Dissertations / Theses on the topic 'Nonlinear econometric'

This thesis considers the filtering and prediction problems of nonlinear noisy econometric systems. As a filter/predictor, the standard tool Extended Kalman Filter and new approaches Discrete Quantization Filter and Sequential Importance Resampling Filter are used. The algorithms are compared by using Monte Carlo Simulation technique. The advantages of the new algorithms over Extended Kalman Filter are shown.

Esta tese apresenta três ensaios empíricos sobre a relação entre comércio internacional e crescimento, utilizando modelos empíricos não lineares. No primeiro ensaio, os autores propõem o modelo MR-STVEC (Multiple Regime Smooth Transition VEC), para uma amostra de quatro países desenvolvidos (Estados Unidos, Canadá, Japão e Alemanha), na perspectiva de avaliar de que modo as exportações influenciam a produtividade total dos fatores (PFT). Os resultados indicam que as exportações possuem um mecanismo de reverter possíveis choques negativos de produtividade. Adicionalmente, para o Canadá e Alemanha, quando há um movimento de ascensão da produtividade, proveniente de um eventual choque positivo, as exportações também agem, mas de modo a restringi-lo. O segundo ensaio verifica a relação de causalidade entre variáveis de comércio internacional (exportações e importações) e a taxa de crescimento do produto, aqui mensurado pela produção industrial. Neste caso, a amostra é composta de vinte nações com diferentes níveis de renda. Uma abordagem empírica alternativa, denominada entropia de transferência (ET), é aplicada, com a vantagem de não assumir a priori qualquer tipo de especificação paramétrica. Os resultados mostram que o comércio internacional é um importante fator para melhor entender crescimento, em termos do conceito de redução de incertezas futura, com destaque para as exportações quando são considerados países em desenvolvimento. Entretanto, o sentido de causalidade reversa é predominante na amostra, em especial para países mais ricos. Por fim, o último ensaio segue o argumento de Hausmman et al (2007) e avalia se o grau de especialização das exportações e importações cria uma possível não linearidade entre abertura comercial e renda per capita. Em outras palavras: a composição da pauta de exportação e importação pode alterar a capacidade que a abertura comercial tem em explicar o diferencial de renda entre nações? Para verificar esta hipótese, aplica-se o modelo de painel com transição suave para 110 países, seguindo o mesmo procedimento Frankel e Romer (1999), evitando assim o problema de endogeneidade. Os resultados empíricos indicam que quando as exportações são especializadas em commodities e as importações são diversificadas, a abertura não é capaz de influenciar a renda. Por outro lado, se as exportações são mais diversificadas, independentemente do grau de especialização que as importações venham apresentar, a abertura torna-se relevante em explicar o diferencial de renda entre as nações.
The present dissertation is composed of three essays that study the relations between economic growth and international trade through nonlinear empirical models. In the first essay, the author uses Multiple Regimes Smooth Transition Vector Error-Correction Models (MR-STVEC) for a sample of developed countries (United States, Canada, Japan and Germany) in order to evaluate how exports may affect productivity. The results indicate that exports may reverse a drop of productivity. Furthermore, in particular for Canada and Germany, exports are able to restrict productivity when there is an ascent movement. The second essay examines the causality between foreign trade variables (exports and imports) and output growth, as measured by industrial production. Here, the sample is composed of twenty nations with different income levels. An alternative time series empirical approach called transfer entropy (ET) is applied; it does not impose any aprioristic parametric function. The results show that trade is an important factor for the understanding of output growth, particularly exports when we focus on some developing countries. However, the reverse causality is also observed and, in general, is preeminent. Finally, the last essay follows the arguments of Hausmman et al (2007) in order to verify if sectorial specialization of exports and imports creates nonlinearities between the degree of openness of an economy and its per capita income. In other words: the compositions of exports and imports can change the capacity that the economic degree of openness has to explain the income differentials among countries? In order to address this issue, the third essay applies a Panel Smooth Transition Model for 110 countries, following the same procedure of Frankel e Romer (1999) to avoid endogeneity problem. Results indicate that when exports are specialized in commodities and imports are diversified, openness do not influence income. Otherwise, if exports are diversified, independently of the levels of import\'s specialization, openness turns out to be relevant to explain per capita income.

Doctor of Philosophy
Department of Economics
Lance J. Bachmeier
This dissertation consists of three essays in nonlinear macroeconomic modeling and econometrics. In the first essay, we decompose oil price movements into oil demand (stock market) shocks and oil supply (oil-market) shocks, and examine the response of the stock market to these shocks. We find that when oil prices are “net-increasing”, a stock market shock that causes the S&P 500 to rise by one percentage point will cause the price of oil to rise approximately 0.2 percentage points, with a statistically significant positive effect one day after the stock market shock. On the other hand, the response of the stock market to an oil market shock is a decline of 6.8 percent when the price of oil doubles. For other days, the initial response of the oil market to a stock market shock is the same as in the net oil price increase case (by construction). We then analyze the response of monetary policy to the identified stock market and oil market shocks and find that short-term interest rates respond to the stock market shocks but not the oil market shocks. Finally, we evaluate the predictive power of the decomposed stock market and oil shocks relative to the change in the price of oil. We find statistically significant gains in both the in-sample fit and out-of-sample forecast accuracy when using the identified stock market and oil market shocks rather than the change in the price of oil. The second essay revisits the statistical specification of near-multicollinearity in the logistic regression model using the Probabilistic Reduction approach. We argue that the ceteris paribus clause invoked with near-multicollinearity is rather misleading. This assumption states that one can assess the impact of near-multicollinearity by holding the parameters of the logistic regression model constant, while examining the impact on their standard errors and t-ratios as the correlation (\rho) between the regressors increases. Using the Probabilistic Reduction approach, we derive the parameters (and related statisitics) of the logistic regression model and show that they are functions of \rho , indicating the ceteris paribus clause in the traditional account of near multicollinearity is unattainable. Monte carlo simulations in the paper confirm these findings. We also show that traditional near-multicollinearity diagnostics, such as the variance inflation factor and condition number can fail to detect near-multicollinearity. Overall, the paper finds that near-multicollinearity in the logistic model is highly variable and may not lead to the problems indicated by the traditional account. Therefore, unexpected, unreliable or unstable estimates and inferences should not be blamed on near-multicollinearity. Rather the modeler should return to economic theory or statistical respecification of their model to address these problems. The third essay examines the correlations between income inequality and economic growth using a panel of income distribution data for 3,109 counties of the U.S. We examine the non-spatial dynamic correlations between county inequality and growth using a System GMM approach, and find significant negative relationships between changes in inequality in one period and growth in the subsequent period. We show that this finding is robust across different sample sizes. We further argue that because the space-specific time-invariant variables that affect economic growth and inequality can differ significantly across counties, failure to incorporate spatial effects into a model of growth and inequality may lead to biased results.We assume that dependence among counties only arises from the disturbance process, hence the estimation of a spatial error model. Our results indicate that the bias in the parameter for inequality amounts to about 2.66 percent, while that for initial income amounts to about 21.51 percent.

During the last few years nonlinear models have been a very active area of econometric research: new models have been introduced and existing ones generalized. To a large extent, these developments have concerned models in which the conditional moments are regime-dependent. In such models, the different regimes are usually linear and the change between them is governed by an observable or unobservable variable. These specifications can be useful in situations in which it is suspected that the behaviour of the dependent variable may vary between regimes. A classical example can be found the business cycle literature where it is argued that contractions in the economy are not only more violent but also short-lived than expansions. Unemployment, which tends to rise faster during recessions than decline during booms, constitutes another example. Two of the most popular regime-dependent models are the smooth transition and the threshold model. In both models cases the transition variable is observable but the specification of the way in which the model changes from one regime to the other is different. Particularly, in the smooth transition model the change is a continuous whereas in the threshold model it is abrupt. One of the factors that has influenced the development of nonlinear models are improvements in computer technology. They have not only permitted an introduction of more complex models but have also allowed the use of computer-intensive methods in hypothesis testing. This is particularly important in nonlinear models because there these methods have proved to be practical in testing statistical hypothesis such as linearity and parameter constancy. In general, these testing situation are not trivial and their solution often requires computer-intensive methods. In particular, bootstrapping and Monte Carlo testing are now commonly used. In this thesis the smooth transition model is used in different ways. In the first chapter, a vector smooth transition model is used as a device for deriving a test for parameter constancy in stationary vector autoregressive models. In the second chapter we introduce a panel model whose parameters can change in a smooth fashion between regimes as a function of an exogenous variable. The method is used to investigate whether financial constraints affect firms' \ investment decisions. The third chapter is concern with linearity testing in smooth transition models. New tests are introduced and Monte Carlo testing techniques are shown to be useful in achieving control over the size of the test. Finally, the last chapter is devoted to the Smooth Permanent Surge model. This is a nonlinear moving average model in which a shock can have transitory or permanent effects depending on its sign and magnitude. Test for linearity and random walk hypothesis are introduced.
Diss. Stockholm : Handelshögsk., 2004

This study examines the effect of exchange rate changes on consumer prices in Nigeria by examining the magnitude and speed of exchange rate pass-through (ERPT) to consumer prices in Nigeria using quarterly time series data from 1986 to 2013. The study also examines the potential nonlinearities and asymmetries in the ERPT in Nigeria during the same period. The study used vector error correction model (VECM) and smooth transition autoregresive (STAR) model. The methodology employed, are free from some weaknesses of the previous empirical studies and contributed to the analysis of ERPT from a macroeconomic perspective. This study focuses on the macroeconomic perspective of the effect of ERPT which is more relevant for monetary policy. To design and implement an efficient monetary policy, theoritical and empirical knowledge of the ERPT to domestic consumer price is necessary. Similarly, the understanding of the level of ERPT to domestic consumer prices would offer more understanding of the international transmission of shocks and the efficiency of exchange rate policy measures on external adjustment. The study results show full and statistically significant ERPT in the long-run in Nigeria during the sample period. However, using linear model (VECM) the short-run estimate shows no significant ERPT in Nigeria. Whereas, the nonlinear STAR model shows significant ERPT even in the short-run in Nigeria. The results of the nonlinear model (STAR) show evidence of nonlinearities and asymmetries in the ERPT in Nigeria. The nonlinearities and asymmetries tend to be prevalent during periods of higher inflation and greater exchange movements when the changes in prices and exchange rates exceed certain thresholds. This study, therefore, confirms Taylor’s (2000) hypothesis that pass-through declines in low and stable inflation environment which create nonlinear ERPT. The result shows asymmetric ERPT to the direction of exchange rate change (appreciation or depreciation). The result also shows clear evidence of nonlinearity with respect to the size of the exchange rate change. This result is in line with the menu cost hypothesis where the importing firms do not transfer the exchange rate changes due to the cost of changing their menu. Therefore, the effect of the exchange rate changes on consumer price is minimal when the exchange rate changes are below the threshold level. The study also examined the output growth as a source of nonlinearities. However, the result does not show evidence of nonlinear ERPT due to the output level. The comparison of statistical test results of linear autoregressive (AR) and the nonlinear (STAR) model indicates that the nonlinear STAR model fits the data better than the linear AR model in all cases. The results of this study, therefore, show a significant impact of exchange rate changes on the domestic consumer price in both short run and long run. The asymmetric and nonlinear ERPT induced by the pricing behaviors of the importing firms also significantly influences the speed and magnitude of the ERPT to consumer prices in Nigeria.

This thesis is concerned with volatility estimation using financial panels and bias-reduction in non-linear dynamic panels in the presence of dependence. Traditional GARCH-type volatility models require large time-series for accurate estimation. This makes it impossible to analyse some interesting datasets which do not have a large enough history of observations. This study contributes to the literature by introducing the GARCH Panel model, which exploits both time-series and cross-section information, in order to make up for this lack of time-series variation. It is shown that this approach leads to gains both in- and out-of-sample, but suffers from the well-known incidental parameter issue and therefore, cannot deal with short data either. As a response, a bias-correction approach valid for a general variety of models beyond GARCH is proposed. This extends the analytical bias-reduction literature to cross-section dependence and is a theoretical contribution to the panel data literature. In the final chapter, these two contributions are combined in order to develop a new approach to volatility estimation in short panels. Simulation analysis reveals that this approach is capable of removing a substantial portion of the bias even when only 150-200 observations are available. This is in stark contrast with the standard methods which require 1,000-1,500 observations for accurate estimation. This approach is used to model monthly hedge fund volatility, which is another novel contribution, as it has hitherto been impossible to analyse hedge fund volatility, due to their typically short histories. The analysis reveals that hedge funds exhibit variation in their volatility characteristics both across and within investment strategies. Moreover, the sample distributions of fund volatilities are asymmetric, have large right tails and react to major economic events such as the recent credit crunch episode.

This thesis consists of four manuscripts in the area of nonlinear time series econometrics on topics of testing, modeling and forecasting nonlinear common features. The aim of this thesis is to develop new econometric contributions for hypothesis testing and forecasting in these area. Both stationary and nonstationary time series are concerned. A definition of common features is proposed in an appropriate way to each class. Based on the definition, a vector nonlinear time series model with common features is set up for testing for common features. The proposed models are available for forecasting as well after being well specified. The first paper addresses a testing procedure on nonstationary time series. A class of nonlinear cointegration, smooth-transition (ST) cointegration, is examined. The ST cointegration nests the previously developed linear and threshold cointegration. An Ftypetest for examining the ST cointegration is derived when stationary transition variables are imposed rather than nonstationary variables. Later ones drive the test standard, while the former ones make the test nonstandard. This has important implications for empirical work. It is crucial to distinguish between the cases with stationary and nonstationary transition variables so that the correct test can be used. The second and the fourth papers develop testing approaches for stationary time series. In particular, the vector ST autoregressive (VSTAR) model is extended to allow for common nonlinear features (CNFs). These two papers propose a modeling procedure and derive tests for the presence of CNFs. Including model specification using the testing contributions above, the third paper considers forecasting with vector nonlinear time series models and extends the procedures available for univariate nonlinear models. The VSTAR model with CNFs and the ST cointegration model in the previous papers are exemplified in detail,and thereafter illustrated within two corresponding macroeconomic data sets.

There seems to be a common understanding nowadays that the economy is nonlinear. Economic theory suggests features that can not be incorporated into linear frameworks, and over the decades a solid body of empirical evidence of nonlinearities in economic time series has been gathered. This thesis consists of four essays that have to do with various forms of nonlinear statistical inference. In the first chapter the problem of determining the number regimes in a threshold autoregressive (TAR) model is considered. Typically, the number of regimes (or thresholds) is assumed unknown and has to be determined from the data. The solution provided in the chapter first uses the smooth transition autoregressive (STAR) model with a fixed and rapid transition to approximate the TAR model. The number of thresholds is then determined using sequential misspecification tests developed for the STAR model.  The main characteristic of the proposed method is that only standard statistical inference is used, as opposed to non-standard inference or computation intensive bootstrap-based methods. In the second chapter a similar idea is employed and the structural break model is approximated with a smoothly time-varying autoregressive model. By making the smooth changes in parameters rapid, the model is able to closely approximate the corresponding model with breaks in the parameter structure. This approximation makes the misspecification tests developed for the STR modelling framework available and they can be used for sequentially determining the number of breaks. Again, the method is computationally simple as all tests rely on standard statistical inference. There exists literature suggesting that business cycle fluctuations affect the pattern of seasonality in macroeconomic series. A question asked in the third chapter is whether other factors such as changes in institutions or technological change may have this effect as well. The time-varying smooth transition autoregressive (TV- STAR) models that can incorporate both types of change are used to model the (possible) changes in seasonal patterns and shed light on the hypothesis that institutional and technological changes (proxied by time) may have a stronger effect on seasonal patterns than business cycle. The TV-STAR testing framework is applied to nine quarterly industrial production series from the G7 countries, Finland and Sweden. These series display strong seasonal patterns and also contain the business cycle fluctuations. The empirical results of the chapter suggest that seasonal patterns in these series have been changing over time and, furthermore, that the business cycle fluctuations do not seem to be the main cause for this change. The last chapter of the thesis considers the possibility of testing for Granger causality in bivariate nonlinear systems when the exact form of the nonlinear relationship between variables is not known. The idea is to linearize the testing problem by approximating the nonlinear system by its Taylor expansion. The expansion is linear in parameters and one gets round the difficulty caused by the unknown functional form of the relationship under investigation.

Diss. Stockholm : Handelshögskolan, 2004

A abordagem de regressão MIDAS (Mixed Data Sampling), proposta por Ghysels et al. (2004), permite relacionar diretamente variáveis em freqüências distintas. Esta característica é particularmente atraente quando se deseja utilizar os dados nas freqüências em que são disponibilizados, bem como quando o objetivo é calcular previsões multi-períodos à frente. Nesta tese, utiliza-se a abordagem de regressão MIDAS em três ensaios em que são realizadas aplicações empíricas nas áreas de finanças e macroeconomia. Os três ensaios são de caráter comparativo. Com aplicações em diferentes contextos de previsão, objetiva-se contribuir fornecendo evidências empíricas comparativas. No primeiro ensaio, são explorados resultados comparativos no contexto de previsão de volatilidade multi-períodos. Compara-se a abordagem MIDAS com dois métodos amplamente utilizados no cálculo de previsões multi-períodos à frente: as abordagens direta e iterada. Seus desempenhos relativos são investigados em um estudo de Monte Carlo e em um estudo empírico em que são computadas previsões de volatilidade para horizontes de até 60 dias à frente. Os resultados do estudo de Monte Carlo indicam que a abordagem MIDAS fornece as melhores previsões para os horizontes iguais ou superiores a 15 dias. Em contraste, as previsões geradas a partir da abordagem iterada são superiores nos horizontes de 5 e 10 dias à frente. No estudo empírico, utilizando-se retornos diários dos índices S&P 500 e NASDAQ, os resultados não são tão conclusivos, mas sugerem um melhor desempenho para a abordagem iterada. Todas as análises são fora da amostra. No segundo ensaio, são comparados diversos modelos de previsão de volatilidade multi-períodos, especificamente das famílias MIDAS e HAR. As comparações são realizadas em termos da acurácia das previsões de volatilidade fora da amostra. No segundo ensaio, são comparados diversos modelos de previsão de volatilidade multi-períodos, especificamente das famílias MIDAS e HAR. As comparações são realizadas em termos da acurácia das previsões de volatilidade fora da amostra. Combinações das previsões dos referidos modelos também são consideradas. São utilizados retornos intradiários do IBOVESPA no cálculo de medidas de volatilidade, tais como variância realizada, variação potente realizada e variação bipotente realizada, sendo estas medidas usadas como regressores em ambos os modelos. Adicionalmente, utiliza-se um procedimento não paramétrico na estimação das medidas de variabilidade dos componentes contínuo e de saltos do processo de variação quadrática. Estas medidas são utilizadas como regressores separados em especificações MIDAS e HAR. Quanto às evidências empíricas, os resultados em termos de erro quadrático médio sugerem que regressores baseados em medidas de volatilidade robustas a saltos (i.e., variação bipotente realizada e variação potente realizada) são melhores em prever volatilidade futura. Entretanto, observa-se que, em geral, as previsões baseadas nestes regressores não são estatisticamente diferentes daquelas baseadas na variância realizada (o regressor benchmark). Além disso, observa-se que, de modo geral, o desempenho relativo das três abordagens de previsão (i.e., MIDAS, HAR e combinação de previsões) é estatisticamente equivalente. No terceiro ensaio, busca-se comparar os modelos MS-MIDAS (Markov-Switching MIDAS) e STMIDAS (Smooth Transition MIDAS) em termos de acurácia preditiva. Para tanto, realiza-se um exercício de previsão em tempo real em que são geradas previsões fora da amostra para o crescimento do PIB trimestral dos Estados Unidos com o uso de indicadores financeiros mensais. Neste exercício, também são considerados modelos lineares MIDAS e outros modelos de previsão (lineares e não-lineares) que incluem informação dos indicadores (via agregação temporal das observações mensais) para fins comparativos de desempenho preditivo. A partir dos resultados do estudo empírico, observa-se que, de modo geral, os modelos MS-MIDAS fornecem previsões mais acuradas que os modelos STMIDAS.
The Mixed Data Sampling (MIDAS) regression approach, proposed by Ghysels et al. (2004), allows us to directly relate variables at different frequencies. This characteristic is particularly attractive when one wishes to use the data at their original sampling frequencies, as well as when the objective is to calculate multi-period-ahead forecasts. In this thesis, we use the MIDAS regression approach in three papers in which we perform empirical applications in the areas of finance and macroeconomics. All papers are comparative studies. With applications in different forecasting contexts, we aim at contributing with empirical comparative evidence. In the first paper, we explore comparative results in the context of multi-period volatility forecasting. We compare the MIDAS approach with two widely used methods of producing multi-period forecasts: the direct and the iterated approaches. Their relative performances are investigated in a Monte Carlo study and in an empirical study in which we forecast volatility at horizons up to 60 days ahead. The results of the Monte Carlo study indicate that the MIDAS forecasts are the best ones at horizons of 15 days ahead and longer. In contrast, the iterated forecasts are superior for shorter horizons of 5 and 10 days ahead. In the empirical study, using daily returns of the S&P 500 and NASDAQ indexes, the results are not so conclusive, but suggest a better performance for the iterated forecasts. All analyses are out-of-sample. In the second paper, we compare several multi-period volatility forecasting models, specifically from MIDAS and HAR families. We perform our comparisons in terms of out-of-sample volatility forecasting accuracy. We also consider combinations of the models forecasts. Using intra-daily returns of the IBOVESPA, we calculate volatility measures such as realized variance, realized power variation, and realized bipower variation to be used as regressors in both models. Further, we use a nonparametric procedure for separately measuring the continuous sample path variation and the discontinuous jump part of the quadratic variation process. Thus, MIDAS and HAR specifications with the continuous sample path and jump variability measures as separate regressors are estimated. Our results in terms of mean squared error suggest that regressors involving volatility measures which are robust to jumps (i.e., realized bipower variation and realized power variation) are better at forecasting future volatility. However, we find that, in general, the forecasts based on these regressors are not statistically different from those based on realized variance (the benchmark regressor). Moreover, we find that, in general, the relative forecasting performances of the three approaches (i.e., MIDAS, HAR and forecast combinations) are statistically equivalent. In the third paper, we compare the Markov-Switching MIDAS (MS-MIDAS) and the Smooth Transition MIDAS (STMIDAS) models in terms of forecast accuracy. We perform a real time forecasting exercise in which out-of-sample forecasts for the quarterly U.S. output growth are generated using monthly financial indicators. In this exercise, we also consider linear MIDAS models, and other forecasting models (linear and nonlinear) that include information on the indicators (via temporal aggregation of the monthly observations) for comparative purposes. From the results of the empirical study, we observe that, in general, the MS-MIDAS models provide more accurate forecasts than do the STMIDAS models.

Nonlinear two-stage residual inclusion (2SRI) estimators have become increasingly favored over traditional linear two-stage least squares (2SLS) methods for instrumental variables analysis of empirical models with inherently nonlinear dependent variables. Rising adoption of nonlinear 2SRI is largely attributable to simulation evidence showing that nonlinear 2SRI generates consistent estimates of population average treatment effects in nonlinear models, while 2SLS and nonlinear 2SPS do not. However, while it is believed that consistency of 2SRI for population average treatment effects is a general result, current evidence is limited to simulations performed under unique and restrictive settings with regards to treatment effect heterogeneity and conditions underlying treatment choices. This research contributes by describing existing simulation evidence and investigating the ability to generate absolute estimates of population average treatment effects (ATE) and local average treatment effects (LATE) using common IV estimators using Monte Carlo simulation methods across 10 alternative scenarios of treatment effect heterogeneity and sorting-on-the-gain. Additionally, estimates for the effect of ACE/ARBs on 1-year survival for Medicare beneficiaries with acute myocardial infarction are generated and compared across alternative linear and nonlinear IV estimators. Simulation results show that, while 2SLS generates unbiased and consistent estimates of LATE across all scenarios, nonlinear 2SRI generates unbiased estimates of ATE only under very restrictive settings. If marginal patients are unique in terms of treatment effectiveness, then nonlinear 2SRI cannot be expected to generate unbiased or consistent estimates of ATE unless all factors related to treatment effect heterogeneity are fully measured.

The thesis contains the four chapters: Testing parameter constancy in unit root autoregressive models against continuous change; Dickey-Fuller type of tests against nonlinear dynamic models; Inference for unit roots in a panel smooth transition autoregressive model where the time dimension is fixed; Testing unit roots in nonlinear dynamic heterogeneous panels. In Chapter  1 we derive tests for parameter constancy when the data generating process is non-stationary against the hypothesis that the parameters of the model change smoothly over time. To obtain the asymptotic distributions of the tests we generalize many theoretical results, as well as new are introduced, in the area of unit roots . The results are derived under the assumption that the error term is a strong mixing. Small sample properties of the tests are investigated, and in particular, the power performances are satisfactory. In Chapter 2 we introduce several test statistics of testing the null hypotheses of a random walk (with or without drift) against models that accommodate a smooth nonlinear shift in the level, the dynamic structure, and the trend. We derive analytical limiting distributions for all tests. Finite sample properties are examined. The performance of the tests is compared to that of the classical unit root tests by Dickey-Fuller and Phillips and Perron, and is found to be superior in terms of power. In Chapter 3 we derive a unit root test against a Panel Logistic Smooth Transition Autoregressive (PLSTAR). The analysis is concentrated on the case where the time dimension is fixed and the cross section dimension tends to infinity. Under the null hypothesis of a unit root, we show that the LSDV estimator of the autoregressive parameter in the linear component of the model is inconsistent due to the inclusion of fixed effects. The test statistic, adjusted for the inconsistency, has an asymptotic normal distribution whose first two moments are calculated analytically. To complete the analysis, finite sample properties of the test are examined. We highlight scenarios under which the traditional panel unit root tests by Harris and Tzavalis have inferior or reasonable power compared to our test. In Chapter 4 we present a unit root test against a non-linear dynamic heterogeneous panel with each country modelled as an LSTAR model. All parameters are viewed as country specific. We allow for serially correlated residuals over time and heterogeneous variance among countries. The test is derived under three special cases: (i) the number of countries and observations over time are fixed, (ii) observations over time are fixed and the number of countries tend to infinity, and (iii) first letting the number of observations over time tend to infinity and thereafter the number of countries. Small sample properties of the test  show modest size distortions and satisfactory power being superior to the Im, Pesaran and Shin t-type of test. We also show clear improvements in power compared to a univariate unit root test allowing for non-linearities under the alternative hypothesis.
Diss. Stockholm : Handelshögskolan, 2004

Depuis une dizaine d'années, une part croissante de fonds de pension rencontrent des difficultés financières. Cette détérioration a soulevé des questions sur la gestion de ces institutions et sur l'efficacité du cadre réglementaires. Cette thèse a pour objet d'analyser les comportements financiers et la gestion des risques opérés par les fonds de pension à prestation définies et les institutions assimilées. En premier lieu, nous relions les choix d'investissement à la question du contrôle managériale. Notre analyse suggère que la bonne gestion des fonds de pension nécessite un partage optimal des droits de contrôle entre les participants du plan et l'entreprise sponsor. Nous montrons alors comment cette répartition affecte les décisions d'investissement. Notre seconde analyse étudie l'impact des fluctuations financières sur la gestion des fonds de pension. Nos résultats suggèrent que le cadre réglementaire actuel conduit à de larges effets pro-cycliques, en particulier sur les exigences de capital et les décisions d'investissement. Finalement, nous analysons comment les changement structurels de la mortalité affectent le risque et les politiques de risque des fonds de pension
Since ten years, an increasing proportion of pension funds faces to severe financial difficulties, addressing some questions about the management of these institutions and the effectiveness of the regulatory framework. This thesis aims to analyze the investment decisions and financial risk management made by the pension fund defined benefit and assimilated institutions, in order to address some advances for the regulation purpose. First, we address the question of the pension funds management by analyzing the implications of the managerial control problem. Our analysis suggests that the efficient management may require an optimal splitting of control rights between plan participants and the sponsoring company. We then show how this splitting of right controls can affects investment decisions in pension funds. Second, we analyze the implications of financial cycles for pension fund management. Our results suggest that the regulatory framework produces large pro-cyclical, including regime-dependent capital requirement and regime-dependent investment decisions. Finally, we analyze how the structural change in mortality affect the risk and the risk management of pension funds

Depuis plusieurs décennies, les prix des énergies sont sujets à une volatilité croissante pesant considérablement sur l’ensemble de l’économie. Comparée aux prix des autres matières premières (comme, par exemple, les métaux précieux, ou encore les produits agricoles), l’évolution des produits énergétiques est apparue exceptionnellement incertaine, tant à long terme qu’à court terme. Dans un contexte économique global, ce phénomène acquiert toute son importance tant les dommages sur l’économie réelle d’une forte variation des prix des matières premières peuvent être conséquents. Cette thèse s’intéresse donc aux causes profondes expliquant ces fluctuations. Plus spécifiquement, en unissant les différents champs de l’économie de l’énergie, de l’économétrie, de la finance et de la psychologie, elle s’attache à comprendre le phénomène de financiarisation des commodités et les relations étroites entre marchés financiers et marchés des matières premières. Cette réflexion s’articule en trois thèmes : d’une part la relation entre les prix des différentes énergies et leurs propriétés financières est analysée, d’autre part les aspects émotionnels et comportementaux des marchés sont étudiés, enfin les liens directs entre marchés boursiers et marchés de commodités sont abordés
Since decades, energy prices are subject to increasing volatility affecting the whole economy. Compared to other commodity prices (for example precious metals and agro-industrial), energy price dynamics appear to be extremely uncertain both at short and long run. In a global economic context, this phenomenon is very important since intense variations of commodity prices can be tragic to real economy. This thesis focuses on the true nature of these movements. More formally, we investigate the commodity markets’ financialization, as well as the relationships between commodity and stock markets by unifying the fields of energy economics, econometrics, finance and psychology. This analysis is based on three themes: first energy prices relationships and their financial properties are analyzed, and then the behavioral and emotional specification of energy markets are studied, finally comovements between stock and commodity markets’ volatility are considered

This dissertation consists of 3 essays In the first essay, A Simple Variable Selection Technique for Nonlinear Models, written in cooperation with Timo Teräsvirta and Rolf Tschernig, I propose a variable selection method based on a polynomial expansion of the unknown regression function and an appropriate model selection criterion. The hypothesis of linearity is tested by a Lagrange multiplier test based on this polynomial expansion. If rejected, a kth order general polynomial is used as a base for estimating all submodels by ordinary least squares. The combination of regressors leading to the lowest value of the model selection criterion is selected.  The second essay, Modelling and Forecasting Economic Time Series with Single Hidden-layer Feedforward Autoregressive Artificial Neural Networks, proposes an unified framework for artificial neural network modelling. Linearity is tested and the selection of regressors performed by the methodology developed in essay I. The number of hidden units is detected by a procedure based on a sequence of Lagrange multiplier (LM) tests. Serial correlation of errors and parameter constancy are checked by LM tests as well. A Monte-Carlo study, the two classical series of the lynx and the sunspots, and an application on the monthly S&P 500 index return series are used to demonstrate the performance of the overall procedure. In the third essay, Forecasting with Artificial Neural Network Models (in cooperation with Marcelo Medeiros), the methodology developed in essay II, the most popular methods for artificial neural network estimation, and the linear autoregressive model are compared by forecasting performance on 30 time series from different subject areas. Early stopping, pruning, information criterion pruning, cross-validation pruning, weight decay, and Bayesian regularization are considered. The findings are that 1) the linear models very often outperform the neural network ones and 2) the modelling approach to neural networks developed in this thesis stands up well with in comparison when compared to the other neural network modelling methods considered here.

Diss. Stockholm : Handelshögskolan, 2002. Spikblad saknas

Least cost formulations for surimi seafood were studied by linear programming (LP) and nonlinear programming (NLP). The effects of water and starches on functional properties of Alaska pollock and Pacific whiting surimi gels were investigated. Six starches (modified potato starch, potato starch, modified wheat starch, wheat starch, modified waxy corn starch, and corn starch) and their mixtures were used as ingredients. Mixture and extreme vertices design were used as experimental designs. Canonical models were applied to the optimization techniques. Blending different kinds of surimi showed linear trends for each functional property, so that LP was successfully employed to optimize surimi lots. Strong interactions were found between surimi and starch or in starch mixtures. Two optimum solutions, obtained from LP and NLP, were compared in this study. Corn starch and modified waxy corn starch greatly improved the functional properties.
Graduation date: 1997

Optimization programming techniques were applied to develop the least cost formulations for Pacific whiting surimi-based seafood (PWSBS). To develop the quality constraint functions, texture and color of whiting surimi gels were determined by torsion test and colorimeter, respectively. Whiting surimi gels were produced by heating at 90��C for 15 min. with 2% NaCl, five final moisture contents (74, 76, 78, 80, 82%), and various combinations of beef plasma protein (0-2%), potato starch (0-8%), and two whey protein concentrates (0-8%). Due to the non-linear constraint functions describing texture and color, a non-linear programming search technique was required to solve the least cost model for PWSBS. Results for target quality constraints are reported in this study and show that whey protein concentrate increases the texture properties and can remain economically competitive with other ingredients which similarly influence functionality in PWSBS. Water holding capacity indicated by thermal transition was also studied as a measure of gel quality. The water evaporization process was quantified using differential scanning calorimetry (DSC) for surimi gels with added potato starch or whey protein concentrate. Pacific whiting surimi gels were produced by heating in a sealed DSC pan from 30 to 90��C at a rate of 5��C/min.; gelled samples were then re-heated from 30 to 180��C at 2��C/min. in an open pan using an equivalent water mass as a reference. The DSC thermogram showed one exothermic peak followed by one endothermic peak, the former indicating a relative energy flow from the protein gels due to the delayed water evaporation. DSC parameters derived in this study showed good correlation with the texture properties of protein gels. The addition of whey protein concentrate and the increase of heating rate increased the water holding capacity of whiting surimi gels.
Graduation date: 1996

Dissertação de doutoramento, Economia, Unidade de Ciências Económicas e Empresariais, Universidade do Algarve, 1997
The purpose of this work is to investigate nonnested tests for competing univariate dynamic linear models with autoregressive disturbances (of order p), where the motivation for Instrumental Variable estimation is mainly due to the recognised presence of current endogenous explanatory variables, either in one or in both models.