Does Gibrat’s Law hold for Swedish energy firms?

Gibrat's law predicts that firm growth is purely random and should be independent of firm size. We use a random effects-random coefficient model to test whether Gibrat's law holds on average in the studied sample as well as at the individual firm level in the Swedish energy market. No study has yet investigated whether Gibrat's law holds for individual firms, previous studies having instead estimated whether the law holds on average in the samples studied. The present results support the claim that Gibrat's law is more likely to be rejected ex ante when an entire firm population is considered, but more likely to be confirmed ex post after market selection has "cleaned" the original population of firms or when the analysis treats more disaggregated data. From a theoretical perspective, the results are consistent with models based on passive and active learning, indicating a steady state in the firm expansion process and that Gibrat's law is violated in the short term but holds in the long term once firms have reached a steady state. These results indicate that approximately 70 % of firms in the Swedish energy sector are in steady state, with only random fluctuations in size around that level over the 15 studied years.

Determinants and effects of central bank independence reforms

This thesis consists of four empirically oriented papers on central bank independence (CBI) reforms.    Paper [1] is an investigation of why politicians around the world have chosen to give up power to independent central banks, thereby reducing their ability to control the economy. A new data-set, including the possible occurrence of CBI-reforms in 132 countries during 1980-2005, was collected. Politicians in non-OECD countries were more likely to delegate power to independent central banks if their country had been characterized by high variability in inflation and if they faced a high probability of being replaced. No such effects were found for OECD countries.    Paper [2], using a difference-in-difference approach, studies whether CBI reform matters for inflation performance. The analysis is based on a dataset including the possible occurrence of CBI-reforms in 132 countries during the period of 1980-2005. CBI reform is found to have contributed to bringing down inflation in high-inflation countries, but it seems unrelated to inflation performance in low-inflation countries.    Paper [3] investigates whether CBI-reforms are important in reducing inflation and maintaining price stability, using a random-effects random-coefficients model to account for heterogeneity in the effects of CBI-reforms on inflation. CBI-reforms are found to have reduced inflation on average by 3.31 percent, but the effect is only present when countries with historically high inflation rates are included in the sample. Countries with more modest inflation rates have achieved low inflation without institutional reforms that grant central banks more independence, thus undermining the time-inconsistency theory case for CBI. There is furthermore no evidence that CBI-reforms have contributed to lower inflation variability    Paper [4] studies the relationship between CBI and a suggested trade-off between price variability and output variability using data on CBI-levels, and data the on implementation dates of CBI-reforms. The results question the existence of such a trade-off, but indicate that there may still be potential gains in stabilization policy from CBI-reforms.

Fitting spatial models in the R package: hglm

We present a new version of the hglm package for fittinghierarchical generalized linear models (HGLM) with spatially correlated random effects. A CAR family for conditional autoregressive random effects was implemented. Eigen decomposition of the matrix describing the spatial structure (e.g. the neighborhood matrix) was used to transform the CAR random effectsinto an independent, but heteroscedastic, gaussian random effect. A linear predictor is fitted for the random effect variance to estimate the parameters in the CAR model.This gives a computationally efficient algorithm for moderately sized problems (e.g. n<5000).

Likelihood estimate of treatment effects under selection bias

We consider methods for estimating causal effects of treatment in the situation where the individuals in the treatment and the control group are self selected, i.e., the selection mechanism is not randomized. In this case, simple comparison of treated and control outcomes will not generally yield valid estimates of casual effects. The propensity score method is frequently used for the evaluation of treatment effect. However, this method is based onsome strong assumptions, which are not directly testable. In this paper, we present an alternative modeling approachto draw causal inference by using share random-effect model and the computational algorithm to draw likelihood based inference with such a model. With small numerical studies and a real data analysis, we show that our approach gives not only more efficient estimates but it is also less sensitive to model misspecifications, which we consider, than the existing methods.