Sylvia Kaufmann

Do customer information programs reduce household electricity demand?--the Irish program

Abstract We study empirically the effectiveness of a customer information program to decrease energy demand by increasing efficient electricity use. This demand side management (DSM) program aims at reducing the lack of information on the customers’ side that is documented in related literature. We study the Irish DSM program which is particularly well suited to investigate this issue as strategic behavior is ruled out in this setting for both customers and suppliers. On the customers’ side because information programs allow only for a limited substitution of own effort. On the producers’ side because the specificity of the Irish case left no room for strategic behavior. We find that providing customers with information reduced overall electricity demand by roughly 7%, as well as reducing demand fluctuations over the year. Further, we find that the DSM program had a larger impact upon long run demand, with consumers’ short run demand behavior not being changed significantly.

Measuring business cycles with a dynamic Markov switching factor model: an assessment using Bayesian simulation methods

A Markov switching common factor is used to drive a dynamic factor model for important macroeconomic variables in eight countries. Bayesian estimation of the model is based on Markov chain Monte Carlo simulation methods which yield inferences about the unobservable path of the common factor, the latent variable of the state process and all model parameters. Additionally, simulation based filtering provides us with samples from the prediction density that can be used for model diagnostics and specification tests. The mean posterior state probabilities are used to date business cycle turning points that follow quite closely previous datings reported in the literature. Moreover, we test the Markov switching against a no-switching specification by means of a Bayes factor. The evidence proves to be quite favorable for the Markov switching model.

Is there an asymmetric effect of monetary policy over time

The present paper assesses whether monetary policy effects are asymmetric over the business cycle by estimating a univariate model for GDP including additionally the first difference of the 3-month Austrian interest rate as a measure for monetary policy. The asymmetry of the effects is captured by allowing for state-dependent parameters where the latent state variable follows a Markov switching process. The model is estimated within a Bayesian framework using Markov Chain Monte Carlo simulation methods. Model selection and specification tests are performed by means of marginal likelihood. The results document significant negative effects of monetary policy during periods of below-average growth, while the effect seems insignificant during periods of normal or above-average growth. These results corroborate those derived in theoretical models assuming price rigidities and implying a convex supply curve. Additionally, the concern of using appropriate state-identifying restrictions is raised to obtain an unbiased posterior inference. Finally, the analysis concludes by assessing the robustness of the results with respect to alternative measures of monetary policy.