Design and testing of stand-specific bucking instructions for use on modern cut-to-length harvesters

This study addresses three important issues in tree bucking optimization in the context of cut-to-length harvesting. (1) Would the fit between the log demand and log output distributions be better if the price and/or demand matrices controlling the bucking decisions on modern cut-to-length harvesters were adjusted to the unique conditions of each individual stand? (2) In what ways can we generate stand and product specific price and demand matrices? (3) What alternatives do we have to measure the fit between the log demand and log output distributions, and what would be an ideal goodness-of-fit measure? Three iterative search systems were developed for seeking stand-specific price and demand matrix sets: (1) A fuzzy logic control system for calibrating the price matrix of one log product for one stand at a time (the stand-level one-product approach); (2) a genetic algorithm system for adjusting the price matrices of one log product in parallel for several stands (the forest-level one-product approach); and (3) a genetic algorithm system for dividing the overall demand matrix of each of the several log products into stand-specific sub-demands simultaneously for several stands and products (the forest-level multi-product approach). The stem material used for testing the performance of the stand-specific price and demand matrices against that of the reference matrices was comprised of 9 155 Norway spruce (Picea abies (L.) Karst.) sawlog stems gathered by harvesters from 15 mature spruce-dominated stands in southern Finland. The reference price and demand matrices were either direct copies or slightly modified versions of those used by two Finnish sawmilling companies. Two types of stand-specific bucking matrices were compiled for each log product. One was from the harvester-collected stem profiles and the other was from the pre-harvest inventory data. Four goodness-of-fit measures were analyzed for their appropriateness in determining the similarity between the log demand and log output distributions: (1) the apportionment degree (index), (2) the chi-square statistic, (3) Laspeyres quantity index, and (4) the price-weighted apportionment degree. The study confirmed that any improvement in the fit between the log demand and log output distributions can only be realized at the expense of log volumes produced. Stand-level pre-control of price matrices was found to be advantageous, provided the control is done with perfect stem data. Forest-level pre-control of price matrices resulted in no improvement in the cumulative apportionment degree. Cutting stands under the control of stand-specific demand matrices yielded a better total fit between the demand and output matrices at the forest level than was obtained by cutting each stand with non-stand-specific reference matrices. The theoretical and experimental analyses suggest that none of the three alternative goodness-of-fit measures clearly outperforms the traditional apportionment degree measure. Keywords: harvesting, tree bucking optimization, simulation, fuzzy control, genetic algorithms, goodness-of-fit

Do Measurement Errors in GDP Announcements Cause Output Fluctuations?

This thesis studies empirically whether measurement errors in aggregate production statistics affect sentiment and future output. Initial announcements of aggregate production are subject to measurement error, because many of the data required to compile the statistics are produced with a lag. This measurement error can be gauged as the difference between the latest revised statistic and its initial announcement. Assuming aggregate production statistics help forecast future aggregate production, these measurement errors are expected to affect macroeconomic forecasts. Assuming agents’ macroeconomic forecasts affect their production choices, these measurement errors should affect future output through sentiment. This thesis is primarily empirical, so the theoretical basis, strategic complementarity, is discussed quite briefly. However, it is a model in which higher aggregate production increases each agent’s incentive to produce. In this circumstance a statistical announcement which suggests aggregate production is high would increase each agent’s incentive to produce, thus resulting in higher aggregate production. In this way the existence of strategic complementarity provides the theoretical basis for output fluctuations caused by measurement mistakes in aggregate production statistics. Previous empirical studies suggest that measurement errors in gross national product affect future aggregate production in the United States. Additionally it has been demonstrated that measurement errors in the Index of Leading Indicators affect forecasts by professional economists as well as future industrial production in the United States. This thesis aims to verify the applicability of these findings to other countries, as well as study the link between measurement errors in gross domestic product and sentiment. This thesis explores the relationship between measurement errors in gross domestic production and sentiment and future output. Professional forecasts and consumer sentiment in the United States and Finland, as well as producer sentiment in Finland, are used as the measures of sentiment. Using statistical techniques it is found that measurement errors in gross domestic product affect forecasts and producer sentiment. The effect on consumer sentiment is ambiguous. The relationship between measurement errors and future output is explored using data from Finland, United States, United Kingdom, New Zealand and Sweden. It is found that measurement errors have affected aggregate production or investment in Finland, United States, United Kingdom and Sweden. Specifically, it was found that overly optimistic statistics announcements are associated with higher output and vice versa.

Essays on Risk Modeling. Applications to Portfolio and Risk Management (summary section only)

In this thesis we deal with the concept of risk. The objective is to bring together and conclude on some normative information regarding quantitative portfolio management and risk assessment. The first essay concentrates on return dependency. We propose an algorithm for classifying markets into rising and falling. Given the algorithm, we derive a statistic: the Trend Switch Probability, for detection of long-term return dependency in the first moment. The empirical results suggest that the Trend Switch Probability is robust over various volatility specifications. The serial dependency in bear and bull markets behaves however differently. It is strongly positive in rising market whereas in bear markets it is closer to a random walk. Realized volatility, a technique for estimating volatility from high frequency data, is investigated in essays two and three. In the second essay we find, when measuring realized variance on a set of German stocks, that the second moment dependency structure is highly unstable and changes randomly. Results also suggest that volatility is non-stationary from time to time. In the third essay we examine the impact from market microstructure on the error between estimated realized volatility and the volatility of the underlying process. With simulation-based techniques we show that autocorrelation in returns leads to biased variance estimates and that lower sampling frequency and non-constant volatility increases the error variation between the estimated variance and the variance of the underlying process. From these essays we can conclude that volatility is not easily estimated, even from high frequency data. It is neither very well behaved in terms of stability nor dependency over time. Based on these observations, we would recommend the use of simple, transparent methods that are likely to be more robust over differing volatility regimes than models with a complex parameter universe. In analyzing long-term return dependency in the first moment we find that the Trend Switch Probability is a robust estimator. This is an interesting area for further research, with important implications for active asset allocation.

Turvemaan männyn (Pinus sylvestris L.) ja kuusen (Picea abies [L.] Karst.) kanto- ja juuripuun ominaisuudet ja asetoniliukoisten uuteaineiden määrä

Tutkimus on osa Metsäklusteri Oy:n Future Biorefinery –tutkimusohjelmaa, jossa kartoitetaan mahdollisuuksia hyödyntää metsäteollisuuden raaka-aineita aiempaa tarkemmin ja uusissa tuotteissa. Tutkimuksen tavoitteena on selvittää männyn (Pinus sylvestris L.) ja kuusen (Picea abies [L.] Karst.) juurten ja kantopuun rakenne ja ominaisuudet. Tutkimuksessa selvitetään löytyykö männyn ja kuusen juurista reaktiopuuta ja määritetään asetoniliukoisten uuteaineiden osuus kantoja juuripuussa. Tutkimusaineistona oli viisi eri-ikäistä mäntyä ja viisi eri-ikäistä kuusta. Juuri- ja kantoaineisto kerättiin Metsäntutkimuslaitoksen toimesta Parkanon seudulta (62.017°N, 23.017°E) hakkuun jälkeen. Maanalaisista juurista otettiin näytteet kolmelta eri etäisyydeltä juurenniskaan nähden. Kummankaan lajin juurista ei löytynyt varsinaista reaktiopuuta, mutta joissakin näytteissä havaittiin lievää reaktiopuuta. Lievää reaktiopuuta löytyi enemmän männyn kuin kuusen juurista, eikä sitä löytynyt lainkaan kaikkein ohuimmista, noin 2 cm paksuisista juurenosista. Männyn kannoissa uuteaineprosentti oli korkeampi kuin kuusen. Männyn juurissa uuteaineprosentti kasvoi edettäessä kohti juuren kärkeä. Kuusella uuteaineprosentti laski aluksi, mutta lähellä juuren kärkeä taas kasvoi. Kuoren uuteainepitoisuus oli molemmilla puulajeilla korkeampi kuin puuaineen. Tutkimusaineisto oli suppea, eikä tutkimuksessa pyritty tilastolliseen yleistettävyyteen. Laajemmasta aineistosta tehdylle tutkimukselle on tarvetta, sillä turvekankailta saatavan puun tarjonta on Suomessa kasvussa, mutta juurten uuteaine- ja reaktiopuututkimuksia on tehty vain kivennäismailta kerätyistä aineistoista.

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.