The degree work is focused on analysis of inflation with help of suitable econometric models. Inflation with it's forms and possibilities of measuring is described at the beginning of the paper. There is mentioned an importance of monitoring and analysing inflation in view of Czech national bank. Consequently there are described characteristics of time series, which are important from viewpoint of construction of econometric models. Next part of this paper is focused on characterization of econometrics models. At first there is vector autoregression model, in this connection there is discussed the essence of Granger causality and impulse reaction. There are also noticed both error correction model and vector error correction model. The empirical part of degree work involves the use of these models on selected macroeconomic time series of the Czech republic. The objective is to analyze the relationship between inflation and other individual macroeconomic quantities. There is established the optimal vector autoregressive model and the results of Granger causality and impulse reaction are interpretated. Both error correction model and vector error correction model examining cointegration are also applied.

Abstract   In this report analysis of foreign exchange rates time series are performed. First, triangular arbitrage is detected and eliminated from data series using linear algebra tools. Then Vector Autoregressive processes are calibrated and used to replicate dynamics of exchange rates as well as to forecast time series. Finally, optimal portfolio of currencies with minimal Expected Shortfall is formed using one time period ahead forecasts

No description available.

<div> <div> <div> <p>First Chapter: The ordinary least squares (OLS) estimator for spatial autoregressions may be consistent as pointed out by Lee (2002), provided that each spatial unit is influenced aggregately by a significant portion of the total units. This paper presents a unified asymptotic distribution result of the properly recentered OLS estimator and proposes a new estimator that is based on the indirect inference (II) procedure. The resulting estimator can always be used regardless of the degree of aggregate influence on each spatial unit from other units and is consistent and asymptotically normal. The new estimator does not rely on distributional assumptions and is robust to unknown heteroscedasticity. Its good finite-sample performance, in comparison with existing estimators that are also robust to heteroscedasticity, is demonstrated by a Monte Carlo study.<br></p><p><br></p><p>Second Chapter: This paper proposes a new estimation procedure for the first-order spatial autoregressive (SAR) model, where the disturbance term also follows a first-order autoregression and its innovations may be heteroscedastic. The estimation procedure is based on the principle of indirect inference that matches the ordinary least squares estimator of the two SAR coefficients (one in the outcome equation and the other in the disturbance equation) with its approximate analytical expectation. The resulting estimator is shown to be consistent, asymptotically normal and robust to unknown heteroscedasticity. Monte Carlo experiments are provided to show its finite-sample performance in comparison with existing estimators that are based on the generalized method of moments. The new estimation procedure is applied to empirical studies on teenage pregnancy rates and Airbnb accommodation prices.<br></p><p><br></p><p>Third Chapter: This paper presents a sample selection model with spatial autoregressive interactions and studies the maximum likelihood (ML) approach to estimating this model. Consistency and asymptotic normality of the ML estimator are established by the spatial near-epoch dependent (NED) properties of the selection and outcome variables. Monte Carlo simulations, based on the characteristics of female labor supply example, show that the proposed estimator has good finite-sample performance. The new model is applied to empirical study on examining the impact of climate change on agriculture in Southeast Asia.<br></p></div></div></div><div><div><div> </div> </div> </div>

This paper examines the effects of monetary policy on macroeconomic variables in Botswana as a developing small macro-economy using the Markov-switching structural vector autoregressive (MS-SVAR) framework, utilising time-series data from 1994: Q1 to 2019: Q4. The study makes use of bank rate (interest rate), inflation and output gap. The first model is a structural vector autoregressive (VAR) model that takes the form employed by Rudebusch and Svensson (1999), whilst the second one makes use of the same structure but includes Markov switching in the policy rule (i.e., Markov switching SVAR). Regime-switching models can effectively describe the data generating process when considering both in-sample and out of sample evaluations compared to the linear models, which submerge the structural changes that have occurred in the economy over the years. The results from the SVAR shows that monetary policy has a symmetric impact on the output gap and inflation. Therefore, it can be noted that non-linearities in the structural model do not necessarily imply asymmetric effects of shocks. Furthermore, the MS-SVAR shows that the Central Bank of Botswana responds differently to policy shocks in different regimes. This underscores the importance of regime-switching features in providing a more accurate description of the economy.

No description available.

An effective risk management program requires reliable risk measurement. Failure to assess inherited risks in mortgage-backed securities in the U.S. market contributed to the financial crisis of 2007–2008, which has prompted government regulators to pay greater attention to controlling risk in banks, investment funds, credit unions, and other financial institutions to prevent bankruptcy and financial crisis in the future. In order to calculate risk in a reliable manner, this thesis has focused on the statistical modeling of expected return and volatility. The primary aim of this study is to propose a framework, based on the probabilistic reduction approach, to reliably quantify market risk using statistical models and historical data. Particular emphasis is placed on the importance of the validity of the probabilistic assumptions in risk measurement by demonstrating how a statistically misspecified model will lead the evaluation of risk astray. The concept of market risk is explained by discussing the narrow definition of risk in a financial context and its evaluation and implications for financial management. After highlighting empirical evidence and discussing the limitations of the ARCH-GARCH-type volatility models using exchange rate and stock market data, we proposed Student's t Autoregressive models to estimate expected return and volatility to measure risk, using Value at Risk (VaR) and Expected Shortfall (ES). The misspecification testing analysis shows that our proposed models can adequately capture the chance regularities in exchange rates and stock indexes data and give a reliable estimation of regression and skedastic functions used in risk measurement. According to empirical findings, the COVID-19 pandemic in the first quarter of 2020 posed an enormous risk to global financial markets. The risk in financial markets returned to levels prior to the COVID-19 pandemic in 2021, after COVID-19 vaccine distribution started in developed countries. / Doctor of Philosophy / Reliable risk measurement is necessary for any effective risk management program. Hence, the primary purpose of this dissertation was to propose a framework to quantify market risk using statistical models and historical data, with a particular emphasis placed on checking the validity of probabilistic assumptions underlying models. After discussing the concept of market risk and its evaluation methods in financial management, we explored the empirical evidence in financial data and highlighted some limitations of other well-known modeling approaches. In order to ameliorate limitations, this study proposed Student's t Autoregressive models to estimate the conditional mean and the conditional variance of the financial variables and use them to measure risk via two popular methods: Value at Risk (VaR) and Expected Shortfall (ES). Further investigation shows that our proposed models can adequately model exchange rates and stock indexes data and give reliable estimations to use in risk measurement. We used our model to quantify risk in global financial markets in recent years. The results show that the COVID-19 pandemic posed an enormous risk to global financial markets in the first quarter of 2020. In 2021, the level of risk in financial markets returned to levels before the COVID-19 pandemic, after COVID-19 vaccine distribution started in developed countries.

O padrão espacial de ocorrência de atributos de espécies florestais, tal como a área basal das árvores, pode fornecer informações para o entendimento da estrutura da comunidade vegetal. Uma vez que fatores ambientais podem influenciar tanto o padrão espacial de ocorrência quanto os atributos das espécies em florestas nativas. Desse modo, investigar a relação entre as características ambientais e o padrão espacial de espécies florestais pode ajudar a entender a dinâmica das florestas. Especificamente, neste trabalho, o objetivo é avaliar métodos estatísticos que permitam identificar quais atributos do solo são capazes de explicar a variação da área basal de cada espécie de árvore. A área basal foi considerada como variável resposta e como covariáveis, um grande número de atributos físicos e químicos do solo, medidos em uma malha de localizações cobrindo a área de estudo. Foram revisados e utilizados os métodos de regressão linear múltipla com método de seleção stepwise, modelos aditivos generalizados e árvores de regressão. Em uma segunda fase das análises, adicionou-se um efeito espacial aos modelos, com o intuito de verificar se havia ainda padrões na variabilidade, não capturados pelos modelos. Com isso, foram considerados os modelos autoregressivo simultâneo, condicional autoregressivo e geoestatístico. Dado o grande número de atributos do solo, as análises foram também conduzidas utilizando-se as covariáveis originais, fatores identificados em uma análise fatorial prévia dos atributos de solo. A seleção de modelos com melhor ajuste foi utilizada para identificar os atributos de solo relevantes, bem como a presença e melhor descrição de padrões espaciais. A área de estudo foi a Estação Ecológica de Assis, da Unidade de Conservação do Estado de São Paulo em parcelas permanentes, dentro do projeto \"Diversidade, Dinâmica e Conservação em Florestas do Estado de São Paulo: 40 ha de parcelas permanentes\", do programa Biota da FAPESP. As análises reportadas aqui se referem à área basal das espécies Copaifera langsdorffii, Vochysia tucanorum e Xylopia aromatica. Com os atributos de solo reduzidos e consistentemente associados à área basal, a declividade, altitude, saturação por alumínio e potássio mostraram-se relevantes para duas das espécies. Resultados obtidos mostraram a presença de um padrão na variabilidade, mesmo levando-se em consideração os efeitos das covariáveis, ou seja, os atributos do solo explicam parcialmente a variabilidade da área basal, mas existe um padrão que ocorre no espaço que não é capturado por essas covariáveis. / The spatial pattern of occurrenceis of forest species and their attributes, such as the basal area of trees, can provide information for understanding the structure of the vegetable community. Considering the environmental factors can influence the spatial pattern of occurrences of species in native forests and related attributes, describing relationship between environmental characteristics and spatial pattern of forest species can be associated with the dynamics of forests. The objective of the present study is to assess different statistical methods used to identify which soil attributes are associated with the basal area of each tree selected species. The basal area was considered as the response variable and the covariates are given by a large number of physical and chemical attributes of the soil, measured at a grid of locations covering the study area. The methods considered are the multiple linear regression with stepwise model selection, generalized additive models and regression trees. Spatial effects were added to the models, in order to ascertain whether there is residual spatial patterns not captured by the covariates. Thus, simultaneous autoregressive model, autoregressive conditional and geostatistical were considered. Considering the large number of soil attributes, analysis were were conducted both ways, using the original covariates, and using factors identified in a preliminar factor analysis of the soil attributes. Model selection was used to identify the relevant attributes of soil as well as the presence and better description of spatial patterns. The study area was the Ecological Station of Assis, the Conservation Unit of the State of São Paulo in permanent plots within the \"Diversity Dynamics and Conservation Forests in the State of São Paulo: 40 ha of permanent plots\" project, under the research project FAPESP biota. The analyzes reported here refer to the basal area of the species Copaifera langsdorffii, Vochysia tucanorum and Xylopia aromatica. Results differ among the considered methods reinforcing the reccomendation of considering differing modeling strategies. Covariates consistently associated with basal area are slope, altitude and aluminum saturation, potassium, relevant to at least two of the species. Results obtained showed the presence of patterns in residual variability, even taking into account the effects of covariates. The soil characteristics only partially explain the variability of the basal area and there are spatial patterns not captured by these covariates.

Submitted by Winston Chun (winston.chun@gmail.com) on 2011-09-08T04:15:02Z No. of bitstreams: 1 Dissertacao VFINAL.pdf: 252650 bytes, checksum: 8b08b5f955a557fe1c18b78a33d10bda (MD5) / Rejected by Gisele Isaura Hannickel (gisele.hannickel@fgv.br), reason: Prezado Winston, O trabalho postado está com as folhas invertidas, deve seguir a seguinte sequencia: 1 - capa 2 - contra-capa 3 - ficha catalográfica 4 - folha de assinaturas. Em caso de dúvidas, favor acessar o caminho: http://bibliotecadigital.fgv.br/site/bkab/normalizacao Att, Gisele Hannickel Secretaria de Registro on 2011-09-08T12:44:39Z (GMT) / Submitted by Winston Chun (winston.chun@gmail.com) on 2011-09-08T13:30:24Z No. of bitstreams: 1 Dissertacao VFINAL.pdf: 209740 bytes, checksum: 9b370c58031ba9a9ecb5718b05d9ee82 (MD5) / Approved for entry into archive by Gisele Isaura Hannickel (gisele.hannickel@fgv.br) on 2011-09-08T13:43:33Z (GMT) No. of bitstreams: 1 Dissertacao VFINAL.pdf: 209740 bytes, checksum: 9b370c58031ba9a9ecb5718b05d9ee82 (MD5) / Approved for entry into archive by Gisele Isaura Hannickel (gisele.hannickel@fgv.br) on 2011-09-08T13:43:46Z (GMT) No. of bitstreams: 1 Dissertacao VFINAL.pdf: 209740 bytes, checksum: 9b370c58031ba9a9ecb5718b05d9ee82 (MD5) / Made available in DSpace on 2011-09-08T13:44:03Z (GMT). No. of bitstreams: 1 Dissertacao VFINAL.pdf: 209740 bytes, checksum: 9b370c58031ba9a9ecb5718b05d9ee82 (MD5) Previous issue date: 2011-08-08 / Esta dissertação tem com objetivo avaliar uma das implicações da hipótese de expectativas para a estrutura a termo de taxa de juros brasileira. Utilizando testes lineares tradicionais e através da reprodução de testes não lineares TAR de Enders e Granger (1998) e ESTAR Kapetanios e Shin (2003) conclui-se que a hipótese de expectativas não é totalmente válida para a ETTJ do Brasil, além disso, são encontradas evidências de não linearidade nas séries de spreads que demandam mais pesquisa sobre o assunto. / This dissertation has the aim to evaluate one of the implications of expectation hypothesis in Brazilian term structure of interests. Using traditional linear tests and through the reproduction of nonlinear Threshold Autoregressive (TAR) tests of Enders and Granger (1998) and Exponential Smooth Transition Autoregressive (ESTAR) of Kapetanios and Shin (2003) the conclusion is that expectation hypothesis is not totally valid for Brazil, besides that, some evidences of non-linearity in spreads series were found then more research is needed on the subject.

O padrão espacial de ocorrência de atributos de espécies florestais, tal como a área basal das árvores, pode fornecer informações para o entendimento da estrutura da comunidade vegetal. Uma vez que fatores ambientais podem influenciar tanto o padrão espacial de ocorrência quanto os atributos das espécies em florestas nativas. Desse modo, investigar a relação entre as características ambientais e o padrão espacial de espécies florestais pode ajudar a entender a dinâmica das florestas. Especificamente, neste trabalho, o objetivo é avaliar métodos estatísticos que permitam identificar quais atributos do solo são capazes de explicar a variação da área basal de cada espécie de árvore. A área basal foi considerada como variável resposta e como covariáveis, um grande número de atributos físicos e químicos do solo, medidos em uma malha de localizações cobrindo a área de estudo. Foram revisados e utilizados os métodos de regressão linear múltipla com método de seleção stepwise, modelos aditivos generalizados e árvores de regressão. Em uma segunda fase das análises, adicionou-se um efeito espacial aos modelos, com o intuito de verificar se havia ainda padrões na variabilidade, não capturados pelos modelos. Com isso, foram considerados os modelos autoregressivo simultâneo, condicional autoregressivo e geoestatístico. Dado o grande número de atributos do solo, as análises foram também conduzidas utilizando-se as covariáveis originais, fatores identificados em uma análise fatorial prévia dos atributos de solo. A seleção de modelos com melhor ajuste foi utilizada para identificar os atributos de solo relevantes, bem como a presença e melhor descrição de padrões espaciais. A área de estudo foi a Estação Ecológica de Assis, da Unidade de Conservação do Estado de São Paulo em parcelas permanentes, dentro do projeto \"Diversidade, Dinâmica e Conservação em Florestas do Estado de São Paulo: 40 ha de parcelas permanentes\", do programa Biota da FAPESP. As análises reportadas aqui se referem à área basal das espécies Copaifera langsdorffii, Vochysia tucanorum e Xylopia aromatica. Com os atributos de solo reduzidos e consistentemente associados à área basal, a declividade, altitude, saturação por alumínio e potássio mostraram-se relevantes para duas das espécies. Resultados obtidos mostraram a presença de um padrão na variabilidade, mesmo levando-se em consideração os efeitos das covariáveis, ou seja, os atributos do solo explicam parcialmente a variabilidade da área basal, mas existe um padrão que ocorre no espaço que não é capturado por essas covariáveis. / The spatial pattern of occurrenceis of forest species and their attributes, such as the basal area of trees, can provide information for understanding the structure of the vegetable community. Considering the environmental factors can influence the spatial pattern of occurrences of species in native forests and related attributes, describing relationship between environmental characteristics and spatial pattern of forest species can be associated with the dynamics of forests. The objective of the present study is to assess different statistical methods used to identify which soil attributes are associated with the basal area of each tree selected species. The basal area was considered as the response variable and the covariates are given by a large number of physical and chemical attributes of the soil, measured at a grid of locations covering the study area. The methods considered are the multiple linear regression with stepwise model selection, generalized additive models and regression trees. Spatial effects were added to the models, in order to ascertain whether there is residual spatial patterns not captured by the covariates. Thus, simultaneous autoregressive model, autoregressive conditional and geostatistical were considered. Considering the large number of soil attributes, analysis were were conducted both ways, using the original covariates, and using factors identified in a preliminar factor analysis of the soil attributes. Model selection was used to identify the relevant attributes of soil as well as the presence and better description of spatial patterns. The study area was the Ecological Station of Assis, the Conservation Unit of the State of São Paulo in permanent plots within the \"Diversity Dynamics and Conservation Forests in the State of São Paulo: 40 ha of permanent plots\" project, under the research project FAPESP biota. The analyzes reported here refer to the basal area of the species Copaifera langsdorffii, Vochysia tucanorum and Xylopia aromatica. Results differ among the considered methods reinforcing the reccomendation of considering differing modeling strategies. Covariates consistently associated with basal area are slope, altitude and aluminum saturation, potassium, relevant to at least two of the species. Results obtained showed the presence of patterns in residual variability, even taking into account the effects of covariates. The soil characteristics only partially explain the variability of the basal area and there are spatial patterns not captured by these covariates.