Ensaios em econometria aplicada

Este tese é composta por quatro ensaios sobre aplicações econométricas em tópicos econômicos relevantes. Os estudos versam sobre consumo de bens não-duráveis e preços de imóveis, capital humano e crescimento econômico, demanda residencial de energia elétrica e, por fim, periodicidade de variáveis fiscais de Estados e Municípios brasileiros. No primeiro artigo, "Non-Durable Consumption and Real-Estate Prices in Brazil: Panel-Data Analysis at the State Level", é investigada a relação entre variação do preço de imóveis e variação no consumo de bens não-duráveis. Os dados coletados permitem a formação de um painel com sete estados brasileiros observados entre 2008- 2012. Os resultados são obtidos a partir da estimação de uma forma reduzida obtida em Campbell e Cocco (2007) que aproxima um modelo estrutural. As estimativas para o caso brasileiro são inferiores as de Campbell e Cocco (2007), que, por sua vez, utilizaram microdados britânicos. O segundo artigo, "Uma medida alternativa de capital humano para o estudo empírico do crescimento", propõe uma forma de mensuração do estoque de capital humano que reflita diretamente preços de mercado, através do valor presente do fluxo de renda real futura. Os impactos dessa medida alternativa são avaliados a partir da estimação da função de produção tradicional dos modelos de crescimento neoclássico. Os dados compõem um painel de 25 países observados entre 1970 e 2010. Um exercício de robustez é realizado para avaliar a estabilidade dos coeficientes estimados diante de variações em variáveis exógenas do modelo. Por sua vez, o terceiro artigo "Household Electricity Demand in Brazil: a microdata approach", parte de dados da Pesquisa de Orçamento Familiar (POF) para mensurar a elasticidade preço da demanda residencial brasileira por energia elétrica. O uso de microdados permite adotar abordagens que levem em consideração a seleção amostral. Seu efeito sobre a demanda de eletricidade é relevante, uma vez que esta demanda é derivada da demanda por estoque de bens duráveis. Nesse contexto, a escolha prévia do estoque de bens duráveis (e consequentemente, a escolha pela intensidade de energia desse estoque) condiciona a demanda por eletricidade dos domicílios. Finalmente, o quarto trabalho, "Interpolação de Variáveis Fiscais Brasileiras usando Representação de Espaço de Estados" procurou sanar o problema de baixa periodicidade da divulgação de séries fiscais de Estados e Municípios brasileiros. Através de técnica de interpolação baseada no Filtro de Kalman, as séries mensais não observadas são projetadas a partir de séries bimestrais parcialmente observadas e covariáveis mensais selecionadas.

A comparison of sales response predictions from demand models applied to store-level versus panel data

In order to generate sales promotion response predictions, marketing analysts estimate demand models using either disaggregated (consumer-level) or aggregated (store-level) scanner data. Comparison of predictions from these demand models is complicated by the fact that models may accommodate different forms of consumer heterogeneity depending on the level of data aggregation. This study shows via simulation that demand models with various heterogeneity specifications do not produce more accurate sales response predictions than a homogeneous demand model applied to store-level data, with one major exception: a random coefficients model designed to capture within-store heterogeneity using store-level data produced significantly more accurate sales response predictions (as well as better fit) compared to other model specifications. An empirical application to the paper towel product category adds additional insights. This article has supplementary material online.

Weak conformance of process models with respect to data objects

Process models specify behavioral aspects by describing ordering constraints between tasks which must be accomplished to achieve envisioned goals. Tasks usually exchange information by means of data objects, i.e., by writing information to and reading information from data objects. A data object can be characterized by its states and allowed state transitions. In this paper, we propose a notion which checks conformance of a process model with respect to data objects that its tasks access. This new notion can be used to tell whether in every execution of a process model each time a task needs to access a data object in a particular state, it is ensured that the data object is in the expected state or can reach the expected state and, hence, the process model can achieve its goals.

Comparison of Interproton Distances in DNA Models with Nuclear Overhauser Enhancement Data

The conformational flexibility inherent in the polynucleotide chain plays an important role in deciding its three-dimensonal structure and enables it to undergo structural transitions in order to fulfil all its functions. Following certain stereochemical guidelines, both right and left handed double-helical models have been built in our laboratory and they are in reasonably good agreement with the fibre patterns for various polymorphous forms of DNA. Recently, nuclear magnetic resonance spectroscopy has become an important technique for studying the solution conformation and polymorphism of nucleic acids. Several workers have used 1H nuclear magnetic resonance nuclear Overhauser enhancement measurements to estimate the interproton distances for the various DNA oligomers and compared them with the interproton distances for particular models of A and Β form DNA. In some cases the solution conformation does not seem to fit either of these models. We have been studying various models for DNA with a view to exploring the full conformational space allowed for nucleic acid polymers. In this paper, the interproton distances calculated for the different stereochemically feasible models of DNA are presented and they are compared and correlated against those obtained from 1Η nuclear magnetic resonance nuclear Overhauser enhancement measurements of various nucleic acid oligomers.

Use of SARIMA models to assess data-poor fisheries: a case study with a sciaenid fishery off Portugal

Research on assessment and monitoring methods has primarily focused on fisheries with long multivariate data sets. Less research exists on methods applicable to data-poor fisheries with univariate data sets with a small sample size. In this study, we examine the capabilities of seasonal autoregressive integrated moving average (SARIMA) models to fit, forecast, and monitor the landings of such data-poor fisheries. We use a European fishery on meagre (Sciaenidae: Argyrosomus regius), where only a short time series of landings was available to model (n=60 months), as our case-study. We show that despite the limited sample size, a SARIMA model could be found that adequately fitted and forecasted the time series of meagre landings (12-month forecasts; mean error: 3.5 tons (t); annual absolute percentage error: 15.4%). We derive model-based prediction intervals and show how they can be used to detect problematic situations in the fishery. Our results indicate that over the course of one year the meagre landings remained within the prediction limits of the model and therefore indicated no need for urgent management intervention. We discuss the information that SARIMA model structure conveys on the meagre lifecycle and fishery, the methodological requirements of SARIMA forecasting of data-poor fisheries landings, and the capabilities SARIMA models present within current efforts to monitor the world’s data-poorest resources.

PANEL DATA UNIT ROOT TEST WITH FIXED TIME DIMENSION

The consideration of the limit theory in which T is fixed and N is allowed to go to infinity improves the finite-sample properties of the tests and avoids the imposition of the relative rates at which T and N go to infinity.

TESTING FOR STATIONARITY IN HETEROGENEOUS PANEL DATA IN THE CASE OF MODEL MISSPECIFICATION

This paper investigates the performance of the tests proposed by Hadri and by Hadri and Larsson for testing for stationarity in heterogeneous panel data under model misspecification. The panel tests are based on the well known KPSS test (cf. Kwiatkowski et al.) which considers two models: stationarity around a deterministic level and stationarity around a deterministic trend. There is no study, as far as we know, on the statistical properties of the test when the wrong model is used. We also consider the case of the simultaneous presence of the two types of models in a panel. We employ two asymptotics: joint asymptotic, T, N -> infinity simultaneously, and T fixed and N allowed to grow indefinitely. We use Monte Carlo experiments to investigate the effects of misspecification in sample sizes usually used in practice. The results indicate that the assumption that T is fixed rather than asymptotic leads to tests that have less size distortions, particularly for relatively small T with large N panels (micro-panels) than the tests derived under the joint asymptotics. We also find that choosing a deterministic trend when a deterministic level is true does not significantly affect the properties of the test. But, choosing a deterministic level when a deterministic trend is true leads to extreme over-rejections. Therefore, when unsure about which model has generated the data, it is suggested to use the model with a trend. We also propose a new statistic for testing for stationarity in mixed panel data where the mixture is known. The performance of this new test is very good for both cases of T asymptotic and T fixed. The statistic for T asymptotic is slightly undersized when T is very small (

Testing the Prebish-Singer hypothesis using second-generation panel data stationarity tests with a break

In this paper, we test the Prebish-Singer (PS) hypothesis, which states that real commodity prices decline in the long run, using two recent powerful panel data stationarity tests accounting for cross-sectional dependence and a structural break. We find that the hypothesis cannot be rejected for most commodities other than oil.

Synergy Between an Improved Covariate Unit Root Test and Cross-sectionally Dependent Panel Data Unit Root Tests, with two Applications

This paper proposes the use of an improved covariate unit root test which exploits the cross-sectional dependence information when the panel data null hypothesis of a unit root is rejected. More explicitly, to increase the power of the test, we suggest the utilization of more than one covariate and offer several ways to select the ‘best’ covariates from the set of potential covariates represented by the individuals in the panel. Employing our methods, we investigate the Prebish-Singer hypothesis for nine commodity prices. Our results show that this hypothesis holds for all but the price of petroleum.

Bt cotton and production risk: Panel data evidence

The farm-level success of Bt-cotton in developing countries is well documented. However, the literature has only recently begun to recognise the importance of accounting for the effects of the technology on production risk, in addition to the mean effect estimated by previous studies. The risk effects of the technology are likely very important to smallholder farmers in the developing world due to their risk-aversion. We advance the emergent literature on Bt-cotton and production risk by using panel data methods to control for possible endogeneity of Bt-adoption. We estimate two models, the first a fixed-effects version of the Just and Pope model with additive individual and time effects, and the second a variation of the model in which inputs and variety choice are allowed to affect the variance of the time effect and its correlation with the idiosyncratic error. The models are applied to panel data on smallholder cotton production in India and South Africa. Our results suggest a risk-reducing effect of Bt-cotton in India, but an inconclusive picture in South Africa.

Growth and exchange rate volatility: a panel data analysis

The aim of this article is to assess the role of real effective exchange rate volatility on long-run economic growth for a set of 82 advanced and emerging economies using a panel data set ranging from 1970 to 2009. With an accurate measure for exchange rate volatility, the results for the two-step system GMM panel growth models show that a more (less) volatile RER has significant negative (positive) impact on economic growth and the results are robust for different model specifications. In addition to that, exchange rate stability seems to be more important to foster long-run economic growth than exchange rate misalignment