In this dissertation we proposed two generalizations of the Second-Order Least Squares (SLS) approach in two popular dynamic econometrics models. The first one is the regression model with time varying nonlinear mean function and autoregressive conditionally heteroskedastic (ARCH) disturbances. The second one is a linear dynamic panel data model. We used a semiparametric framework in both models where the SLS approach is based only on the first two conditional moments of response variable given the explanatory variables. There is no need to specify the distribution of the error components in both models. For the ARCH model under the assumption of strong-mixing process with finite moments of some order, we established the strong consistency and asymptotic normality of the SLS estimator. It is shown that the optimal SLS estimator, which makes use of the additional information inherent in the conditional skewness and kurtosis of the process, is superior to the commonly used quasi-MLE, and the efficiency gain is significant when the underlying distribution is asymmetric. Moreover, our large scale simulation studies showed that the optimal SLSE behaves better than the corresponding estimating function estimator in finite sample situation. The practical usefulness of the optimal SLSE was tested by an empirical example on the U.K. Inflation. For the linear dynamic panel data model, we showed that the SLS estimator is consistent and asymptotically normal for large N and finite T under fairly general regularity conditions. Moreover, we showed that the optimal SLS estimator reaches a semiparametric efficiency bound. A specification test was developed for the first time to be used whenever the SLS is applied to real data. Our Monte Carlo simulations showed that the optimal SLS estimator performs satisfactorily in finite sample situations compared to the first-differenced GMM and the random effects pseudo ML estimators. The results apply under stationary/nonstationary process and wih/out exogenous regressors. The performance of the optimal SLS is robust under near-unit root case. Finally, the practical usefulness of the optimal SLSE was examined by an empirical study on the U.S. airfares.

Bostadsmarknaden är en av de största tillgångsmarknaderna i ett land varpå förändringar i bostadspriserna får långt gångna konsekvenser för det enskilda hushållet, det finansiella systemet och samhällsekonomin i stort. Flertalet tidigare studier har analyserat den svenska bostadsmarknaden utifrån ett storstadsperspektiv alternativt jämfört Sveriges bostadsmarknad mot andra länder. Vi har identifierat att studiet kring vad som bestämmer prisnivån på regionala bostadsmarknader i Sverige är tämligen oexploaterat varför avsikten med den här studien är att analysera bestämningsfaktorer till de svenska bostadspriserna på länsnivå. Sålunda är ett bidragande mål med denna studie att tillföra en bättre förståelse för dynamiken på den svenska bostadsmarknaden. I studien använder vi multipel regression där vi bearbetar paneldata med en Fixed Effect Model. Ett flertal förtester har gjorts för att få fram den mest tillförlitliga modellen i vilken vi skattat bostadspriserna utifrån teoretiskt belagda förklaringsvariabler. De slutsatser vi har dragit är att disponibel inkomst, befolkningstäthet och sysselsättningsgrad kan förklara bostadspriserna på länsnivå med en procents signifikansnivå. Skillnaden i bostadspriserna mellan länen har relativt sett ökat över tidsperioden för studien. Avslutningsvis diskuteras uppvisade avvikelser mellan de verkliga bostadspriserna och de skattade bostadspriserna vilka kan förklaras av att bostadsmarknaden är känslostyrd med inslag av spekulationer. / The housing market is one of the greatest assets markets in a given country. Therefore, changes in housing prices have a big impact on the single household, the financial system and the economic system as a whole. Due to the housing markets vital role in the society, many scientific studies have been done with the purpose of enlighten and discover the dynamics of the Swedish housing market. The focuses in these earlier studies have more than often taken a metropolitan perspective or compared the Swedish housing market with other countries. However, this study divides the Swedish housing market into regional county level with the purpose of analyzing determinants of housing prices due to county specific variables. By analyzing the housing prices due to county specific factors a contributing goal with this study is to deepen the understanding about the dynamics in the Swedish housing market. In this study we have used multiple regressions in order to work with panel data. The Fixed Effect Model fitted our purpose well which is why that kind of model was used in order to estimate the housing prices for every single Swedish county. The conclusions drawn in this study are that disposable income, people density and employment rate are all statistically significant on one percent level in order to explain the housing price at state level. We have also discovered that, during the observed period, the relative differences in housing prices between the different states have increased. Finally, the differences found between the real housing prices and the estimated housing prices, can be explained by the assumption that the housing market is driven by emotions and speculations.

Several studies have examined the effect of remittances on economic growth,poverty, education, and governance, among other factors, in developing countrieswith inconclusive results. Using annual panel data of 99 developing countries invarious empirical models, this study aim to answer the question how remittances affect a broader aspect of development using the Human Development Index asdependent variable. The findings indicate that there is a positive relationship between remittances and the level of human development in developing countries.

Paskutiniais metais aktualiu klausimu tampa inovacijų modeliavimas. Pasirodant kiekvienai naujovei, verslininkams reikia suprasti ir prognozuoti rinkos vystymąsi, kad galėtų numatyti pardavimų eigą, planuoti pardavimų kiekius ir atlikti finansinį planavimą. Šiame darbe tyrinėsime kompiuterių skverbties koeficientus (penetration rate) 23 skirtingose pasaulio šalyse ir mėginsime parinkti geriausiai prognozavimui tinkantį modelį. Skverbties koeficientas paprastai parodo prekės ženklo ar produkto kategorijos populiarumą. Jis apskaičiuojamas dalinant žmonių, kurie perka kažkokią prekę ar paslaugą, skaičių iš tos rinkos visos populiacijos skaičiaus tam tikru periodu. Taigi, skverbties koeficientas yra procentas žmonių populiacijoje, kurie per stebimą periodą nusipirko tam tikrą prekę ar paslaugą. Marketinge dažnai yra spendžiama problema: didinti pardavimus stengiantis privilioti klientus iš konkurentų ar plečiant potencialių pirkėjų populiaciją, t.y. einant į naujas rinkas. Darbo pradžioje bus pristatyti panelinių duomenų vertinimo būdai, analizei reikalinga teorinė dalis, kai kurių reikalingų statistikų skaičiavimas. Vėliau pereisime prie pagrindinės darbo dalies - geriausio modelio parinkimo. Pirmiausia bus nagrinėjami paprasčiausi tiesinio trendo modeliai, įtraukiant autoregresijos procesą bei egzogeninius kintamuosius. Toliau įvairiais metodais mėginsime modeliuoti difuzijos procesą. Bandysime jį aproksimuoti kvadratiniu trendu, taikyti logistinį bei Gompertz modelius ir... [toliau žr. visą tekstą] / Lately the modelling of innovation process is a constantly recurring problem. As with any new service for managers it is important to understand and to able to forecast the market developement for strategic, capacity and financial planning purposes. This master thesis analyses computer penetration data in 23 different countries and tries to propose the best fitting model for forcasting. Penetration rate usually shows the popularity of a brand or a product. It is given as a percentage of the countries’ households who have bought that particular brand or product at least once within a defined period of time. In marketing there is a decision to make: enlarge sales by trying to attract more costumers in the sales region or by expanding the population of potential buyers, i.e. by expanding to other emerging markets. In the beginning of this thesis we will introduce some basic information about the panel data and some approaches of the theory needed for estimation. Later on we will pass to the main part of the work which is devoted to choosing the model. At first, we analyze some linear trend models with autoregression terms and exogenous variables. Then we examine a diffusion process by approximating it with a quadratic trend, logistic and Gompertz models. Also some basic information about the Bass diffusion model will be provided.

This study examines the determinants of economic growth at sub-national level in South Africa, and investigates cross-locality medium-term (five-year) growth rate differentials between 354 magisterial districts. The period in question is 1998 to 2002. A dynamic panel data regression model is used that includes measures of geography (distance and natural resources) as well as recent estimates of physical and human capital. It is found that the significant determinants of local economic growth are distance from internal markets, human capital, export propensity, and the capital stock of municipalities (reflecting institutional quality and governance on local government level). Distance from international harbours, as a measure of transport costs, and urban agglomeration (or density) affects growth indirectly through its significant effect on the ability of a region to export. Overall, these results indicate that geography is important for economic growth, independent of its effects in institutions. Bearing in mind the medium-term focus of the work, no evidence of absolute convergence could be found over a five-year period, rather the tentative evidence suggests slow beta convergence. / Thesis (Ph.D. (Economics))--North-West University, Potchefstroom Campus, 2005.

This study provides a systematic description of the nature and evolution of major transactions in the U.S. apparel sector, using a theory that applies across sectors. This research investigates the determinants of the existence and magnitude of major inter-firm transactions, relying on a unique longitudinal dataset of over 2,000 of the largest transactional (buy-sell) relations between publicly traded firms in the U.S. apparel sector. The results indicate the importance of inter-firm complementarity, rather than inter-firm similarity, in explaining the sector architecture; thus contributing to the future improvement of industry classification systems. This study also contributes to a deeper understanding of the apparel sector focusing on the change in the relative importance of manufacturing activities versus service activities and in the involvement of firms from the outside apparel sector. Implications of inter-firm transactions are discussed regarding industry policies, and human and environmental welfare in manufacturing and raw materials industries.

This paper investigates the impact of knowledge capital stocks on total factor productivity through the lens of the knowledge capital model proposed by Griliches (1979), augmented with a spatially discounted cross-region knowledge spillover pool variable. The objective is to shift attention from firms and industries to regions and to estimate the impact of cross-region knowledge spillovers on total factor productivity (TFP) in Europe. The dependent variable is the region-level TFP, measured in terms of the superlative TFP index suggested by Caves, Christensen and Diewert (1982). This index describes how efficiently each region transforms physical capital and labour into output. The explanatory variables are internal and out-of-region stocks of knowledge, the latter capturing the contribution of cross-region knowledge spillovers. We construct patent stocks to proxy regional knowledge capital stocks for N=203 regions over the 1997- 2002 time period. In estimating the effects we implement a spatial panel data model that controls for the spatial autocorrelation due to neighbouring regions and the individual heterogeneity across regions. The findings provide a fairly remarkable confirmation of the role of knowledge capital contributing to productivity differences among regions, and add an important spatial dimension to the discussion, by showing that productivity effects of knowledge spillovers increase with geographic proximity. (authors' abstract)

Bolagsskattesänkningen genomfördes den 1 januari 2013, målet med sänkningen var att stimulera Sveriges tillväxt då en sänk bolagsskatt sägs öka investeringsviljan. Med sänkningen ville man också minska incitamenten för företag att flytta sina verksamheter till lågskatteländer. Att bolagsskattesatsen sänktes väckte ett intresse som skapade denna uppsats att undersöka vilka faktorer det är som styr bolagsskattesatsen, till exempel, i en liten öppen ekonomi som den svenska.  Teorin grundar sig i kapitalstruktur och finansieringsbeslut. Faktorer som anses påverka bolagsskatten och som valts ut är utländska direktinvesteringar, öppenhet mot kapitalflöde och BNP per Capita. Datainsamling har skett sekundärt och bearbetats i det analytiska programmet R. Med hjälp av R och statistiska metoder har det genomförts paneldataanalys och regressionsanalys. Svaret är kort och gott, ja, faktorerna påverkar bolagsskattesatsen. Det visar att ett land som är större geografiskt och har en hög öppenhet för kapitalrörelser tenderar att ha en lägre bolagsskattesats och att det tycktes vara ett bra val av Sverige att justera bolagsskatten. / The goal with lowering of the Swedish corporate tax rate the 1st of January 2013 was to stimulate the Swedish growth, as a lower corporate tax rate is said to increase the will to invest. The incentives for corporations to move to low tax countries was also thought to be decreased due to the reduction.  When the tax rate was reduced an interest arise to examine which factors influence the tax rate, as for example, for a small economy as the Swedish. Theory presented is based on capital structure and finance decisions. The variables chosen is FDI, Openness towards capital flow and BNP per capita. Collection of data is secondary and has been analysed in the statistic program R with focus on panel data and regression analysis. The answer is for short, yes, the variables do indeed influence the corporate tax rate. Countries whom are larger geographically and has a high openness towards capital movement are more likely to have a lower corporate tax rate and it seemed to be a good choice of the Swedish parliament to lower the corporate tax rate.

Spillover phenomena are usually statistically estimated on the basis of regional and temporal panel data. In this paper we review and investigate exploratory and confirmatory statistical panel data techniques. We illustrate the methods by calculations in the stetting of the well known Research and Development Spillover study by Coe and Helpman (1995). It will be demonstrated that alternative estimation techniques that are well compatible with the data can lead to opposite conclusions. (author's abstract) / Series: Working Papers Series "Growth and Employment in Europe: Sustainability and Competitiveness"

Employee turnover is a fairly common phenomenon across organizations throughout the globe, which creates both direct and indirect costs to companies (Lambert et al., 2012). Though numerous authors have investigated the problem, only a small number have studied the Canadian labour market. Furthermore, few have examined how various hiring or screening tests during the hiring process affect worker attrition. The thesis aims to complement existing research about employee voluntary turnover (vs. involuntary turnover) and retention by further investigating some of the root causes and potential solutions from a Canadian perspective. Using longitudinal data from the Workplace and Employee Survey (WES) supplied by Statistics Canada through an 8-year period, it explores 5 hypotheses relating to the initial hiring process (ten screening tests), the gender and marital status of employees, compensation, and employees’ seniority in the company. The survey datasets are based on respondents of, on average, 6,268 companies and 20,387 corresponding workers from 1999 to 2006. Logit and probit regression models are employed for the empirical tests. The results are surprising, and seem to differ from most studies in other countries. In Canada, it appears wage has no effect on workers’ turnover at all, employee engagement programs negatively affect workers’ decisions to stay, women are more likely to quit than men are, married employees are no more likely to quit than anyone else, children seem to have no impact on employee attrition, and workers with lower status in the company are more likely to stay. The concluding chapter discusses implications of these findings and how they might help Canadian organizations deal with employee voluntary turnover.