Local module identification in dynamic networks with correlated noise: the full input case

The identification of local modules in dynamic networks with known topology has recently been addressed by formulating conditions for arriving at consistent estimates of the module dynamics, typically under the assumption of having disturbances that are uncorrelated over the different nodes. The conditions typically reflect the selection of a set of node signals that are taken as predictor inputs in a MISO identification setup. In this paper an extension is made to arrive at an identification setup for the situation that process noises on the different node signals can be correlated with each other. In this situation the local module may need to be embedded in a MIMO identification setup for arriving at a consistent estimate with maximum likelihood properties. This requires the proper treatment of confounding variables. The result is an algorithm that, based on the given network topology and disturbance correlation structure, selects an appropriate set of node signals as predictor inputs and outputs in a MISO or MIMO identification setup. As a first step in the analysis, we restrict attention to the (slightly conservative) situation where the selected output node signals are predicted based on all of their in-neighbor node signals in the network.Comment: Extended version of paper submitted to the 58th IEEE Conf. Decision and Control, Nice, 201

An empirical Bayes approach to identification of modules in dynamic networks

We present a new method of identifying a specific module in a dynamic network, possibly with feedback loops. Assuming known topology, we express the dynamics by an acyclic network composed of two blocks where the first block accounts for the relation between the known reference signals and the input to the target module, while the second block contains the target module. Using an empirical Bayes approach, we model the first block as a Gaussian vector with covariance matrix (kernel) given by the recently introduced stable spline kernel. The parameters of the target module are estimated by solving a marginal likelihood problem with a novel iterative scheme based on the Expectation-Maximization algorithm. Additionally, we extend the method to include additional measurements downstream of the target module. Using Markov Chain Monte Carlo techniques, it is shown that the same iterative scheme can solve also this formulation. Numerical experiments illustrate the effectiveness of the proposed methods

Identification of Nonlinear Systems From the Knowledge Around Different Operating Conditions: A Feed-Forward Multi-Layer ANN Based Approach

The paper investigates nonlinear system identification using system output data at various linearized operating points. A feed-forward multi-layer Artificial Neural Network (ANN) based approach is used for this purpose and tested for two target applications i.e. nuclear reactor power level monitoring and an AC servo position control system. Various configurations of ANN using different activation functions, number of hidden layers and neurons in each layer are trained and tested to find out the best configuration. The training is carried out multiple times to check for consistency and the mean and standard deviation of the root mean square errors (RMSE) are reported for each configuration.Comment: "6 pages, 9 figures; The Second IEEE International Conference on Parallel, Distributed and Grid Computing (PDGC-2012), December 2012, Solan

Estimating Network Effects and Compatibility in Mobile Telecommunications

This paper provides empirical evidence on the extent of network effects and compatibility between networks in mobile telecommunications. We specify a structural model of demand for mobile telephone service, which allows us to identify the parameters of interest from the S-shape of mobile service diffusion. The network effects are measured by the dependence of consumer willingness to pay on the installed base of subscribers. Compatibility is measured by the relative extent of cross- and own-network effects. As such, it indicates whether the network effects operate at the firm level, at the industry level, or at both. We then estimate this model for the Polish mobile telephone industry using quarterly panel data for the period 1996-2001. We find strong network effects, which give rise to upward-sloping demand, and, despite full interconnection of the mobile telephone networks, low compatibility. We show also that ignoring network effects in empirical models of emerging network industries might substantially bias downward the estimated price elasticity of demand. ZUSAMMENFASSUNG - (Schätzung von Netzwerkeffekten und Kompatibilität in Mobilfunktelekommunikation) Der vorliegende Beitrag bietet empirische Evidenz für den Umfang von Netzwerkeffekten und Netzwerkkompatibilität in der Mobilfunktelekom-munikation. Wir spezifizieren ein strukturelles Modell für die Nachfrage von Mobilfunkdienstleistungen, das die Parameter von Interesse mithilfe der S-förmigen Funktion der Dienstleistungsdiffusion identifiziert. Die Netzwerkeffekte werden durch die Abhängigkeit der Zahlungsbereitschaft der Konsumenten von der installierten Abonnentenbasis gemessen. Netzwerkkompatibilität wird durch den relativen Umfang der Quer- und Eigen-Netzwerkeffekte gemessen. Sie weist darauf hin, ob Netzwerkeffekten auf Unternehmensebene, auf Industrieebene, oder auf beiden Ebenen bestehen. Dann schätzen wir das Modell für die Polnische Mobilfunkindustrie mit vierteljährlichen Paneldaten für 1996-2001. Wir stellen starke Netzwerkeffekte fest, die eine positive Steigung in der Nachfragefunktion verursachen, und - trotz der technisch vollständigen Querverbindung der Mobilfunknetze - niedrige Kompatibilität. Wir zeigen auch, dass das Übersehen von Netzwerkeffekten in empirischen Modellen von neuen Netzwerkindustrien die geschätzte Preiselastizität der Nachfrage signifikant nach unten verzerren kann.Structural Econometric Model, Network Effects, Compatibility, Mobile Telecommunications

Inferring informal risk-sharing regimes: Evidence from rural Tanzania

This paper studies informal risk-sharing regimes in a unified framework by examining intertemporal consumption behavior of rural households in Tanzania. We exploit  a theoretically-consistent link between interest rates and cross-sectional consumption moments to test alternative risk-sharing models without requiring data on interest rates or assuming a restriction to eliminate the need for such data, which are often unavailable in developing economies. We specify tests that allow us to distinguish among models even with temporal dependence in income shocks. Our analysis shows that the consumption pattern in rural Tanzania is consistent with the self-insurance regime, and that risk aversion varies substantially across districts. Imposing a strict condition on interest rates, as often done in prior literature, misses their intertemporal heterogeneity and biases the estimation of risk aversion

Inferring Informal Risk-Sharing Regimes: Evidence from Rural Tanzania

This paper studies informal risk-sharing regimes in a unified framework by examining intertemporal consumption behavior of rural households in Tanzania. We exploit  a theoretically-consistent link between interest rates and cross-sectional consumption moments to test alternative risk-sharing models without requiring data on interest rates or assuming a restriction to eliminate the need for such data, which are often unavailable in developing economies. We specify tests that allow us to distinguish among models even with temporal dependence in income shocks. Our analysis shows that the consumption pattern in rural Tanzania is consistent with the self-insurance regime, and that risk aversion varies substantially across districts. Imposing a strict condition on interest rates, as often done in prior literature, misses their intertemporal heterogeneity and biases the estimation of risk aversion