Publication

Combining time series and cross sectional data for the analysis of dynamic marketing systems

Horváth, C. & Wieringa, J. E., 2003, s.n., 38 p.

Research output: Working paperAcademic

APA

Horváth, C., & Wieringa, J. E. (2003). Combining time series and cross sectional data for the analysis of dynamic marketing systems. s.n.

Author

Horváth, Csilla ; Wieringa, Jaap E. / Combining time series and cross sectional data for the analysis of dynamic marketing systems. s.n., 2003.

Harvard

Horváth, C & Wieringa, JE 2003 'Combining time series and cross sectional data for the analysis of dynamic marketing systems' s.n.

Standard

Combining time series and cross sectional data for the analysis of dynamic marketing systems. / Horváth, Csilla; Wieringa, Jaap E.

s.n., 2003.

Research output: Working paperAcademic

Vancouver

Horváth C, Wieringa JE. Combining time series and cross sectional data for the analysis of dynamic marketing systems. s.n. 2003.


BibTeX

@techreport{b43d9e22ccfd471ab1522f88314d1bd2,
title = "Combining time series and cross sectional data for the analysis of dynamic marketing systems",
abstract = "Vector AutoRegressive (VAR) models have become popular in analyzing the behavior of competitive marketing systems. However, an important drawback of VAR models is that the number of parameters to be estimated can become very large. This may cause estimation problems, due to a lack of degrees of freedom. In this paper, we consider a solution to these problems. Instead of using a single time series, we develop pooled models that combine time series data for multiple units (e.g. stores). These approaches increase the number of available observations to a great extent and thereby the efciency of the parameter estimates. We present a small simulation study that demonstrates this gain in efficiency. An important issue in estimating pooled dynamic models is the heterogeneity among cross sections, since the mean parameter estimates that are obtained by pooling heterogenous cross sections may be biased. In order to avoid these biases, the model should accommodate a sufficient degree of heterogeneity. At the same time, a model that needlessly allows for heterogeneity requires the estimation of extra parameters and hence, reduces efciency of the parameter estimates. So, a thorough investigation of heterogeneity should precede the choice of the nal model. We discuss pooling approaches that accommodate for parameter heterogeneity in different ways and we introduce several tests for investigating cross-sectional heterogeneity that may facilitate this choice. We provide an empirical application using data of the Chicago market of the three largest national brands in the U.S. in the 6.5 oz. tuna sh product category. We determine the appropriate level of pooling and calibrate the pooled VAR model using these data.",
author = "Csilla Horv{\'a}th and Wieringa, {Jaap E.}",
note = "Relation: http://som.rug.nl/ date_submitted:2003 Rights: Graduate School/Research Institute, Systems, Organisations and Management (SOM)",
year = "2003",
language = "English",
publisher = "s.n.",
type = "WorkingPaper",
institution = "s.n.",

}

RIS

TY - UNPB

T1 - Combining time series and cross sectional data for the analysis of dynamic marketing systems

AU - Horváth, Csilla

AU - Wieringa, Jaap E.

N1 - Relation: http://som.rug.nl/ date_submitted:2003 Rights: Graduate School/Research Institute, Systems, Organisations and Management (SOM)

PY - 2003

Y1 - 2003

N2 - Vector AutoRegressive (VAR) models have become popular in analyzing the behavior of competitive marketing systems. However, an important drawback of VAR models is that the number of parameters to be estimated can become very large. This may cause estimation problems, due to a lack of degrees of freedom. In this paper, we consider a solution to these problems. Instead of using a single time series, we develop pooled models that combine time series data for multiple units (e.g. stores). These approaches increase the number of available observations to a great extent and thereby the efciency of the parameter estimates. We present a small simulation study that demonstrates this gain in efficiency. An important issue in estimating pooled dynamic models is the heterogeneity among cross sections, since the mean parameter estimates that are obtained by pooling heterogenous cross sections may be biased. In order to avoid these biases, the model should accommodate a sufficient degree of heterogeneity. At the same time, a model that needlessly allows for heterogeneity requires the estimation of extra parameters and hence, reduces efciency of the parameter estimates. So, a thorough investigation of heterogeneity should precede the choice of the nal model. We discuss pooling approaches that accommodate for parameter heterogeneity in different ways and we introduce several tests for investigating cross-sectional heterogeneity that may facilitate this choice. We provide an empirical application using data of the Chicago market of the three largest national brands in the U.S. in the 6.5 oz. tuna sh product category. We determine the appropriate level of pooling and calibrate the pooled VAR model using these data.

AB - Vector AutoRegressive (VAR) models have become popular in analyzing the behavior of competitive marketing systems. However, an important drawback of VAR models is that the number of parameters to be estimated can become very large. This may cause estimation problems, due to a lack of degrees of freedom. In this paper, we consider a solution to these problems. Instead of using a single time series, we develop pooled models that combine time series data for multiple units (e.g. stores). These approaches increase the number of available observations to a great extent and thereby the efciency of the parameter estimates. We present a small simulation study that demonstrates this gain in efficiency. An important issue in estimating pooled dynamic models is the heterogeneity among cross sections, since the mean parameter estimates that are obtained by pooling heterogenous cross sections may be biased. In order to avoid these biases, the model should accommodate a sufficient degree of heterogeneity. At the same time, a model that needlessly allows for heterogeneity requires the estimation of extra parameters and hence, reduces efciency of the parameter estimates. So, a thorough investigation of heterogeneity should precede the choice of the nal model. We discuss pooling approaches that accommodate for parameter heterogeneity in different ways and we introduce several tests for investigating cross-sectional heterogeneity that may facilitate this choice. We provide an empirical application using data of the Chicago market of the three largest national brands in the U.S. in the 6.5 oz. tuna sh product category. We determine the appropriate level of pooling and calibrate the pooled VAR model using these data.

M3 - Working paper

BT - Combining time series and cross sectional data for the analysis of dynamic marketing systems

PB - s.n.

ER -

ID: 2992554