Generalised regression estimation for domain class frequencies

This study examines the properties of Generalised Regression (GREG) estimators for domain class frequencies and proportions. The family of GREG estimators forms the class of design-based model-assisted estimators. All GREG estimators utilise auxiliary information via modelling. The classic GREG estimator with a linear fixed effects assisting model (GREG-lin) is one example. But when estimating class frequencies, the study variable is binary or polytomous. Therefore logistic-type assisting models (e.g. logistic or probit model) should be preferred over the linear one. However, other GREG estimators than GREG-lin are rarely used, and knowledge about their properties is limited. This study examines the properties of L-GREG estimators, which are GREG estimators with fixed-effects logistic-type models. Three research questions are addressed. First, I study whether and when L-GREG estimators are more accurate than GREG-lin. Theoretical results and Monte Carlo experiments which cover both equal and unequal probability sampling designs and a wide variety of model formulations show that in standard situations, the difference between L-GREG and GREG-lin is small. But in the case of a strong assisting model, two interesting situations arise: if the domain sample size is reasonably large, L-GREG is more accurate than GREG-lin, and if the domain sample size is very small, estimation of assisting model parameters may be inaccurate, resulting in bias for L-GREG. Second, I study variance estimation for the L-GREG estimators. The standard variance estimator (S) for all GREG estimators resembles the Sen-Yates-Grundy variance estimator, but it is a double sum of prediction errors, not of the observed values of the study variable. Monte Carlo experiments show that S underestimates the variance of L-GREG especially if the domain sample size is minor, or if the assisting model is strong. Third, since the standard variance estimator S often fails for the L-GREG estimators, I propose a new augmented variance estimator (A). The difference between S and the new estimator A is that the latter takes into account the difference between the sample fit model and the census fit model. In Monte Carlo experiments, the new estimator A outperformed the standard estimator S in terms of bias, root mean square error and coverage rate. Thus the new estimator provides a good alternative to the standard estimator.

Residual noise covariance for Planck low-resolution data analysis

Aims: Develop and validate tools to estimate residual noise covariance in Planck frequency maps. Quantify signal error effects and compare different techniques to produce low-resolution maps. Methods: We derive analytical estimates of covariance of the residual noise contained in low-resolution maps produced using a number of map-making approaches. We test these analytical predictions using Monte Carlo simulations and their impact on angular power spectrum estimation. We use simulations to quantify the level of signal errors incurred in different resolution downgrading schemes considered in this work. Results: We find an excellent agreement between the optimal residual noise covariance matrices and Monte Carlo noise maps. For destriping map-makers, the extent of agreement is dictated by the knee frequency of the correlated noise component and the chosen baseline offset length. The significance of signal striping is shown to be insignificant when properly dealt with. In map resolution downgrading, we find that a carefully selected window function is required to reduce aliasing to the sub-percent level at multipoles, ell > 2Nside, where Nside is the HEALPix resolution parameter. We show that sufficient characterization of the residual noise is unavoidable if one is to draw reliable contraints on large scale anisotropy. Conclusions: We have described how to compute the low-resolution maps, with a controlled sky signal level, and a reliable estimate of covariance of the residual noise. We have also presented a method to smooth the residual noise covariance matrices to describe the noise correlations in smoothed, bandwidth limited maps.

An Expenditure-Based Estimation of Self-Employment Income Underreporting in Finland

This paper estimates the extent of income underreporting by the self-employed in Finland using the expenditure based approach developed by Pissarides & Weber (1989). Household spending data are for the years 1994 to 1996. The results suggest that self-employment income in Finland is underreported by some 27% on average. Since income for the self-employed is about 8 % of all incomes in Finland, the size of this part of the black economy in Finland is estimated to be about 2,3% of GDP.

With Good Reputation Size Does not Matter: Issue Frequency and the Determinants of Debt Maturity

This paper examines empirically the effect firm reputation has on the determinants of debt maturity. Utilising data from European primary bond market between 1999 and 2005, I find that the maturity choice of issuers with a higher reputation is less sensitive to macroeconomic conditions, market credit risk-premiums, prevailing firm credit quality and size of the debt issue. The annualised coupon payments are shown to be a significant factor in determining the debt maturity and reveal a monotonously increasing relationship between credit quality and debt maturity once controlled for. Finally, I show that issuers lacking a credit rating have an implied credit quality positioned between investment-grade and speculative-grade debt.

Analysts' Accuracy of Estimation and the Relative Trading Volume

This study contributes to the neglect effect literature by looking at the relative trading volume in terms of value. The results for the Swedish market show a significant positive relationship between the accuracy of estimation and the relative trading volume. Market capitalisation and analyst coverage have in prior studies been used as proxies for neglect. These measures however, do not take into account the effort analysts put in when estimating corporate pre-tax profits. I also find evidence that the industry of the firm influence the accuracy of estimation. In addition, supporting earlier findings, loss making firms are associated with larger forecasting errors. Further, I find that the average forecast error increased in the year 2000 – in Sweden.

Stock Option Compensation in Finland: An Analysis of Economic Determinants, Contracting Frequency, and Design

This paper addresses several questions in the compensation literature by examining stock option compensation practices of Finnish firms. First, the results indicate that principal-agent theory succeeds quite well in predicting the use of stock options. Proxies for monitoring costs, growth opportunities, ownership structure, and risk are found to determine the use of incentives consistent with theory. Furthermore, the paper examines whether determinants of stock options targeted to top management differ from determinants of broad-based stock option plans. Some evidence is found that factors driving these two types of incentives differ. Second, the results reveal that systematic risk significantly increases the likelihood that firms adopt stock option plans, whereas total firm risk and unsystematic risk do not seem to affect this decision. Third, the results show that growth opportunities are related to time-dimensional contracting frequency, consistent with the argument that incentive levels deviate more rapidly from optimum in firms with high growth opportunities. Finally, the results suggest that vesting schedules are decreasing in financial leverage, and that contract maturity is decreasing in firm focus. In addition, both vesting schedules and contract maturity tend to be longer in firms involving state ownership.

Applications of Treatment Effects Models and Semiparametric Estimation

This thesis is composed of an introductory chapter and four applications each of them constituting an own chapter. The common element underlying each of the chapters is the econometric methodology. The applications rely mostly on the leading econometric techniques related to estimation of causal effects. The first chapter introduces the econometric techniques that are employed in the remaining chapters. Chapter 2 studies the effects of shocking news on student performance. It exploits the fact that the school shooting in Kauhajoki in 2008 coincided with the matriculation examination period of that fall. It shows that the performance of men declined due to the news of the school shooting. For women the similar pattern remains unobserved. Chapter 3 studies the effects of minimum wage on employment by employing the original Card and Krueger (1994; CK) and Neumark and Wascher (2000; NW) data together with the changes-in-changes (CIC) estimator. As the main result it shows that the employment effect of an increase in the minimum wage is positive for small fast-food restaurants and negative for big fast-food restaurants. Therefore, it shows that the controversial positive employment effect reported by CK is overturned for big fast-food restaurants and that the NW data are shown, in contrast to their original results, to provide support for the positive employment effect. Chapter 4 employs the state-specific U.S. data (collected by Cohen and Einav [2003; CE]) on traffic fatalities to re-evaluate the effects of seat belt laws on the traffic fatalities by using the CIC estimator. It confirms the CE results that on the average an implementation of a mandatory seat belt law results in an increase in the seat belt usage rate and a decrease in the total fatality rate. In contrast to CE, it also finds evidence on compensating-behavior theory, which is observed especially in the states by the border of the U.S. Chapter 5 studies the life cycle consumption in Finland, with the special interest laid on the baby boomers and the older households. It shows that the baby boomers smooth their consumption over the life cycle more than other generations. It also shows that the old households smoothed their life cycle consumption more as a result of the recession in the 1990s, compared to young households.

Population projection for Namibia 2011-2020 - With an estimation of the impact of HIV/AIDS

This study presents a population projection for Namibia for years 2011–2020. In many countries of sub-Saharan Africa, including Namibia, the population growth is still continuing even though the fertility rates have declined. However, many of these countries suffer from a large HIV epidemic that is slowing down the population growth. In Namibia, the epidemic has been severe. Therefore, it is important to assess the effect of HIV/AIDS on the population of Namibia in the future. Demographic research on Namibia has not been very extensive, and data on population is not widely available. According to the studies made, fertility has been shown to be generally declining and mortality has been significantly increasing due to AIDS. Previous population projections predict population growth for Namibia in the near future, yet HIV/AIDS is affecting the future population developments. For the projection constructed in this study, data on population is taken from the two most recent censuses, from 1991 and 2001. Data on HIV is available from HIV Sentinel Surveys 1992–2008, which test pregnant women for HIV in antenatal clinics. Additional data are collected from different sources and recent studies. The projection is made with software (EPP and Spectrum) specially designed for developing countries with scarce data. The projection includes two main scenarios which have different assumptions concerning the development of the HIV epidemic. In addition, two hypothetical scenarios are made: the first considering the case where HIV epidemic would never have existed and the second considering the case where HIV treatment would never have existed. The results indicate population growth for Namibia. Population in the 2001 census was 1.83 million and is projected to result in 2.38/2.39 million in 2020 in the first two scenarios. Without HIV, population would be 2.61 million and without treatment 2.30 million in 2020. Urban population is growing faster than rural. Even though AIDS is increasing mortality, the past high fertility rates still keep young adult age groups quite large. The HIV epidemic shows to be slowing down, but it is still increasing the mortality of the working-aged population. The initiation of HIV treatment in 2004 in the public sector seems to have had an effect on many projected indicators, diminishing the impact of HIV on the population. For example, the rise of mortality is slowing down.

Estimation of cutting volume with three inventory methods for harvest planning in Canadian boreal forests.

Two methods of pre-harvest inventory were designed and tested on three cutting sites containing a total of 197 500 m3 of wood. These sites were located on flat-ground boreal forests located in northwestern Quebec. Both methods studied involved scaling of trees harvested to clear the road path one year (or more) prior to harvest of adjacent cut-blocks. The first method (ROAD) considers the total road right-of-way volume divided by the total road area cleared. The resulting volume per hectare is then multiplied by the total cut-block area scheduled for harvest during the following year to obtain the total estimated cutting volume. The second method (STRATIFIED) also involves scaling of trees cleared from the road. However, in STRATIFIED, log scaling data are stratified by forest stand location. A volume per hectare is calculated for each stretch of road that crosses a single forest stand. This volume per hectare is then multiplied by the remaining area of the same forest stand scheduled for harvest one year later. The sum of all resulting estimated volumes per stand gives the total estimated cutting-volume for all cut-blocks adjacent to the studied road. A third method (MNR) was also used to estimate cut-volumes of the sites studied. This method represents the actual existing technique for estimating cutting volume in the province of Quebec. It involves summing the cut volume for all forest stands. The cut volume is estimated by multiplying the area of each stand by its estimated volume per hectare obtained from standard stock tables provided by the governement. The resulting total estimated volume per cut-block for all three methods was then compared with the actual measured cut-block volume (MEASURED). This analysis revealed a significant difference between MEASURED and MNR methods with the MNR volume estimate being 30 % higher than MEASURED. However, no significant difference from MEASURED was observed for volume estimates for the ROAD and STRATIFIED methods which respectively had estimated cutting volumes 19 % and 5 % lower than MEASURED. Thus the ROAD and STRATIFIED methods are good ways to estimate cut-block volumes after road right-of-way harvest for conditions similar to those examined in this study.