The Macroeconomic Controversy Over Price Rigidity — How to Resolve it and How Bayesian Estimation has Led us Astray

Price rigidity plays a central role in macroeconomic models but remains controversial. Those espousing it look to Bayesian estimated models in support, while those assuming price flexibility largely impose it on their models. So controversy continues unresolved by testing on the data. In a Monte Carlo experiment we ask how different estimation methods could help to resolve this controversy. We find Bayesian estimation creates a large potential estimation bias compared with standard estimation techniques. Indirect estimation where the bias is found to be low appears to do best, and offers the best way forward for settling the price rigidity controversy.

Can Indirect Estimation Methods and the Medical Officer of Health Reports 'Correct' Distorted Infant Mortality Rates Reported by the Registrar General? The Case of London, 1896–1911

The Registrar General's Returns are an integral source for historical demographers. Concerns have been raised, however, over the geographical accuracy of their pre-1911 mortality figures when institutional deaths were not redistributed to place of residence. This paper determines the extent of the distortions caused by institutional mortality in the context of aggregate infant mortality rates for London's registration sub-districts. The potential of two alternative methods to 'correct' these distortions is then assessed. The first method uses indirect estimation techniques based on data from the 1911 Fertility Census, and the second exploits the rich detail available from the Medical Officer of Health reports. Through narrowing the focus to seven London registration sub-districts over the years 1896–1911, it is shown that both suggested alternative methods remove the institutional mortality biases found in the Registrar General's figures, yet they come with their own limitations.

Indirect Estimation of the Development of Capital Productivity in the Regions: The Case of Poland

This study introduces a new (adopted) method of indirect estimation of the development of the productivity structure in the regions, which at the same time allows estimation of the contribution resulting from changes within the capital factor. Its theoretical background is built on the principles of growth accounting. Within this framework the study employs an arguable assumption of analogy in development of multifactor productivity of industry between the national and regional level. The literature review and empirical results shows, however, that such an assumption may be correct in some cases. Therefore, the article enhances the existing productivity analysis capabilities at the regional level. Within the aim, this study verifies the potential of applicability of proposed method on the regions of Poland. It uses the measure of symmetric mean absolute percentage error (SMAPE) to evaluate the accuracy of method proposed against actual values and the results of two other frequently used methods for disaggregation of capital among the regions in a country. The results indicate that the new method should be more accurate than the methods of regional decomposition of capital-based on value added, and flows investment accumulation. In fact, it seems to be quite correct especially in the industries of wholesale & retail trade, transport & storage, real estates, health & social work, and manufacturing. On the other hand, it is likely incorrect in the industries of information & communication activities, finance & insurance, and administrative & support activities. In general, the method seems to be more accurate for larger industries and vice versa. Higher precision is also observed for industries where capital demand is clearly increasing. Similarly, the method is more accurate in industries where none of the regions are more specialized and vice versa.

Indirect Estimation Of Distribution Algorithms For The Evolution Of Tree-Shaped Structures

This thesis explores indirect estimation of distribution algorithms (IEDAs) for the evolution of tree structured expressions. Unlike conventional estimation of distribution algorithms, IEDAs maintain a distribution of the genotype space and indirectly search the solution space by performing a genotype-to-phenotype mapping. In this work we introduce two IEDAs named PDPE and N-gram GEP. PDPE induces a population of programs, encoded as fixed-length gene expression programming (GEP) chromosomes, by iteratively refining and randomly sampling a probability distribution of program instructions. N-gram GEP attempts to capture regularities in GEP chromosomes by sampling the probability distribution of triplet of instructions (3-grams). We tested the performance of these systems using a variety of non-trivial test problems, such as symbolic regression and the lawn-mower problem. We compared PDPE and N-gram GEP with their predecessors, probabilistic incremental program evolution (PIPE) and N-gram GP, and the canonical GEP algorithm. The results proved that our methodology is more efficient than PIPE and the canonical GEP algorithm.

Indirect Estimation Of Distribution Algorithms For The Evolution Of Tree-Shaped Structures

This thesis explores indirect estimation of distribution algorithms (IEDAs) for the evolution of tree structured expressions. Unlike conventional estimation of distribution algorithms, IEDAs maintain a distribution of the genotype space and indirectly search the solution space by performing a genotype-to-phenotype mapping. In this work we introduce two IEDAs named PDPE and N-gram GEP. PDPE induces a population of programs, encoded as fixed-length gene expression programming (GEP) chromosomes, by iteratively refining and randomly sampling a probability distribution of program instructions. N-gram GEP attempts to capture regularities in GEP chromosomes by sampling the probability distribution of triplet of instructions (3-grams). We tested the performance of these systems using a variety of non-trivial test problems, such as symbolic regression and the lawn-mower problem. We compared PDPE and N-gram GEP with their predecessors, probabilistic incremental program evolution (PIPE) and N-gram GP, and the canonical GEP algorithm. The results proved that our methodology is more efficient than PIPE and the canonical GEP algorithm.