<p>In recent years, two estimators have been proposed to correct the bias exhibited by the leastsquares (LS) estimator of the lagged dependent variable (LDV) coefficient in dynamic regression models when the sample is finite. They have been termed as &lsquo / mean-unbiased&rsquo / and &lsquo / medianunbiased&rsquo / estimators. Relative to other similar procedures in the literature, the two locationbased estimators have the advantage that they offer an exact and uniform methodology for LS estimation of the LDV coefficient in a first order autoregressive model with or without exogenous regressors i.e. ARX(1).</p> <p><br /> However, no attempt has been made to accurately establish and/or compare the statistical properties among these estimators, or relative to those of the LS estimator when the LDV coefficient is restricted to realistic values. Neither has there been an attempt to&nbsp / compare their performance in terms of their mean squared error (MSE) when various forms of the exogenous regressors are considered. Furthermore, only implicit confidence intervals have been given for the &lsquo / medianunbiased&rsquo / estimator. Explicit confidence bounds that are directly usable for inference are not available for either estimator. In this study a new estimator of the LDV coefficient is proposed / the &lsquo / most-probably-unbiased&rsquo / estimator. Its performance properties vis-a-vis the existing estimators are determined and compared when the parameter space of the LDV coefficient is restricted. In addition, the following new results are established: (1) an explicit computable form for the density of the LS estimator is derived for the first time and an efficient method for its numerical evaluation is proposed / (2) the exact bias, mean, median and mode of the distribution of the LS estimator are determined in three specifications of the ARX(1) model / (3) the exact variance and MSE of LS estimator is determined / (4) the standard error associated with the determination of same quantities when simulation rather than numerical integration method is used are established and the methods are compared in terms of computational time and effort / (5) an exact method of evaluating the density of the three estimators is described / (6) their exact bias, mean, variance and MSE are determined and analysed / and finally, (7) a method of obtaining the explicit exact confidence intervals from the distribution functions of the estimators is proposed.</p> <p><br /> The discussion and results show that the estimators are still biased in the usual sense: &lsquo / in expectation&rsquo / . However the bias is substantially reduced compared to that of the LS estimator. The findings are important in the specification of time-series regression models, point and interval estimation, decision theory, and simulation.</p>

<p>In recent years, two estimators have been proposed to correct the bias exhibited by the leastsquares (LS) estimator of the lagged dependent variable (LDV) coefficient in dynamic regression models when the sample is finite. They have been termed as &lsquo / mean-unbiased&rsquo / and &lsquo / medianunbiased&rsquo / estimators. Relative to other similar procedures in the literature, the two locationbased estimators have the advantage that they offer an exact and uniform methodology for LS estimation of the LDV coefficient in a first order autoregressive model with or without exogenous regressors i.e. ARX(1).</p> <p><br /> However, no attempt has been made to accurately establish and/or compare the statistical properties among these estimators, or relative to those of the LS estimator when the LDV coefficient is restricted to realistic values. Neither has there been an attempt to&nbsp / compare their performance in terms of their mean squared error (MSE) when various forms of the exogenous regressors are considered. Furthermore, only implicit confidence intervals have been given for the &lsquo / medianunbiased&rsquo / estimator. Explicit confidence bounds that are directly usable for inference are not available for either estimator. In this study a new estimator of the LDV coefficient is proposed / the &lsquo / most-probably-unbiased&rsquo / estimator. Its performance properties vis-a-vis the existing estimators are determined and compared when the parameter space of the LDV coefficient is restricted. In addition, the following new results are established: (1) an explicit computable form for the density of the LS estimator is derived for the first time and an efficient method for its numerical evaluation is proposed / (2) the exact bias, mean, median and mode of the distribution of the LS estimator are determined in three specifications of the ARX(1) model / (3) the exact variance and MSE of LS estimator is determined / (4) the standard error associated with the determination of same quantities when simulation rather than numerical integration method is used are established and the methods are compared in terms of computational time and effort / (5) an exact method of evaluating the density of the three estimators is described / (6) their exact bias, mean, variance and MSE are determined and analysed / and finally, (7) a method of obtaining the explicit exact confidence intervals from the distribution functions of the estimators is proposed.</p> <p><br /> The discussion and results show that the estimators are still biased in the usual sense: &lsquo / in expectation&rsquo / . However the bias is substantially reduced compared to that of the LS estimator. The findings are important in the specification of time-series regression models, point and interval estimation, decision theory, and simulation.</p>

nÃo hà / The real estate investment (REI) is a newly created investment vehicle and still under constant development. Introduces, as basic characteristic, a property used for rental as the main asset. Governed by federal laws and regulations of the CVM instruction, regulatory frameworks help to give credibility to this investment vehicle. The REIs have tax benefits and remunerate its shareholders with regular income through rents. In addition, we present a third types of gain, which is the value of the shares of real estate funds. The current characteristics have a debonding between the equity and value of your shares, setting its recovery from supply and demand in the market. The study of this factor recovery was used to study development. Featuring a conservative perspective while being traded at BOVESPA, the question to be answered is whether the REI have a conservative characteristic when compared with other market indicators, such as IMOB, IBOVESPA, CDI, the IGP and INCC. And especially if there is a tendency over time with these same indicators, allowing to verify long-term behavior. With a stochastic characteristic non-stationary, the REI are cointegrated with the market indicators. The presentation of this tendency implies on a similar behavior over time, making it understandable with what market indicator the real estate investment presents tendency. Thus, the REI can be considered conservative investments, which have two returns (valuation of shares and payment of monthly rent), have characteristics of present value above the market benchmarks, low total and systemic risks and can be used as protection for stock investors, as a hedging tool. / O fundo de investimento imobiliÃrio (FII) à um instrumento de investimento recentemente criado e ainda em constante desenvolvimento. Apresenta como caracterÃstica bÃsica, possuir como o ativo principal um imÃvel utilizado para locaÃÃo. Regidos por leis federais e por instruÃÃes normativas da CVM, os marcos regulatÃrios ajudam a dar credibilidade a este instrumento de investimento. Os FII apresentam benefÃcios tributÃrios e remuneram seus cotistas atravÃs de receitas periÃdicas com aluguÃis. AlÃm destes, à apresentada uma terceira tipologias de ganho, que à a valorizaÃÃo das cotas dos fundos imobiliÃrios. As caracterÃsticas atuais apresentam um descolamento entre o patrimÃnio lÃquido e o valor das suas cotas, configurando uma valorizaÃÃo proveniente da oferta e procura pelas mesmas no mercado. O estudo desta valorizaÃÃo foi o elemento utilizado para o desenvolvimento do estudo. Apresentando uma perspectiva conservadora embora sendo negociado na BOVESPA, a pergunta a ser respondida à se os FII apresentam uma caracterÃstica conservadora comparado com outros indicadores de mercado, tais como o IMOB, o IBOVESPA, o CDI, o IGPM e o INCC. E principalmente se existe tendÃncia ao longo do tempo com estes mesmo indicadores, possibilitando verificar comportamento de longo prazo. Com uma caracterÃstica estocÃstica nÃo estacionÃria, os FII sÃo co-integrados com os indicadores de mercado. A apresentaÃÃo desta tendÃncia determina comportamento semelhante ao longo do tempo, fazendo com que possa ser entendido com qual indicador de mercado o fundo imobiliÃrio apresenta tendÃncia. Desta forma, os FII podem ser considerados investimentos conservadores, que apresentam duas rentabilidades (valorizaÃÃo das cotas e pagamento mensal de aluguel), possuem caracterÃsticas de apresentarem valorizaÃÃo acima dos benchmarks de mercado, apresentam baixo risco total e sistÃmico e podem ser utilizados como proteÃÃo para quem investe em aÃÃes, como uma ferramenta de hedge.

Magister Scientiae - MSc / In recent years, two estimators have been proposed to correct the bias exhibited by the leastsquares (LS) estimator of the lagged dependent variable (LDV) coefficient in dynamic regression models when the sample is finite. They have been termed as &lsquo;mean-unbiased&rsquo; and &lsquo;medianunbiased&rsquo; estimators. Relative to other similar procedures in the literature, the two locationbased estimators have the advantage that they offer an exact and uniform methodology for LS estimation of the LDV coefficient in a first order autoregressive model with or without exogenous regressors i.e. ARX(1). However, no attempt has been made to accurately establish and/or compare the statistical properties among these estimators, or relative to those of the LS estimator when the LDV coefficient is restricted to realistic values. Neither has there been an attempt to&nbsp; compare their performance in terms of their mean squared error (MSE) when various forms of the exogenous regressors are considered. Furthermore, only implicit confidence intervals have been given for the &lsquo;medianunbiased&rsquo; estimator. Explicit confidence bounds that are directly usable for inference are not available for either estimator. In this study a new estimator of the LDV coefficient is proposed; the &lsquo;most-probably-unbiased&rsquo; estimator. Its performance properties vis-a-vis the existing estimators are determined and compared when the parameter space of the LDV coefficient is restricted. In addition, the following new results are established: (1) an explicit computable form for the density of the LS estimator is derived for the first time and an efficient method for its numerical evaluation is proposed; (2) the exact bias, mean, median and mode of the distribution of the LS estimator are determined in three specifications of the ARX(1) model; (3) the exact variance and MSE of LS estimator is determined; (4) the standard error associated with the determination of same quantities when simulation rather than numerical integration method is used are established and the methods are compared in terms of computational time and effort; (5) an exact method of evaluating the density of the three estimators is described; (6) their exact bias, mean, variance and MSE are determined and analysed; and finally, (7) a method of obtaining the explicit exact confidence intervals from the distribution functions of the estimators is proposed. The discussion and results show that the estimators are still biased in the usual sense: &lsquo;in expectation&rsquo;. However the bias is substantially reduced compared to that of the LS estimator. The findings are important in the specification of time-series regression models, point and interval estimation, decision theory, and simulation. / South Africa

This thesis consists of four papers concerning modelling of count data and tourism demand. For three of the papers the focus is on the integer-valued autoregressive moving average model class (INARMA), and especially on the ENAR(l) model. The fourth paper studies the interaction between households' choice of number of leisure trips and number of overnight stays within a bivariate count data modelling framework. Paper [I] extends the basic INAR(1) model to enable more flexible and realistic empirical economic applications. The model is generalized by relaxing some of the model's basic independence assumptions. Results are given in terms of first and second conditional and unconditional order moments. Extensions to general INAR(p), time-varying, multivariate and threshold models are also considered. Estimation by conditional least squares and generalized method of moments techniques is feasible. Monte Carlo simulations for two of the extended models indicate reasonable estimation and testing properties. An illustration based on the number of Swedish mechanical paper and pulp mills is considered. Paper[II] considers the robustness of a conventional Dickey-Fuller (DF) test for the testing of a unit root in the INAR(1) model. Finite sample distributions for a model with Poisson distributed disturbance terms are obtained by Monte Carlo simulation. These distributions are wider than those of AR(1) models with normal distributed error terms. As the drift and sample size, respectively, increase the distributions appear to tend to T-2) and standard normal distributions. The main results are summarized by an approximating equation that also enables calculation of critical values for any sample and drift size. Paper[III] utilizes the INAR(l) model to model the day-to-day movements in the number of guest nights in hotels. By cross-sectional and temporal aggregation an INARMA(1,1) model for monthly data is obtained. The approach enables easy interpretation and econometric modelling of the parameters, in terms of daily mean check-in and check-out probability. Empirically approaches accounting for seasonality by dummies and using differenced series, as well as forecasting, are studied for a series of Norwegian guest nights in Swedish hotels. In a forecast evaluation the improvements by introducing economic variables is minute. Paper[IV] empirically studies household's joint choice of the number of leisure trips and the total night to stay on these trips. The paper introduces a bivariate count hurdle model to account for the relative high frequencies of zeros. A truncated bivariate mixed Poisson lognormal distribution, allowing for both positive as well as negative correlation between the count variables, is utilized. Inflation techniques are used to account for clustering of leisure time to weekends. Simulated maximum likelihood is used as estimation method. A small policy study indicates that households substitute trips for nights as the travel costs increase. / <p>Härtill 4 uppsatser.</p> / digitalisering@umu

In an effort to assess the predictive ability of exchange rate models when data on African countries is sampled, this paper studies nonlinear modelling and prediction of the nominal exchange rate series of the United States dollar to currencies of thirty-eight African states using the smooth transition autoregressive (STAR) model. A three step analysis is undertaken. One, it investigates nonlinearity in all nominal exchange rate series examined using a chain of credible statistical in-sample tests. Significantly, evidence of nonlinear exponential STAR (ESTAR) dynamics is detected across all series. Two, linear models are provided another chance to make it right by shuffling to data on African countries to investigate their predictive power against the tough random walk without drift model. Linear models again failed significantly. Lastly, the predictive ability of nonlinear models against both the random walk without drift and the corresponding linear models is investigated. Nonlinear models display useful forecasting gains over all contending models.

This study examined the effects of government expenditure on different components of economic growth in South Africa using quarterly data from the period 1970Q1 to 2016Q4. The six key policy variables employed in the analysis were derived from the Ram (1986) production model and the New Growth Path (NGP), a macroeconomic framework designed to address the main challenges (unemployment, poverty and inequality) facing the economy as a result of its political past. The analysis of the relationship was carried out using the VECM while the findings from the analysis revealed that though there exists a long-run equilibrium relationship among the variables. The long-run estimates showed that aggregate private consumption expenditure and employment-to-population ratio are significant but negatively, related to economic growth. However, the net inflows of foreign direct investment and gross fixed capital formation are negatively related to gross government expenditure. This implies that excessive public capital expenditure might reduce the positive impact of the two variables on economic growth. The study therefore suggests that government should consider increasing its expenditure on the significant variables that support labour and capital development, in order to enhance economic growth in South Africa. / Economics

本文改良了Shafiee-Topal(2010)所提出之跳躍模型之波動率，並歸納成三種模型:改良跳躍模型、改良平滑跳躍模型以及最佳化跳躍模型，並運用時間序列模型探討樣本期間內黃金價格。第一部份比較三種跳躍模型與Shafiee-Topal模型在訓練集及測試集的預測結果，並預測2012年至2018年之黃金價格走勢。第二部份探討黃金價格、原油價格以及美元加權指數之間的互動關係，建立多變數模型以預測黃金價格之長期趨勢。 首先，本文檢驗黃金價格、原油價格及美元加權指數樣本之恆定性，經由ADF 單根檢定法發現序列具有單根，進而使用TSP(Trend Stationary Process)估計模型參數。其次，黃金價格、原油價格及美元加權指數經共整合檢定發現，各模型變數間均具有共整合關係，即變數間具有長期均衡關係。黃金價格與原油價格呈正向反應，而黃金價格和原油價格與美元加權指數呈負向反應，除了受自身的預測解釋能力外，亦可以做為觀察其他變數的未來走勢方向及影響大小預估。最後，探討黃金價格受波動率的影響情形，本文改良Shafiee-Topal模型之波動率，並比較四種模型對黃金價格趨勢預測之結果，發現改良平滑跳躍模型在實際黃金價格波動率大時，其趨勢預測結果會優於Shafiee-Topal模型。 / This research advanced the volatility component (λ) of the jump and dip model (Shafiee and Topal,2010) on gold prices from 1968 to 2012 and estimated the gold price for the next 6 years. Based on the trend stationary process, we defined the three components and derived three new models: Adjusted Jump and Dip Model, Adjusted Smooth Jump and Dip Model and Optimized Jump and Dip Model. First part of the thesis compared the performance in prediction of the training data and the testing data for three different models and the jump and dip model. Second part of the thesis investigated the relationship among the gold price, crude oil price, and trade weighted U.S. dollar index of the concepts The result illustrated the long term trend of gold price described by a multivariate predictive model. We found evidence that different levels of volatility affect the prediction of gold price, and the adjusted jump and dip Model performs best when the true volatility is relatively high.

This thesis estimates the quarterly electricity demand of Turkey. First of all proper seasonal time series model are found for the variables: electricity demand, temperature, gross domestic product and electricity price. After the right seasonal time series model are found Hylleberg, Engle, Granger and Yoo (1990) test is applied to each variable. The results of the test show that seasonal unit roots exist for the electricity price even it cannot be seen at the graph. The other variables have no seasonal unit roots when the proper seasonal time series model is chosen. Later, the cointegration is tested by looking at the vector autoregressive model. As the cointegration is seen vector error correction model is found. There is long-run equilibrium when the price is the dependent variable and independent variable is gross domestic product. Temperature is taken as exogenous variable and demand is not statistically significant.

This study presents our attempt to examine the linkages and to investigate the linkage of stock price indexes among Taiwan, China and its major trading countries. Our empirical analysis employs daily data on stock price indexes over the period of January 2, 2000 to May 10, 2010. The total number of observations is about 2500. This study employ a sequence of time-series methodologies, including unit root test, cointegration test, vector error correction model, Granger causality test, Criterion, autocorrelation test, heteroscedasticity test, GARCH and Bi-GARCH. The findings of this study as follows. First, after first difference, every stock price indexes series all became stationary. Second, we found there has no long-run interrelationship among these stock markets. Third, we found that Taiwan¡¦s stock market exits leading role to China¡¦s stock market, but other countries¡¦ stock market lead Taiwan¡¦s stock market. For China, the stock market of United States, Japan, Taiwan and Hong Kong has a leading role to China¡¦s stock market. Only the rela-tionship between South Korea and China¡¦s stock market is independent. Forth, the result of autocorrelation test and ARCH test indicates that the influence of stock price indexes of major trading countries to Taiwan and China¡¦s stock price index has changed over time. Finally, the result of study indicates that every stock market can forecast its future trend by using its past stock data and investor can use the past stock data of stock market of major trading countries to forecast Taiwan and China¡¦s stock market.