Política fiscal e impactos produtivos dos gastos públicos

Esta tese é composta por três ensaios que versam sobre os efeitos macroeconômicos da Política Fiscal, especialmente sobre os principais agregados, tais como Produto, Investimento, Consumo e a Produtividade Geral da Economia. A literatura econômica e os trabalhos empíricos não são consensuais com relação à natureza dos impactos produtivos da Política Fiscal, mesmo para o caso do capital público. O objetivo dessa Tese não é buscar esse consenso, mas acrescentar à literatura novas evidências sobre os países em desenvolvimento da América Latina. O primeiro ensaio investiga as relações dinâmicas (no curto e longo prazo) entre investimento público e produto e investimento público e a Produtividade Total dos Fatores (PTF) para a Argentina, Brasil e Chile. Os resultados encontrados para os três países foram unânimes quando se refere a uma relação de longo prazo positiva entre investimento público e produto. O mesmo não se pode afirmar com respeito aos impactos de longo prazo entre investimento público e produtividade total dos fatores. O segundo ensaio aperfeiçoa a discussão do artigo anterior ao inquirir os efeitos não somente do investimento público, mas também do consumo do governo. Além disso, os impactos são avaliados sobre o PIB e seus principais componentes, tais como consumo das famílias e investimento privado. Os resultados desse capítulo sugerem que, no longo prazo, os investimentos públicos tendem a afetar positivamente o produto e o consumo das famílias. O consumo do governo afeta negativamente o produto e os investimentos privados para a maioria dos países. No entanto, esse resultado não é absoluto e depende do nível relativo do consumo do governo. No curto prazo, os resultados de uma política de estabilização ativa baseados nos pressupostos keynesianos são bastante limitados em termos de magnitude e duração ao longo do tempo. O terceiro ensaio analisa a consistência da política fiscal no Brasil, a partir de 1999, sob a perspectiva da estabilidade acroeconômica e seus efeitos de longo prazo sobre a sustentabilidade da dívida pública. Vale ressaltar que a consistência da política fiscal no médio e no longo prazos é fundamental para se vislumbrar um crescimento econômico sustentado. Os resultados indicam que, na formação de suas expectativas, o mercado observa apenas o número do superávit primário e o nível da dívida pública, desconsiderando a consistência do superávit primário, o que sugere certo grau de miopia em relação à política fiscal brasileira.

Análise do mecanismo de transmissão dos preços internacionais de commodities agrícolas sobre o comportamento da taxa de câmbio real no Brasil

This paper examined the transmission mechanism of international prices of agricultural commodities into the real exchange rate in Brazil for the period from January 2000 to February 2010. We used time series models (ARIMA Model, Transfer Model, Intervention Analysis, Johansen Cointegration Test) in determination of the short and long run elasticities. Transfer Function Model results show that changes in international prices of agricultural commodities are transmitted to the real exchange rate in Brazil in the short run, however, that transmission is less than unity, thus configuring the inelastic relationship. Johansen cointegration tests show that these variables are not co-integrated, no longer converge to the long-run equilibrium. These results are in agreement Cashim et al. (2004), which also found no long run relationship between real exchange rate and commodity prices in the case of Brazil. These results show that monetary shocks have greater weight on changes of the real exchange rate than real shocks.

Previsão de inflação utilizando modelos de séries temporais

Este trabalho compara modelos de séries temporais para a projeção de curto prazo da inflação brasileira, medida pelo Índice de Preços ao Consumidor Amplo (IPCA). Foram considerados modelos SARIMA de Box e Jenkins e modelos estruturais em espaço de estados, estimados pelo filtro de Kalman. Para a estimação dos modelos, foi utilizada a série do IPCA na base mensal, de março de 2003 a março de 2012. Os modelos SARIMA foram estimados no EVIEWS e os modelos estruturais no STAMP. Para a validação dos modelos para fora da amostra, foram consideradas as previsões 1 passo à frente para o período de abril de 2012 a março de 2013, tomando como base os principais critérios de avaliação de capacidade preditiva propostos na literatura. A conclusão do trabalho é que, embora o modelo estrutural permita, decompor a série em componentes com interpretação direta e estudá-las separadamente, além de incorporar variáveis explicativas de forma simples, o desempenho do modelo SARIMA para prever a inflação brasileira foi superior, no período e horizonte considerados. Outro importante aspecto positivo é que a implementação de um modelo SARIMA é imediata, e previsões a partir dele são obtidas de forma simples e direta.

Transfer function-noise modeling and spatial interpolation to evaluate the risk of extreme (shallow) water-table levels in the Brazilian Cerrados

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Padrões ecológicos da comunidade de ácaros em euforbiáceas de um fragmento de mata estacional semidecidual, no Estado de São Paulo

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Modelagem de séries de monitoramento de nível freático em área de afloramento do sistema aquífero guarani (sag) sob influência de diferentes usos agrícolas da terra

Pós-graduação em Agronomia (Irrigação e Drenagem) - FCA

Modelagem dos níveis freáticos do sistema aquífero Bauru (SAB) em diferentes usos da terra no município de Assis - SP

Pós-graduação em Agronomia (Irrigação e Drenagem) - FCA

Cointegration: Bayesian Significance Test

To estimate causal relationships, time series econometricians must be aware of spurious correlation, a problem first mentioned by Yule (1926). To deal with this problem, one can work either with differenced series or multivariate models: VAR (VEC or VECM) models. These models usually include at least one cointegration relation. Although the Bayesian literature on VAR/VEC is quite advanced, Bauwens et al. (1999) highlighted that "the topic of selecting the cointegrating rank has not yet given very useful and convincing results". The present article applies the Full Bayesian Significance Test (FBST), especially designed to deal with sharp hypotheses, to cointegration rank selection tests in VECM time series models. It shows the FBST implementation using both simulated and available (in the literature) data sets. As illustration, standard non informative priors are used.

A Functional-Based Distribution Diagnostic for a Linear Model with Correlated Outcomes: Technical Report

Despite the widespread popularity of linear models for correlated outcomes (e.g. linear mixed modesl and time series models), distribution diagnostic methodology remains relatively underdeveloped in this context. In this paper we present an easy-to-implement approach that lends itself to graphical displays of model fit. Our approach involves multiplying the estimated marginal residual vector by the Cholesky decomposition of the inverse of the estimated marginal variance matrix. Linear functions or the resulting "rotated" residuals are used to construct an empirical cumulative distribution function (ECDF), whose stochastic limit is characterized. We describe a resampling technique that serves as a computationally efficient parametric bootstrap for generating representatives of the stochastic limit of the ECDF. Through functionals, such representatives are used to construct global tests for the hypothesis of normal margional errors. In addition, we demonstrate that the ECDF of the predicted random effects, as described by Lange and Ryan (1989), can be formulated as a special case of our approach. Thus, our method supports both omnibus and directed tests. Our method works well in a variety of circumstances, including models having independent units of sampling (clustered data) and models for which all observations are correlated (e.g., a single time series).

Cholesky Residuals for Assessing Normal Errors in a Linear Model with Correlated Outcomes: Technical Report

Despite the widespread popularity of linear models for correlated outcomes (e.g. linear mixed models and time series models), distribution diagnostic methodology remains relatively underdeveloped in this context. In this paper we present an easy-to-implement approach that lends itself to graphical displays of model fit. Our approach involves multiplying the estimated margional residual vector by the Cholesky decomposition of the inverse of the estimated margional variance matrix. The resulting "rotated" residuals are used to construct an empirical cumulative distribution function and pointwise standard errors. The theoretical framework, including conditions and asymptotic properties, involves technical details that are motivated by Lange and Ryan (1989), Pierce (1982), and Randles (1982). Our method appears to work well in a variety of circumstances, including models having independent units of sampling (clustered data) and models for which all observations are correlated (e.g., a single time series). Our methods can produce satisfactory results even for models that do not satisfy all of the technical conditions stated in our theory.

Seasonal Analyses of Air Pollution and Mortality in 100 U.S. Cities

Time series models relating short-term changes in air pollution levels to daily mortality counts typically assume that the effects of air pollution on the log relative rate of mortality do not vary with time. However, these short-term effects might plausibly vary by season. Changes in the sources of air pollution and meteorology can result in changes in characteristics of the air pollution mixture across seasons. The authors develop Bayesian semi-parametric hierarchical models for estimating time-varying effects of pollution on mortality in multi-site time series studies. The methods are applied to the updated National Morbidity and Mortality Air Pollution Study database for the period 1987--2000, which includes data for 100 U.S. cities. At the national level, a 10 micro-gram/m3 increase in PM(10) at lag 1 is associated with a 0.15 (95% posterior interval: -0.08, 0.39),0.14 (-0.14, 0.42), 0.36 (0.11, 0.61), and 0.14 (-0.06, 0.34) percent increase in mortality for winter, spring, summer, and fall, respectively. An analysis by geographical regions finds a strong seasonal pattern in the northeast (with a peak in summer) and little seasonal variation in the southern regions of the country. These results provide useful information for understanding particle toxicity and guiding future analyses of particle constituent data.

"Ripple Effects" and Forecasting Home Prices in Los Angeles, Las Vegas, and Phoenix

We examine the time-series relationship between housing prices in Los Angeles, Las Vegas, and Phoenix. First, temporal Granger causality tests reveal that Los Angeles housing prices cause housing prices in Las Vegas (directly) and Phoenix (indirectly). In addition, Las Vegas housing prices cause housing prices in Phoenix. Los Angeles housing prices prove exogenous in a temporal sense and Phoenix housing prices do not cause prices in the other two markets. Second, we calculate out-of-sample forecasts in each market, using various vector autoregessive (VAR) and vector error-correction (VEC) models, as well as Bayesian, spatial, and causality versions of these models with various priors. Different specifications provide superior forecasts in the different cities. Finally, we consider the ability of theses time-series models to provide accurate out-of-sample predictions of turning points in housing prices that occurred in 2006:Q4. Recursive forecasts, where the sample is updated each quarter, provide reasonably good forecasts of turning points.

Regression with autocorrelated data: A study of time trend of global infant mortality rate

The infant mortality rate (IMR) is considered to be one of the most important indices of a country's well-being. Countries around the world and other health organizations like the World Health Organization are dedicating their resources, knowledge and energy to reduce the infant mortality rates. The well-known Millennium Development Goal 4 (MDG 4), whose aim is to archive a two thirds reduction of the under-five mortality rate between 1990 and 2015, is an example of the commitment. ^ In this study our goal is to model the trends of IMR between the 1950s to 2010s for selected countries. We would like to know how the IMR is changing overtime and how it differs across countries. ^ IMR data collected over time forms a time series. The repeated observations of IMR time series are not statistically independent. So in modeling the trend of IMR, it is necessary to account for these correlations. We proposed to use the generalized least squares method in general linear models setting to deal with the variance-covariance structure in our model. In order to estimate the variance-covariance matrix, we referred to the time-series models, especially the autoregressive and moving average models. Furthermore, we will compared results from general linear model with correlation structure to that from ordinary least squares method without taking into account the correlation structure to check how significantly the estimates change.^

Causality Relationships between Total Exports with Agricultural and Manufacturing GDP in Tanzania

This paper, investigates causal relationships among agriculture, manufacturing and export in Tanzania by using time series data for the period between 1970 and 2005. The empirical results show in both sectors there is Granger causality where agriculture causes both exports and manufacturing. Exports also cause both agricultural GDP and manufacturing GDP and any two variables out of three jointly cause the third one. There is also some evidence that manufacturing does not cause export and agriculture. Regarding cointegration, pairwise agricultural GDP and export are cointegrated, export and manufacture are cointegrated. Agriculture and manufacture are cointegrated but they are lag sensitive. However, three variables, manufacturing, export and agriculture both together are cointegrated showing that they share long run relation and this has important economic implications.

Use of prediction methods for patent and trademark applications in Spain

Patent and trademark offices which run according to principles of new management have an inherent need for dependable forecasting data in planning capacity and service levels. The ability of the Spanish Office of Patents and Trademarks to carry out efficient planning of its resource needs requires the use of methods which allow it to predict the changes in the number of patent and trademark applications at different time horizons. The approach for the prediction of time series of Spanish patents and trademarks applications (1979e2009) was based on the use of different techniques of time series prediction in a short-term horizon. The methods used can be grouped into two specifics areas: regression models of trends and time series models. The results of this study show that it is possible to model the series of patents and trademarks applications with different models, especially ARIMA, with satisfactory model adjustment and relatively low error.