Linear regression using both temporally aggregated and temporally disaggregated data

  • 252 Pages
  • 4.18 MB
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by
Institute for Policy Analysis, University of Toronto , Toronto
Regression analysis, Time-series ana
Statementby Cheng Hsiao.
SeriesReprint series - Institute for Policy Analysis, University of Toronto -- no. 126
Classifications
LC ClassificationsQA278.2 H75
The Physical Object
Paginationp. [243]-252. --
ID Numbers
Open LibraryOL19324820M

Journal of Econometrics 19 () North-Holland Publishing Company LINEAR REGRESSION USING BOTH TEMPORALLY AGGREGATED AND TEMPORALLY DISAGGREGATED DATA F.C.

PALM and T.E. NUMAN Free University, MC Amsterdam, The Netherlands Received Junefinal version received March We consider the model introduced by Hsiao () for analyzing data Cited by:   Economists frequently encounter data which are subject to different temporal aggregation.

In this paper we give a maximum likelihood approach to these Cited by: LINEAR REGRESSION USING BOTH TEMPORALLY AGGREGATED AND TEMPORALLY DISAGGREGATED DATA F.C. Palm Th.E. Nijman '} March Comments welcome Abstract In this note we consider the model introduced by Hsiao () in order to analyze data that are subject to different temporal aggregation.

We show that maximum likelihood estimation of the. Downloadable. This paper discusses regression models with aggregated covariate data. Reparameterized likelihood function is found to be separable when one endogenous variable corresponds to one instrument. In that case, the full-information maximum likelihood estimator has an analytic form, and thus outperforms the conventional imputed value two-step estimator in terms of both.

Palm, FC & Nijman, T ' Linear regression using both temporally aggregated and temporally disaggregated data ' Serie Research Memoranda, no.

Faculty of Economics and Business Administration, Vrije Universiteit Amsterdam, by: "Linear regression using both temporally aggregated and temporally disaggregated data," Journal of Econometrics, Elsevier, vol.

10(2), pagesJune. Dagenais, Marcel G., " The use of incomplete observations in multiple regression analysis: A generalized least squares approach," Journal of Econometrics, Elsevier, vol. 1(4), pages. Access to raw data.

Details Linear regression using both temporally aggregated and temporally disaggregated data FB2

API Dataset FastSync. Content discovery. Recommender Discovery. FAQs. About About CORE Blog Contact us. Linear regression using both temporally aggregated and temporally disaggregated data. By F.C. Palm and T.E. Nijman. Cite. BibTex.

Linear regression using both temporally aggregated and. Linear regression using both temporally aggregated and temporally disaggregated data: Revisited. By Hang Qian. Get PDF ( KB) Abstract. This paper discusses regression models with aggregated covariate data.

Reparameterized likelihood function is found to be separable when one endogenous variable corresponds to one instrument. select article Linear regression using both temporally aggregated and temporally disaggregated data.

Research article Full text access Linear regression using both temporally aggregated and temporally disaggregated data. F.C. Palm, T.E. Nijman. Linear regression using both temporally aggregated and temporally disaggregated data () Pagina-navigatie: Main; Save publication.

Save as MODS; Export to Mendeley; Save as EndNote; Export to RefWorks; Title: Linear regression using both temporally aggregated and temporally disaggregated data: Published in: Journal of Econometrics, 19, Linear regression using both temporally aggregated and ().

Pagina-navigatie: Main; Save publication. Save as MODS; Export to Mendeley; Save as EndNote; Export to RefWorks; Title: Linear regression using both temporally aggregated and temporally disaggregated data: Series: Serie Research Memoranda: Author: Palm, F.C., Nijman, Th.

Qian, Hang, "Linear regression using both temporally aggregated and temporally disaggregated data: Revisited," MPRA PaperUniversity Library of Munich, Germany. Christensen, Bent Jesper & Posch, Olaf & van der Wel, Michel, Linear regression using both temporally aggregated and temporally disaggregated data.

Journal of Econometrics – CrossRef Google Scholar. Litterman, R. A random walk Markov model for the distribution of time series. Buy this book on publisher's site; Reprints and Permissions; Personalised recommendations. Cite chapter. Linear regression using both temporally aggregated and temporally disaggregated data: Macro-panels and reality: Microeconometric evidence of financing frictions and innovative activity: Missing observations in a quarterly model for the aggregate labor market in the Netherlands: On employment decisions of rational entrepreneurs under uncertainty.

“Linear Regression Using Both Temporally Aggregated and Temporally Disaggregated Data,” Journal of Econometr – Google Scholar Press, S. J., and A. "Linear regression using both temporally aggregated and temporally disaggregated data," Journal of Econometrics, Elsevier, vol.

19(), pagesAugust. Palm, F.C. & Nijman, Th., " Linear regression using both temporally aggregated and temporally disaggregated data," Serie Research MemorandaVU University Amsterdam, Faculty.

select article Linear regression using both temporally aggregated and temporally disaggregated data. Linear regression using both temporally aggregated and temporally disaggregated data. Cheng Hsiao. Pages Download PDF.

Article preview. select article The characterization of instantaneous causality: A correction. The statistical model for linear regression; the mean response is a straight-line function of the predictor variable. The sample data then fit the statistical model: Data = fit + residual.

where the errors (ε i) are independent and normally distributed N (0, σ). Linear regression also assumes equal variance of y (σ is the same for all values. The second problem we analyse is the issue of aggregation and disaggregation of data in relation to predictive precision and modelling.

Linear Regression Using Both Temporally Aggregated and. Hsiao, C.,Linear regression using both temporally aggregated and temporally disaggregated data, Journal of Econometr Nijman, T.E.

and F.C. Palm,The construction and use of approximations for missing quarterly observations: A model-based approach, Journal of Business and Economic Statistics 4, Palm F.C. and Nijman T.E., Linear regression using both temporally aggregated and temporally disaggregated data, Journal of Econometrics, Vol pages –Proietti T., Temporal disaggregation by state space methods: Dynamic regression.

Palm, F.C., and Th.E. Nijman (), “Linear regression using both temporally aggregated and temporally disaggregated data”,Journal of Econometrics, 19, – MATH Article MathSciNet Google Scholar Download references. In fact, when temporally aggregated data are used to model, study and investigate the relationship among variables, individuals, and/or entities, it is quite possible that a distorted view of.

Aggregate vs disaggregate data analysis – A paradox in the estimation of money demand function of Japan under the low interest rate policy Linear Regression Using Both Temporally Aggregated. Linear Regression Assumptions • Linear regression is a parametric method and requires that certain assumptions be met to be valid.

The sample must be representative of the population 2. The dependent variable must be of ratio/interval scale and normally distributed overall and normally distributed for each value of the independent variables 3. Estimation of China's disaggregate import demand functions Author(s): Fukumoto, Mayumi ; Linear Regression Using Both Temporally Aggregated and Temporally Disaggregated Data Author(s): Hsiao, Cheng Such a finding was further demonstrated by a multiple linear regression analysis [51] which showed that the influence of both E and CCT on colour preference was statistically significant (p.

"Linear Regression Using Both Temporally Aggregated and Temporally I)isaggegated Data", Journal of Econometrics. Vol.

19, pp. (1) It is assumed that the matrix [X' [X'.sub.*]] has full column rank k which is less than n + m. Koreisha and Fang () compare the performance of two different predictors for temporally aggregated series: one based on ARMA models generated from aggregate data.

Linear Regression vs. Multiple Regression: An Overview.

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Regression analysis is a common statistical method used in finance and regression is .The regression is the difference between the actual y value of a data point and the y value predicted by your line, and the LSRL minimizes the sum of all the squares of your regression on the line.Linear regression using both temporally aggregated and temporally disaggregated data by Palm, F.

C. & Nijman, Th. A short run econometric analysis of the international coffee market by Palm, F. C. & Vogelvang, E.

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Some econometric applications of the exact distribution of the ratio of two quadratic forms in normal variates by Palm, F. C. & Sneek.