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

Cover of: On the estimation of dynamic relations from combined cross section time series data = | Vidar Ringstad

Published by [H. Aschehoug] in Oslo .

Written in English

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Places:

  • Norway

Subjects:

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

Edition Notes

Book details

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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This article deals with a variety of dynamic issues in the analysis of time-series–cross-section (TSCS) data. Although the issues raised are general, we focus on applications to comparative political economy, which frequently uses TSCS data.

We begin with a discussion of spec-ification and lay out the theoretical differences implied by the. Authors dealing with combined cross-section/time-series data usually assume that complete time-series exist for all units under observation. In the co Cited by: 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'.

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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: 1 Models for time series Time series data A time series is a set of statistics, usually collected at regular intervals.

Time series data occur naturally in many application areas. • economics - e.g., monthly data for unemployment, hospital admissions, etc. • finance - e.g., daily exchange rate, a share price, Size: KB. (b) Use the property (A N B) 1 = A 1 N B 1 to get the inverse of 2 6 6 6 6 4 1 0 0 0 4 0 2 0 0 0 2 0 2 3 7 7 7 7 5 (The inverse of a 2 2 matrix is: " a 11 a 12 a.

Balestra, P. and M. Nerlove,Pooling cross section and time series data in the estmation of a dynamic model: The demand for natural gas, Econometr Kiefer, N. M.,Population heterogeneity and inference from panel data on the effects of vocational education, Journal of Political Econ Cited by: Time-Series Cross-Section Data September number of units are observed for a relatively long period of time.3 The critical assumption of TSCS models is that of "pooling"; that is, all units are characterized by the same regression equation at all points in time.

Given this assumption, we can write the generic TSCS model as. Cross-sectional data, or a cross section of a study population, in statistics and econometrics is a type of data collected by observing many subjects (such as individuals, firms, countries, or regions) at the one point or period of time.

The analysis might also have no regard to differences in time. Analysis of cross-sectional data usually consists of comparing the differences among selected. Causal network reconstruction from time series is an emerging topic in many fields of science. Beyond inferring directionality between two time series, the goal of causal network reconstruction or causal discovery is to distinguish direct from indirect dependencies and common drivers among multiple time by: $\begingroup$ @Aksakal In Fama-Macbeth procedure, they run cross-sectional regressions each time-period then take the time-series average of the regression coefficients.

They care about the cross-sectional relationship, but they want to estimate standard errors that are robust to cross-sectional correlation. $\endgroup$ – Matthew Gunn Jul An alternative to Rob Hyndman's approach, to make a single data series, is to merge the data. This might be appropriate if your multiple time series represent noisy readings from a set of machines recording the same event.

(If each time series is on a different scale you need to normalize the data first.). Mckenzie, D.J. Asymptotic Theory of Heterogeneous Dynamic Pseudo-Panels, Journal of Econometrics,My repeated cross-section time series data is selected using a cluster sample design.

I agree that an important subject related to this problem is the sample selection method of the repeated cross-section. Certain > literature treats Panel and cross-sectional time series as one in > the same, > while others indicate that they are not.

> Further complication is knowing which Stata commands to use. > Initially I ran this command >. xtreg y x1 x2 x3 x4 x5 x6 x7, re > to test to see whether GLS is necessary or simple OLS will do for > the.

A PROGRAM FOR THE ESTIMATION OF E)YNAMLC ECONOMIC REEAT1ONS FROM A TIME SERIES OF CROSS SECTIONS* 1W FRclunN An IBM FORTRAN IV level G computer programhas been developed by the author which can he used to estimate parameters of the simple variance com-ponent model suggested by Marc Nerlove(1).

This model is designed to treat data. Summary. This article deals with three models of complete systems of demand equations (Rotterdam model, AIDS and CBS model). After the models and the theoretical restrictions have been described the parameters are estimated subject to an increasing number of restrictions and using aggregate time series for the Netherlands on five commodity by: 8.

Dynamic data-driven and model-based recursive analysis for estimation of battery state-of-chargeq Yue Li, Pritthi Chattopadhyay, Sihan Xiong, Asok Ray⇑, Christopher D.

Rahn Pennsylvania State University, University Park, PAUSA highlights A combination of symbolic time series analysis and linear least-squares Size: 1MB.

Pooled Time Series and Cross Sectional Data • PTSCS data is either dominated by time OR simply has fewer units than the typical panel data set relative to the number of time periods.

• Examples include studies of dyads, countries, states observed over periods of time that are File Size: KB. Data sets may combine time series and cross section data. Two types of data sets are: A data set with cross-sections such as states, provinces or countries.

A micro-panel or longitudinal data set constructed from a survey of the same micro-units over time. refer to the former as the time series dimension and the latter as the cross-section dimension of the model.

Both di-mensions of the model can be analyzed separately, but there is a growing literature that estimates term-structure mod-els using panel data-that is, combined cross-section and time series data (e.g., see Babbs and Nowman ; Bams.

C hapter 4 Tim e-Series C onsum ption Functions I. A System o f Consum ption Functions A. The System B. Incom e and Price E la sticitie s C.

C reating "Cross-Section-Param eter Predictions" and W eighted Populations D. Incorporating the Cross-Section Variables II. E stim ation and Data A. E stim ation Procedure File Size: 8MB. Data collected on different elements at the same point in time or for the same period of time are called cross-section data.

Definition of time-series data Data collected on the same element for the same variable at different points in time or for different periods of time are called time-series data.

Time Series and Dynamic Models Section 1 Intro to Bayesian Inference Outline1 tions of Bayesian Statistics an Estimation Normal Model ariate Normal Model 1Reference: A First Course in Bayesian Statistical Methods by Peter Ho 1.

Foundations I Statistical Inference: Study of problems in which data has. Data on output, capital and labor for 15 – 20 branches of industry and 17 – 20 years were collected for each of the mentioned countries. About 40 different models of production function were estimated using time series, cross-section, and combined time series and cross-section regression analysis.

Researchers typically analyze time-series-cross-section data with a binary dependent variable (BTSCS) using ordinary logit or probit.

However, BTSCS observations are likely to violate the independence assumption of the ordinary logit or probit statistical model. It is well known that if the observations are temporally related that the results of an ordinary logit or probit analysis may be Cited by: Time Series Analysis.

Time Series Analysis. MIT S Dr. Kempthorne. Fall MIT S Lecture 8: Time Series AnalysisFile Size: KB. Time Series Cross Section Data Anjali Thomas Bohlken Time Series Cross Section Data analysis techniques are needed for datasets consisting of a number of cross-sectional units (e.g.

countries, dyads, provinces, villages) with repeated observations over time (e.g. years, months, days). Consistent Estimation of Dynamic Panel Data Models With Cross-sectional Dependence Vasilis Sara–dis June (Preliminary version) Abstract This paper proposes new moment estimators for autoregressive panels with cross-sectional dependence.

In particular, for each unit ithe proposed estimators make use of instruments with respect to another. Beck, Nathaniel and Jonathan M. Katz. “Modeling Dynamics in Time-Series-Cross- Section Political Economy Data.” Annual Review of Political Science Brunner, R.D.

and K. Liepelt. “Data Analysis, Process Analysis and System Change” Midwest (American) Journal of Political Science [AJPS] 17(1): State Estimation for Dynamic Systems presents the state of the art in this field and discusses a new method of state estimation. The method makes it possible to obtain optimal two-sided ellipsoidal bounds for reachable sets of linear and nonlinear control systems with discrete and continuous time.

POOL. The POOL command can be used to combine cross section and time series data under certain model specifications and conditions. Pooled Cross-Section Time Series data (aka Panel Data) takes the form of N cross-sectional units (a cross-sectional unit is, for example, a household, an industry or a region) with T(i) observations for cross-section i, i = 1.This paper investigates a variety of dynamic probit models for time-series– cross-section data in the context of explaining state failure.

It shows that ordinary probit, which ignores dynamics, is misleading. Alternatives that seem to produce sensible results are the transition model and a model which includes a lagged latent dependent variable.a random field.

We are concerned with data samples where the cross-sectional dimension N and the time-series dimension T take on the same order of mag- nitude. We hypothesize that the dependence in X across j and r can be repre- sented as (3) Xj(t) = uj * U(t) + Yj(t), where U is a K X 1 vector of orthogonal random walks; Y, is zero mean.

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