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For an equity-linked insurance, the death benefit is linked to the performance of the company’s investment portfolio. Hence, both mortality risk and equity return shall be considered for pricing such insurance. Several studies have found some dependence between mortality improvement and economy growth. In this thesis, we showed that American mortality rate and Dow Jones Industrial Average (DJIA) index price are negatively dependent by using several copulas to define the joint distribution. Then, we used these copulas to forecast mortality rates and index prices, and calculated the payoffs of a 10-year term equity-linked insurance. We showed that the predicted insurance payoffs will be smaller if dependence between mortality and index price is taken into account. / October 2016

We used neural-network based modelling to generalize the linear econometric return models and compare their out-of-sample predictive ability in terms of different performance measures under three density specifications. As error measures we used the likelihood values on the test sets as well as standard volatility measures. The empirical analysis was based on return series of stock indices from different financial markets. The results indicate that for all markets there was found no improvement in the forecast by non-linear models over linear ones, while nongaussian models significantly dominate the gaussian models with respect to most performance measures. The likelihood performance measure mostly favours the linear model with Student-t distribution, but the significance of its superiority differs between the markets. (author's abstract) / Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

We provide a comprehensive study of out-of-sample forecasts for the EUR/USD exchange rate based on multivariate macroeconomic models and forecast combinations. We use profit maximization measures based on directional accuracy and trading strategies in addition to standard loss minimization measures. When comparing predictive accuracy and profit measures, data snooping bias free tests are used. The results indicate that forecast combinations, in particular those based on principal components of forecasts, help to improve over benchmark trading strategies, although the excess return per unit of deviation is limited.

Recently, construction cost variations have been larger and less predictable. These variations are apparent in trends of indices such as Engineering News Record (ENR) Construction Cost Index (CCI) and National Highway Construction Cost Index (NHCCI). These variations are problematic for cost estimation, bid preparation and investment planning. Inaccurate cost estimation can result in bid loss or profit loss for contractors and hidden price contingencies, delayed or cancelled projects, inconsistency in budgets and unsteady flow of projects for owner organizations. Cost variation has become a major concern in all industry sectors, such as infrastructure, heavy industrial, light industrial, and building. The major problem is that construction cost is subject to significant variations that are difficult to forecast. The objectives of this dissertation are to identify the leading indicators of CCI and NHCCI from existing macroeconomic, energy and construction market variables and create appropriate models to use the information in past values of CCI and NHCCI and their leading indicators in order to forecast CCI and NHCCI more accurately than existing CCI and NHCCI forecasting models. A statistical approach based on multivariate time series analysis is used as the main research approach. The first step is to identify leading indicators of construction cost variations. A pool of 16 candidate (potential) leading indicators is initially selected based on a comprehensive literature review about construction cost variations. Then, the leading indicators of CCI are identified from the pool of candidate leading indicators using empirical tests including correlation tests, unit root tests, and Granger causality tests. The identified leading indicators represent the macroeconomic and construction market context in which the construction cost is changing. Based on the results of statistical tests, several multivariate time series models are created and compared with existing models for forecasting CCI. These models take advantage of contextual information about macroeconomic condition, energy price and construction market for forecasting CCI accurately. These multivariate time series models are rigorously diagnosed using statistical tests including Breusch-Godfrey serial correlation Lagrange multiplier tests and Autoregressive conditional heteroskedasticity (ARCH) tests. They are also compared with each other and other existing models. Comparison is based on two typical error measures: out-of-sample mean absolute prediction error and out-of-sample mean squared error. Based on the unit root tests and Granger causality tests, consumer price index, crude oil price, producer price index, housing starts and building permits are selected as leading indicators of CCI. In other words, past values of these variables contain information that is useful for forecasting CCI. Based on the results of cointegration tests, Vector Error Correction (VEC) models are created as proper multivariate time series models to forecast CCI. Our results show that the multivariate time series model including CCI and crude oil price pass diagnostic tests successfully. It is also more accurate than existing models for forecasting CCI in terms of out-of-sample mean absolute prediction error and out-of-sample mean square error. The predictability of the multivariate time series modeling for forecasting CCI is also evaluated using stochastically simulated data (Simulated CCI and crude oil price). First, 50 paths of crude oil price are created using Geometric Brownian Motion (GBM). Then, 50 paths of CCI are created using Gaussian Process that is considering the relationship between CCI and crude oil price over time. Finally, 50 multivariate and univariate time series models are created using the simulated data and the predictability of univariate and multivariate time series models are compared. The results show that the multivariate modeling is more accurate than univariate modeling for forecasting simulated CCI. The sensitivity of the models to inputs is also examined by adding errors to the simulated data and conducting sensitivity analysis. The proposed approach is also implemented for identifying the leading indicators of NHCCI from the pool of candidate leading indicators and creating appropriate multivariate forecasting models that use the information in past values of NHCCI and its leading indicators. Based on the unit root tests and Granger causality tests, crude oil price and average hourly earnings in the construction industry are selected as leading indicators of NHCCI. In other words, past values of these variables contain information that is useful for forecasting NHCCI. Based on the results of cointegration tests, Vector Error Correction (VEC) models are created as the proper multivariate time series models to forecast NHCCI. The results show that the VEC model including NHCCI and crude oil price, and the VEC model including NHCCI, crude oil price, and average hourly earnings pass diagnostic tests. These VEC models are also more accurate than the univariate models for forecasting NHCCI in terms of out-of-sample prediction error and out-of-sample mean square error. The findings of this dissertation contribute to the body of knowledge in construction cost forecasting by rigorous identification of the leading indicators of construction cost variations and creation of multivariate time series models that are more accurate than the existing models for forecasting construction cost variations. It is expected that proposed forecasting models enhance the theory and practice of construction cost forecasting and help cost engineers and capital planners prepare more accurate bids, cost estimates and budgets for capital projects.

Este trabalho a um estudo a respeito da aplicação de Redes Neurais Artificiais (RNAs), mais especificamente do modelo perceptron multi-camadas com aprendizado por retro-propagação de erros, a previsão de valores futuros de Series Temporais. 0 estudo foi realizado através da realização de previsões a partir de uma determinada arquitetura de rede neural, a qual é construída com base na analise estatística da serie, para três series reais. A primeira representa o Índice mensal de passageiros das linhas aéreas americanas entre janeiro de 1960 e dezembro de 1971, a segunda corresponde ao índice pluviométrico anual da cidade de Fortaleza no estado do Ceara entre 1849 e 1984, e a terceira trata do índice mensal de produção industrial do estado do Rio Grande do Sul entre janeiro de 1981 e julho de 1993. As duas primeiras series são exemplos clássicos utilizados no estudo dos modelos estatísticos aplicados a previsão de Series Temporais. Os resultados obtidos com as RNAs foram comparados aos progn6sticos realizados pelo método economêtrico que apresenta os melhores resultados para o problema da previsão de Series Temporais: o método da decomposição da serie em suas componentes básicas não-observáveis (tendência, sazonalidade, ciclo e irregular). Tais resultados mostraram que as RNAs podem apresentar excelentes níveis de precisão em seus prognósticos, indicando sua adaptação ao problema da previsão de valores futuros de Séries Temporais. / This work presents a study of the prediction power of Artificial Neural Networks (ANN) in comparison with prediction capability of traditional Time Series models, more specifically the Unobservable Components Models (UCM). The data used to perform the study was the monthly american airlines passengers, the annual rainfall in Fortaleza, Brazil and the monthly gross industrial output for the state of Rio Grande do Sul, Brazil. The results show that Artificial Neural Networks can outperform the forecasts of Unobservable Components Models.

This PhD thesis focuses on using time series models for counts in modelling and forecasting a special type of count series called intermittent series. An intermittent series is a series of non-negative integer values with some zero values. Such series occur in many areas including inventory control of spare parts. Various methods have been developed for intermittent demand forecasting with Croston’s method being the most widely used. Some studies focus on finding a model underlying Croston’s method. With none of these studies being successful in demonstrating an underlying model for which Croston’s method is optimal, the focus should now shift towards stationary models for intermittent demand forecasting. This thesis explores the application of a class of models for count data called the Integer Autoregressive Moving Average (INARMA) models. INARMA models have had applications in different areas such as medical science and economics, but this is the first attempt to use such a model-based method to forecast intermittent demand. In this PhD research, we first fill some gaps in the INARMA literature by finding the unconditional variance and the autocorrelation function of the general INARMA(p,q) model. The conditional expected value of the aggregated process over lead time is also obtained to be used as a lead time forecast. The accuracy of h-step-ahead and lead time INARMA forecasts are then compared to those obtained by benchmark methods of Croston, Syntetos-Boylan Approximation (SBA) and Shale-Boylan-Johnston (SBJ). The results of the simulation suggest that in the presence of a high autocorrelation in data, INARMA yields much more accurate one-step ahead forecasts than benchmark methods. The degree of improvement increases for longer data histories. It has been shown that instead of identification of the autoregressive and moving average order of the INARMA model, the most general model among the possible models can be used for forecasting. This is especially useful for short history and high autocorrelation in data. The findings of the thesis have been tested on two real data sets: (i) Royal Air Force (RAF) demand history of 16,000 SKUs and (ii) 3,000 series of intermittent demand from the automotive industry. The results show that for sparse data with long history, there is a substantial improvement in using INARMA over the benchmarks in terms of Mean Square Error (MSE) and Mean Absolute Scaled Error (MASE) for the one-step ahead forecasts. However, for series with short history the improvement is narrower. The improvement is greater for h-step ahead forecasts. The results also confirm the superiority of INARMA over the benchmark methods for lead time forecasts.

I study the role of heterogeneity and idiosyncratic risk in Macroeconomics, and their implications on problems of income taxation. In the first chapter, I study the effects of redistributive taxation in an incomplete market economy with heterogeneous agents and idiosyncratic risk. I focus on the role of distortions in labor supply decisions and the interplay of heterogeneity and uninsurable idiosyncratic shocks, conducting the first general equilibrium analysis of a Negative Income Tax (NIT). I show that a NIT is a serious candidate to replace the current income tax in the United States. I find that the optimal NIT has a marginal tax rate of 28% and a transfer of 10% of per capita GDP, roughly $4600. The welfare gains of replacing the current US income tax with a NIT are equivalent to a 6.3% increase in annual consumption in every state of the world. Low-ability agents, in the bottom quintile of the productivity distribution, benefit the most, while high-ability agents are worse off. A consequence of the reform is that the composition of the labor force changes, with high-productivity agents working more, in relative terms, than low-productivity agents. Finally, I find that the riskier the economy, the higher the welfare gains of the NIT as a provider of public insurance. In the second chapter, I study labor income dynamics over the life cycle and introduce a novel methodology that can detect the presence of patterns in the idiosyncratic earnings shocks and recognize economic forces in action. Using a sample from the Panel Study of Income Dynamics (PSID), I estimate a Bayesian Logistic Smoothed Transition Autoregressive model of order 1 (LSTAR(1)) with a rich level of heterogeneity in the innovations. I find that there is a life-cycle pattern in the earning shocks: before the age 29, young workers experience shocks with higher variance and a positive probability of lower persistence than older workers. A comparison with conventional models shows that an incorrect model specification introduces bias in the estimates. The proposed model can be easily approximated with a discrete Markov process. This means that this model can be used by macroeconomists to calibrate income processes.

Abstract This thesis considers some linear and nonlinear time series models. In the linear case, the analysis of a large number of short time series generated by a first order autoregressive type model is considered. The conditional and exact maximum likelihood procedures are developed to estimate parameters. Simulation results are presented and compare the bias and the mean square errors of the parameter estimates. In Chapter 3, five important nonlinear models are considered and their time series properties are discussed. The estimating function approach for nonlinear models is developed in detail in Chapter 4 and examples are added to illustrate the theory. A simulation study is carried out to examine the finite sample behavior of these proposed estimates based on the estimating functions.

In the past, a lot of studies about the comparison of exchange rate forecasting models have been carried out. Most of these studies have a similar result which is the random walk model has the best forecasting performance. In this thesis, I want to find a model to beat the random walk model in forecasting the exchange rate. In my study, the vector autoregressive model (VAR), restricted vector autoregressive model (RVAR), vector error correction model (VEC), Bayesian vector autoregressive model are employed in the analysis. These multivariable time series models are compared with the random walk model by evaluating the forecasting accuracy of the exchange rate for three North European countries both in short-term and long-term. For short-term, it can be concluded that the random walk model has the best forecasting accuracy. However, for long-term, the random walk model is beaten. The equal accuracy test proves this phenomenon really exists.