In recent times the emergence of the property cycle and the effects that it has on the property market has caused the relevant parties involved in the market to start placing more emphasis on how these cycle works. The overall objective of this study is to try establish if the interpretation of market related data affects at what position these parties are relative to one another on the property curve. The study concentrates on the use of market indicators, indices and variables in trying to determine an individual’s position on the property market curve. It also concentrates on how this market data is retrieved and what effect it has on how they interpret the data. The methodology adopted for this study involves the collecting and interpretation of market related indices and indicators relevant to the property market over a ten year period from 1996 through to 2006. This data was then used to establish the key indicators used. A questionnaire was sent out to the relevant parties involved in the property market to ascertain the extent of what the main sources of market information are and how this data is collected and interpreted. This was limited to individuals in the Gauteng region. The data was examined and collected in the form of line graphs, histograms and pie charts. The data was then examined and presented in four areas: the major sources of information used by parties for market related data, to try and establish where these parties are relative to one another on the property curve, the effect that the different types of sources of information has on each party and finally to try determine by how much these parties lag or lead one another on the curve.

In this thesis we analyze learning mechanisms applied to a variety of macroeconomic models. In the first chapter, we present and discuss the advantages and limitations of estimating Dynamic Stochastic General Equilibrium (DSGE) models added with learning, thus suppressing the central assumption of rational expectations. First, we introduce the reader on how learning can be inserted in those models, starting from the discussion of where and how the rational expectations operator is substituted by the learning mechanism. We then present several additional learning setups related to the information set available to agents considered by the literature, which affect directly the dynamics of the final model. Last, we estimate three different models to assess the advantages of learning in our artificially generated data and real data for Brazil. In the second chapter, we algebraically show the limitations of learning and propose two flexible methods to deal with the parameter instability in data. The first of these methods is closely related to the DSGE-VAR methodology, which we call Learning DSGE-VAR, and the second, which departs even further from the DSGE model, which we call Learning Minimum State Variable, or LMSV. Finally, in the third chapter we provide evidences that the supposedly moderate improvements found in the previous chapters have more to do with the nature of the model at hand than to the learning method itself. To do so, we simulate problems using a time-varying structure similar to the one presented in chapter 1 and evaluate the likelihood improvements with different learning mechanisms. We then provide empirical evidences of learning in reduced form models to forecast inflation, interest rates and output gap for the Brazilian economy, using ad-hoc reduced form models commonly used by practitioners. / Nesta tese analisamos os instrumentos de aprendizado (Learning) aplicados a uma variedade de modelos macroeconômicos. Em nosso primeiro capítulo, apresentamos e discutimos as vantagens e limitações de se estimar modelos dinâmicos e estocásticos de equilíbrio geral (DSGE) acrescidos de um mecanismo de aprendizado, ou seja, abandonando-se a hipótese de expectativas racionais, tão cara a estes modelos. Em primeiro lugar, mostramos como esse mecanismo pode ser introduzido nesses modelos, começando pela discussão de onde e como o operador de expectativas racionais é substituído pelo operador de aprendizado. Em seguida apresentamos configurações alternativas em relação ao conjunto de informações disponível aos agentes dentro do mecanismo de aprendizado, que afeta diretamente a dinâmica do modelo final a ser estimado. Por fim, estimamos três modelos usando nosso mecanismo de aprendizado, aplicando-o a dados artificiais e reais para a economia brasileira. No segundo capítulo, mostramos algebricamente as limitações do mecanismo de aprendizado em modelos DSGE e propomos dois métodos mais flexíveis para lidar com a instabilidade dos parâmetros nos dados. O primeiro desses métodos é intimamente ligado à literatura de DSGEVAR, e que chamamos de Learning DSGE-VAR, enquanto o segundo método, que se afasta ainda mais do modelo DSGE, ao qual chamamos de LMSV. No terceiro capítulo, provemos evidências de que os ganhos supostamente moderados de nosso modelo de aprendizado apresentados nos dois primeiros capítulos têm mais a ver com a natureza dos modelos estimados do que com o método de aprendizado utilizado. Para tal, simulamos dois grupos de dados usando uma estrutura econômica que varia no tempo, semelhante àquela estudada no primeiro capítulo, e estimamos os modelos utilizando diferentes mecanismos de aprendizado. Por fim, fornecemos evidências empíricas de aprendizado em modelos de forma reduzida para projetar inflação, taxas de juros e hiato do produto para a economia brasileira, através de modelos ad-hoc comumente utilizado por econometristas.

Kwong, Wai Ming. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 34-35). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts also in Chinese.

Thesis (Ph. D.)--University of California, San Diego, 2005. / Vita. Includes bibliographical references.

Thesis (Ph. D.)--University of Oregon, 2002. / Typescript. Includes vita and abstract. Includes bibliographical references (leaves 161-169). Also available for download via the World Wide Web; free to University of Oregon users.

Thesis (Ph. D.)--University of California, San Diego, 2000. / Vita. Includes bibliographical references (leaves 65-68).

Thesis (Ph. D.)--University of Oregon, 1999. / Typescript. Includes vita and abstract. Includes bibliographical references (leaves 110-113). Also available for download via the World Wide Web; free to University of Oregon users. Address: http://wwwlib.umi.com/cr/uoregon/fullcit?p9947970.

Thesis (Ph. D.)--Ohio State University, 2007. / Title from first page of PDF file. Includes bibliographical references (p. 95-99).

In this thesis we analyze learning mechanisms applied to a variety of macroeconomic models. In the first chapter, we present and discuss the advantages and limitations of estimating Dynamic Stochastic General Equilibrium (DSGE) models added with learning, thus suppressing the central assumption of rational expectations. First, we introduce the reader on how learning can be inserted in those models, starting from the discussion of where and how the rational expectations operator is substituted by the learning mechanism. We then present several additional learning setups related to the information set available to agents considered by the literature, which affect directly the dynamics of the final model. Last, we estimate three different models to assess the advantages of learning in our artificially generated data and real data for Brazil. In the second chapter, we algebraically show the limitations of learning and propose two flexible methods to deal with the parameter instability in data. The first of these methods is closely related to the DSGE-VAR methodology, which we call Learning DSGE-VAR, and the second, which departs even further from the DSGE model, which we call Learning Minimum State Variable, or LMSV. Finally, in the third chapter we provide evidences that the supposedly moderate improvements found in the previous chapters have more to do with the nature of the model at hand than to the learning method itself. To do so, we simulate problems using a time-varying structure similar to the one presented in chapter 1 and evaluate the likelihood improvements with different learning mechanisms. We then provide empirical evidences of learning in reduced form models to forecast inflation, interest rates and output gap for the Brazilian economy, using ad-hoc reduced form models commonly used by practitioners. / Nesta tese analisamos os instrumentos de aprendizado (Learning) aplicados a uma variedade de modelos macroeconômicos. Em nosso primeiro capítulo, apresentamos e discutimos as vantagens e limitações de se estimar modelos dinâmicos e estocásticos de equilíbrio geral (DSGE) acrescidos de um mecanismo de aprendizado, ou seja, abandonando-se a hipótese de expectativas racionais, tão cara a estes modelos. Em primeiro lugar, mostramos como esse mecanismo pode ser introduzido nesses modelos, começando pela discussão de onde e como o operador de expectativas racionais é substituído pelo operador de aprendizado. Em seguida apresentamos configurações alternativas em relação ao conjunto de informações disponível aos agentes dentro do mecanismo de aprendizado, que afeta diretamente a dinâmica do modelo final a ser estimado. Por fim, estimamos três modelos usando nosso mecanismo de aprendizado, aplicando-o a dados artificiais e reais para a economia brasileira. No segundo capítulo, mostramos algebricamente as limitações do mecanismo de aprendizado em modelos DSGE e propomos dois métodos mais flexíveis para lidar com a instabilidade dos parâmetros nos dados. O primeiro desses métodos é intimamente ligado à literatura de DSGEVAR, e que chamamos de Learning DSGE-VAR, enquanto o segundo método, que se afasta ainda mais do modelo DSGE, ao qual chamamos de LMSV. No terceiro capítulo, provemos evidências de que os ganhos supostamente moderados de nosso modelo de aprendizado apresentados nos dois primeiros capítulos têm mais a ver com a natureza dos modelos estimados do que com o método de aprendizado utilizado. Para tal, simulamos dois grupos de dados usando uma estrutura econômica que varia no tempo, semelhante àquela estudada no primeiro capítulo, e estimamos os modelos utilizando diferentes mecanismos de aprendizado. Por fim, fornecemos evidências empíricas de aprendizado em modelos de forma reduzida para projetar inflação, taxas de juros e hiato do produto para a economia brasileira, através de modelos ad-hoc comumente utilizado por econometristas.

This study examines whether preliminary estimates of real growth of GDP and the major user side components in the Swedish quarterly national accounts are unbiased forecasts of revised estimates, and whether available information from the process of reconciling GDP from the production and user side is used efficiently to minimise revisions. Regression analysis is performed to find that preliminary GDP growth estimates are rational forecasts of revised estimates. The results are mixed for the user side components. Preliminary estimates of growth of investments and exports are rational forecasts whereas revisions of growth of government spending could be minimised by more efficiently using information about preliminary estimate values. Moreover, information about the statistical discrepancy between the GDP growth estimates from the production and user side could be used to minimise revisions of growth of consumer spending and imports, but these conclusions are sensitive to the period of volatile economic development 2008-2010.