDF Available) February with 2, Reads How we measure 'reads'. time series cross sectional data dynamic marketing system parameter estimate available observation single time series rms vector autoregressive different way cross section great extent parameter heterogeneity important drawback empirical application sufcient degree extra parameter pooled dynamic model chicago market combine time series data som. THE USE OF analysis of covariance techniques in the problem of pooling cross section and time series data has now become a common practice in econometric work. Suppose we have data on N firms over T periods of time. The model usually used in pooling procedures is k Yij = (i + Tj + E > rXrij + Uij (i = 1, 2, N; j = 1, 2,T), r= 1.

This paper is concerned with the estimation of Cobb-Douglas production function parameters by the analysis of variance, using combined time-series and cross-section data. Some theoretical development is followed by empirical results for a sample of farm firms over a period of years. The use of a sample consisting of time series observations on a cross section constitutes an important problem of empirical research in economics. A simple version of this problem is concerned with the estimation of a vector of parameters Le in the relation. () Y=XfX3+eFile Size: KB. Dynamic panel data models are often estimated with samples for which the number of cross sections (N) far exceeds the number of available time periods (T). When T is small, straightforward application of maximum-likelihood can yield unreliable estimates, a fact that has been known for over forty years (see, e.g., Nerlove ). Combining time series and cross sectional data for the analysis of dynamic marketing systems Horváth, C. & Wieringa, J. E., , we develop pooled models that combine time series data for multiple units (e.g. stores). An important issue in estimating pooled dynamic models is the heterogeneity among cross sections, since the mean Cited by: 9.

Pooling Cross Section and Time Series Data in the Estimation of a Dynamic Model: The Demand for Natural Gas when the demand model is cast in dynamic terms and when observations are drawn from a time series of cross sections. In Section 1, we present the theoretical formulation of the dynamic model for gas. In Section 2, the results of. The results expressed in table 2 come from the application of time-series cross-section methods to the CHES dataset (Beck, ). Namely, I implemented a multilevel mixed effect model accounting Author: Nathaniel Beck. for the time series cross section estimation Bronwyn H. HALL * This paper presents the design of a program to handle the specific estimation problems asso ciated with time series-cross section data. In order to minimize the costs of dealing with this kind of data, the program design relies on theFile Size: 1MB. Japan's largest platform for academic e-journals: J-STAGE is a full text database for reviewed academic papers published by Japanese societiesCited by: