Dynamic Factor Models and Forecasting Finnish Macroeconomic Variables

In recent years, thanks to developments in information technology, large-dimensional datasets have been increasingly available. Researchers now have access to thousands of economic series and the information contained in them can be used to create accurate forecasts and to test economic theories. To exploit this large amount of information, researchers and policymakers need an appropriate econometric model.Usual time series models, vector autoregression for example, cannot incorporate more than a few variables. There are two ways to solve this problem: use variable selection procedures or gather the information contained in the series to create an index model. This thesis focuses on one of the most widespread index model, the dynamic factor model (the theory behind this model, based on previous literature, is the core of the first part of this study), and its use in forecasting Finnish macroeconomic indicators (which is the focus of the second part of the thesis). In particular, I forecast economic activity indicators (e.g. GDP) and price indicators (e.g. consumer price index), from 3 large Finnish datasets. The first dataset contains a large series of aggregated data obtained from the Statistics Finland database. The second dataset is composed by economic indicators from Bank of Finland. The last dataset is formed by disaggregated data from Statistic Finland, which I call micro dataset. The forecasts are computed following a two steps procedure: in the first step I estimate a set of common factors from the original dataset. The second step consists in formulating forecasting equations including the factors extracted previously. The predictions are evaluated using relative mean squared forecast error, where the benchmark model is a univariate autoregressive model. The results are dataset-dependent. The forecasts based on factor models are very accurate for the first dataset (the Statistics Finland one), while they are considerably worse for the Bank of Finland dataset. The forecasts derived from the micro dataset are still good, but less accurate than the ones obtained in the first case. This work leads to multiple research developments. The results here obtained can be replicated for longer datasets. The non-aggregated data can be represented in an even more disaggregated form (firm level). Finally, the use of the micro data, one of the major contributions of this thesis, can be useful in the imputation of missing values and the creation of flash estimates of macroeconomic indicator (nowcasting).

Factors affecting partitioning of private forest holdings in Finland.

Questions of the small size of non-industrial private forest (NIPF) holdings in Finland are considered and factors affecting their partitioning are analyzed. This work arises out of Finnish forest policy statements in which the small average size of holdings has been seen to have a negative influence on the economics of forestry. A survey of the literature indicates that the size of holdings is an important factor determining the costs of logging and silvicultural operations, while its influence on the timber supply is slight. The empirical data are based on a sample of 314 holdings collected by interviewing forest owners in the years 1980-86. In 1990-91 the same holdings were resurveyed by means of a postal inquiry and partly by interviewing forest owners. The principal objective in compiling the data is to assist in quantifying ownership factors that influence partitioning among different kinds of NIPF holdings. Thus the mechanism of partitioning were described and a maximum likelihood logistic regression model was constructed using seven independent holding and ownership variables. One out of four holdings had undergone partitioning in conjunction with a change in ownership, one fifth among family owned holdings and nearly a half among jointly owned holdings. The results of the logistic regression model indicate, for instance, that the odds on partitioning is about three times greater for jointly owned holdings than for family owned ones. Also, the probabilities of partitioning were estimated and the impact of independent dichotomous variables on the probability of partitioning ranged between 0.02 and 0.10. The low value of the Hosmer-Lemeshow test statistic indicates a good fit of the model and the rate of correct classification was estimated to be 88 per cent with a cutoff point of 0.5. The average size of holdings undergoing ownership changes decreased from 29.9 ha to 28.7 ha over the approximate interval 1983-90. In addition, the transition probability matrix showed that the trends towards smaller size categories mostly involved in the small size categories, less than 20 ha. The results of the study can be used in considering the effects of the small size of holdings for forestry and if the purpose is to influence partitioning through forest or rural policy.

BER Analysis of Weighted Interference Cancellation in Multicarrier DS-CDMA Systems

In this paper, we present the design and bit error performance analysis of weighted linear parallel interference cancellers (LPIC) for multicarrier (MC) DS-CDMA systems. We propose an LPIC scheme where we estimate (and cancel) the multiple access interference (MAI) based on the soft outputs on individual subcarriers, and the interference cancelled outputs on different subcarriers are combined to form the final decision statistic. We scale the MAI estimate on individual subcarriers by a weight before cancellation; these weights are so chosen to maximize the signal-to-interference ratios at the individual subcarrier outputs. For this weighted LPIC scheme, using an approach involving the characteristic function of the decision variable, we derive exact bit error rate (BER) expressions for different cancellation stages. Using the same approach, we also derive exact BER expressions for the matched filter (MF) and decorrelating detectors for the considered MC DS-CDMA system. We show that the proposed weighted LPIC scheme performs better than the MF detector and the conventional LPIC (where the weights are taken to be unity), and close to the decorrelating detector.

GNSS Signal Detection Under Noise Uncertainty

High sensitivity detection techniques are required for indoor navigation using Global Navigation Satellite System (GNSS) receivers, and typically, a combination of coherent and non- coherent integration is used as the test statistic for detection. The coherent integration exploits the deterministic part of the signal and is limited due to the residual frequency error, navigation data bits and user dynamics, which are not known apriori. So, non- coherent integration, which involves squaring of the coherent integration output, is used to improve the detection sensitivity. Due to this squaring, it is robust against the artifacts introduced due to data bits and/or frequency error. However, it is susceptible to uncertainty in the noise variance, and this can lead to fundamental sensitivity limits in detecting weak signals. In this work, the performance of the conventional non-coherent integration-based GNSS signal detection is studied in the presence of noise uncertainty. It is shown that the performance of the current state of the art GNSS receivers is close to the theoretical SNR limit for reliable detection at moderate levels of noise uncertainty. Alternate robust post-coherent detectors are also analyzed, and are shown to alleviate the noise uncertainty problem. Monte-Carlo simulations are used to confirm the theoretical predictions.

Non-Gaussianity and direction-dependent systematics in HST Key Project data

Two new statistics, namely Delta(chi 2) and Delta(chi), based on the extreme value theory, were derived by Gupta et al. We use these statistics to study the direction dependence in the HST Key Project data, which provides one of the most precise measurements of the Hubble constant. We also study the non-Gaussianity in this data set using these statistics. Our results for Delta(chi 2) show that the significance of direction-dependent systematics is restricted to well below the 1 sigma confidence limit; however, the presence of non-Gaussian features is subtle. On the other hand, the Delta(chi). statistic, which is more sensitive to direction dependence, shows direction dependence systematics to be at a slightly higher confidence level, and the presence of non-Gaussian features at a level similar to the Delta(chi 2) statistic.

Comparative statistical aging studies on an oil-pressboard insulation model

Accelerated aging experiments have been conducted on a representative oil-pressboard insulation model to investigate the effect of constant and sequential stresses on the PD behavior using a built-in phase resolved partial discharge analyzer. A cycle of the applied voltage starting from the zero of the positive half cycle was divided into 16 equal phase windows (Φ1 to Φ16) and partial discharge (PD) magnitude distribution in each phase was determined. Based on the experimental results, three stages of aging mechanism were identified. Gumbel's extreme value distribution of the largest element was used to model the first stage of aging process. Second and subsequent stages were modeled using two-parameter Weibull distribution. Spearman's non-parametric rank correlation test statistic and Kolmogrov-Smirnov two sample test were used to relate the aging process of each phase with the corresponding process of the full cycle. To bring out clearly the effect of stress level, its duration and test procedure on the distribution parameters and hence of the aging process, non-parametric ANOVA techniques like Kruskal-Wallis and Fisher's LSD multiple comparison tests were used. Results of the analysis show that two phases (Φ13 and Φ14) near the vicinity of the negative voltage peak were found to contribute significantly to the aging process and their aging mechanism also correlated well with that of the corresponding full cycle mechanism. Attempts have been made to relate these results with the published work of other workers

Delay Optimal Event Detection on Ad Hoc Wireless Sensor Networks

We consider a small extent sensor network for event detection, in which nodes periodically take samples and then contend over a random access network to transmit their measurement packets to the fusion center. We consider two procedures at the fusion center for processing the measurements. The Bayesian setting, is assumed, that is, the fusion center has a prior distribution on the change time. In the first procedure, the decision algorithm at the fusion center is network-oblivious and makes a decision only when a complete vector of measurements taken at a sampling instant is available. In the second procedure, the decision algorithm at the fusion center is network-aware and processes measurements as they arrive, but in a time-causal order. In this case, the decision statistic depends on the network delays, whereas in the network-oblivious case, the decision statistic does not. This yields a Bayesian change-detection problem with a trade-off between the random network delay and the decision delay that is, a higher sampling rate reduces the decision delay but increases the random access delay. Under periodic sampling, in the network-oblivious case, the structure of the optimal stopping rule is the same as that without the network, and the optimal change detection delay decouples into the network delay and the optimal decision delay without the network. In the network-aware case, the optimal stopping problem is analyzed as a partially observable Markov decision process, in which the states of the queues and delays in the network need to be maintained. A sufficient decision statistic is the network state and the posterior probability of change having occurred, given the measurements received and the state of the network. The optimal regimes are studied using simulation.

Robust GNSS signal detection in the presence of navigation data bits

This paper considers the problem of weak signal detection in the presence of navigation data bits for Global Navigation Satellite System (GNSS) receivers. Typically, a set of partial coherent integration outputs are non-coherently accumulated to combat the effects of model uncertainties such as the presence of navigation data-bits and/or frequency uncertainty, resulting in a sub-optimal test statistic. In this work, the test-statistic for weak signal detection is derived in the presence of navigation data-bits from the likelihood ratio. It is highlighted that averaging the likelihood ratio based test-statistic over the prior distributions of the unknown data bits and the carrier phase uncertainty leads to the conventional Post Detection Integration (PDI) technique for detection. To improve the performance in the presence of model uncertainties, a novel cyclostationarity based sub-optimal PDI technique is proposed. The test statistic is analytically characterized, and shown to be robust to the presence of navigation data-bits, frequency, phase and noise uncertainties. Monte Carlo simulation results illustrate the validity of the theoretical results and the superior performance offered by the proposed detector in the presence of model uncertainties.

Efecto de diferentes sistemas de preparación del suelo sobre las características físicas, crecimiento y rendimiento del cultivo de la yuca (Manihot esculenta crantz) variedad algodón en Nueva Guinea

El estudio se realizó en municipio Nueva Guinea, RAAS desde octubre del 2005 a agosto 2006 para evaluar el efecto de diferentes sistemas de preparación de suelo sobre sus propiedades físicas, el crecimiento y rendimiento del cultivar yuca Algodón. Los tratamientos fueron; subsoleo más encamado (SE), grada más encamado (GE),cero labranza (CL), y tracción animal (TA). El diseño utilizado fue de bloques completos al azar (BCA) con tres repeticiones en cinco profundidades. Las variables de suelo evaluadas fueron: densidad aparente, densidad real, porosidad total, y capacidad de campo, las variables agronómicas altura diámetro del tallo, peso fresco de la planta, número de raíces, peso de raíz y rendimiento de raíces. Para el análisis de variables se utilizó el programa Statistic Análisis Systems (SAS V9.1). El tratamiento de TA presentó los menores valores de densidad aparente, una distribución más uniforme en la capacidad de retención de agua dentro del perfil del suelo, así como mayor porosidad. CL y TA presentaron los mayores valores de altura de planta, diámetro del tallo y peso fresco de hojas y tallos del cultivo de la yuca. TA presentó los valores más altos del número de raíces totales por planta de yuca seguido de CL el cual a su vez presentó el mayor número de raíces exportables seguido de TA, pero sin diferencia significativa entre la longitud de las raíces exportables entre los cuatro tratamientos. CL presentó el mayor diámetro, el mayor peso de las raíces exportables seguido siempre por TA. CL presentó el mayor rendimiento de raíces exportables y no exportables o mayores rendimientos totales (kg/ha) de raíces reservantes seguido por TA. Se encontraron diferencias de promedios en los rendimientos de raíces exportables entre CL y GE 3,399 kg/ha, en los rendimientos de raíces no exportables una diferencia 2,351.5 kg/ha entre CL y SE. Se recomienda repetir este ensayo utilizando los mismos tratamientos evaluar el efecto del cambio de la calidad del suelo en el tiempo.

A Non-Parametric Dimension Test of the Term Structure

Published as an article in: Studies in Nonlinear Dynamics & Econometrics, 2004, vol. 8, issue 3, article 6.

Long memory in return structures from developed markets

[En]The present study aimed at investigating the existence of long memory properties in ten developed stock markets across the globe. When return series exhibit long memory, the series realizations are not independent over time and past returns can help predict future returns, thus violating the market efficiency hypothesis. It poses a serious challenge to the supporters of random walk behavior of the stock returns indicating a potentially predictable component in the series dynamics. We computed Hurst-Mandelbrot’s Classical R/S statistic, Lo’s statistic and semi parametric GPH statistic using spectral regression. The findings suggest existence of long memory in volatility and random walk for logarithmic return series in general for all the selected stock market indices. Findings are in line with the stylized facts of financial time series.

Respuesta a la sequía de Pinus radiata D. Don y su implicación en los procesos de tolerancia

160 p. (Bibliogr. 141-160)

Análisis histológico e inmunohistoquímico de los subtipos de enfermedad liquenoide oral

176 p. : il.

Entwicklungstendenzen der Steinbuttfischerei in der Ostsee

The economical value of turbot (Psetta maxima) from the Baltic Sea has distinctly increased in the past decade. Compared to the eighties the catches of this species on the main fishing grounds increased threefold. Besides a more directed fishery on this species and an improved catch statistic the main reason for this fact is probably an increased stock size. Improved protection measures caused three consecutive strong year-classes. The main parameters of the turbot stock are presented.

An evaluation of geographic and annual variation in morphometric characters and gill-raker counts of yellowfin tuna, Thunnus albacares, from the Pacific Ocean

ENGLISH: Samples of yellowfin tuna, Thunnus albacares, collected from five areas of the Pacific Ocean (Mexico, Ecuador, Australia, Japan, and Hawaii) between January and May of 1988 and 1990 were examined for spatiotemporal variation in morphometric characters and gill-raker counts. 'Iwo-factor analysis of variance, with area and year treated as grouping factors, indicated a significant difference in the means of the total gill-raker counts among fish from different areas, but no significant difference between fish caught in different years. The morphometric data were adjusted by allometric formulae to remove size effects. The correct classification rates for the five groups, using discriminant function analysis, based on adjusted morphometric characters, were 77.60/0 for the samples from 1988 and 74.40/0 for those from 1990. These are 72.00/0 and 68.00/0 (Cohen's kappa statistic) better than would have occurred chance. The pattern of geographic variability, however, is unstable for these two years, thus requiring separate discriminant functions for each year. Although there is annual variability in the morphometric characters, these results demonstrate that the stocks examined are morphometrically distinguishable and that their phenetic relationships reflect their geographic origin. SPANISH: Se examinaron muestras de atún aleta amarilla, Thunnus albacares, tomadas de cinco áreas del Océano Pacífico (México, Ecuador, Australia, Japón, y Hawaii) entre enero y mayo de 1988 y 1990, para descubrir variaciones espaciotemporales en las características morfométricas y los conteos de branquiespinas. Un análisis de varianza de dos factores, con área y año como factores de agrupación, indicó una diferencia significativa en los promedios de los conteos de branquiespinas totales entre peces de distintas áreas, pero ninguna entre peces capturados en distintos años. Se ajustaron los datos morfométricos con fórmulas alométricas para eliminar los efectos de la talla del pez. En un análisis de función discriminante, las tasas de clasificación correcta de los cinco grupos, basadas en características morfométricas ajustadas, fueron 77.60/0 para las muestras de 1988 y 74.40/0 para aquellas de 1990. Estas cifras son 72.00/0 y 68.00/0 (estadístico de kappa de Cohen) mejores de lo que se hubiera obtenido al azar. Sin embargo, la variabilidad geográfica es inestable en estos dos años, requiriendo por lo tanto funciones discriminantes separadas para cada año. Aunque existe variabilidad anual en las características morfométricas, estos resultados demuestran que los stocks examinados son morfamétricamente distinguibles, y que su relación fenética refleja su origen geográfico. (PDF contains 31 pages.)