CO2 Emissions, GDP and trade : a panel cointegration approach

This paper examines the relationships among per capita CO2 emissions, per capita GDP and international trade based on panel data sets spanning the period 1960-2008: one for 150 countries and the others for sub-samples comprising OECD and Non-OECD economies. We apply panel unit root and cointegration tests, and estimate a panel error correction model. The results from the error correction model suggest that there are long-term relationships between the variables for the whole sample and for Non-OECD countries. Finally, Granger causality tests show that there is bi-directional short-term causality between per capita GDP and international trade for the whole sample and between per capita GDP and CO2 emissions for OECD countries

The Passionate Combining Entrepreneurs

Entrepreneurs are portrayed as salient drivers of regional development and for a number of years nascent entrepreneurs have been studied in a large number of countries as part of the Global Entrepreneurship Monitor project and the Panel Study of Entrepreneurial Dynamics. Scholars have devoted much effort to investigating factors that determine how individuals engage in entrepreneurial activities, with most of the discussion limited to business start-ups. However, since this type of project does not follow identical nascent entrepreneurs over time, limited knowledge exists about their development and whether they stay in this nascent phase for a long time. In practice, it is common for entrepreneurs to run a business and at the same time work in wage work, so-called combining entrepreneurs. In Sweden, almost half of all business owners combine wage work with a business. However, not all combining entrepreneurs will eventually decide to leave the wage work and invest fully in the business. Consequently, much research has focused on the first step of entering entrepreneurship full time, but less has focused on the second step, the transition from the combining phase to full-time self-employment. The aim of this thesis is therefore to contribute to the theory of entrepreneurship by gaining a deeper understanding of combining entrepreneurs and their motives and intentions.   In the context of combining entrepreneurs, the theory of identity, resources and choice overload has been used to examine how entrepreneurs’ age (when starting the business), entrepreneurial tenure (the length of engagement in the side-business), hours spent (weekly involvement in the side-business), involvement in entrepreneurial teams (leading the business with one or more partners) and involvement in networks (business networks) influence their passion for engaging in entrepreneurship while sustaining wage work. Different categories of combining entrepreneurs and their intentions have also been examined.   A survey was administered to 1457 entrepreneurs within the creative sector in two counties in Sweden (Gävleborgs County and Jämtlands County). Since there were no separate mailing lists to only combining entrepreneurs, the survey was sent to all entrepreneurs within the chosen industry and counties. The total response rate was 33.5 percent and of them 57.6 percent combined, yielding 262 combining entrepreneurs who answered the questionnaire. The survey was then followed up with eight focus group interviews and two single interviews to validate the answers from the questionnaire.   The results indicate three types of combining entrepreneurs: nascent – with the intention to leave the combining phase for a transition into full-time self-employment, lifestyle – with the intention to stay in the combining phase, and occasional – with the intention to leave the combining phase for full-time wage work and close down the business. Transitioning fully to self-employment increases with the individual’s age. Also, a positive interactive effect exists with involvement in entrepreneurial networks. The results also indicate that the ability to work with something one is passionate about is the top motive for combining wage work with a side-business. Passion is also more likely to be the main motive behind the combining form among individuals who are older at business start-up, but passion is less likely to be the main motive behind the combining form among individuals who spend more time on the business. The longer the individual has had the side-business, the less likely passion is the main motive behind the combining form, and passion is less likely to be the main motive among those who are part of an entrepreneurial team.

SOLAR PANEL MODELING ON SUITABLE ROOFS AT COLBY

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Solar Panel Modeling on Suitable Roofs at Colby

With the recent construction of Colby Green and the current plans for the construction of several new buildings, the total area for future development on campus has declined. The goal of this study was to illustrate existing campus development and to determine where future growth could occur. GIS was used in determining the different soil systems on campus, the current use of the land, and the boundaries of the Colby property. The project shows what potential obstacles the college will have in attempting to expand the campus and proposes where the best options are for construction.

Using Spatiotemporal Methods to Fill Gaps In Energy Usage Interval Data

Researchers analyzing spatiotemporal or panel data, which varies both in location and over time, often find that their data has holes or gaps. This thesis explores alternative methods for filling those gaps and also suggests a set of techniques for evaluating those gap-filling methods to determine which works best.

A stochastic discount factor approach to asset pricing using panel data

Using the Pricing Equation, in a panel-data framework, we construct a novel consistent estimator of the stochastic discount factor (SDF) mimicking portfolio which relies on the fact that its logarithm is the ìcommon featureîin every asset return of the economy. Our estimator is a simple function of asset returns and does not depend on any parametric function representing preferences, making it suitable for testing di§erent preference speciÖcations or investigating intertemporal substitution puzzles.

A panel data approach to economic forecasting: the bias-corrected average forecast

In this paper, we propose a novel approach to econometric forecasting of stationary and ergodic time series within a panel-data framework. Our key element is to employ the (feasible) bias-corrected average forecast. Using panel-data sequential asymptotics we show that it is potentially superior to other techniques in several contexts. In particular, it is asymptotically equivalent to the conditional expectation, i.e., has an optimal limiting mean-squared error. We also develop a zeromean test for the average bias and discuss the forecast-combination puzzle in small and large samples. Monte-Carlo simulations are conducted to evaluate the performance of the feasible bias-corrected average forecast in finite samples. An empirical exercise based upon data from a well known survey is also presented. Overall, theoretical and empirical results show promise for the feasible bias-corrected average forecast.

A panel data approach to economic forecasting: the bias-corrected average forecast

In this paper, we propose a novel approach to econometric forecasting of stationary and ergodic time series within a panel-data framework. Our key element is to employ the bias-corrected average forecast. Using panel-data sequential asymptotics we show that it is potentially superior to other techniques in several contexts. In particular it delivers a zero-limiting mean-squared error if the number of forecasts and the number of post-sample time periods is sufficiently large. We also develop a zero-mean test for the average bias. Monte-Carlo simulations are conducted to evaluate the performance of this new technique in finite samples. An empirical exercise, based upon data from well known surveys is also presented. Overall, these results show promise for the bias-corrected average forecast.

A panel data approach to economic forecasting: the bias-corrected average forecast

In this paper, we propose a novel approach to econometric forecasting of stationary and ergodic time series within a panel-data framework. Our key element is to employ the (feasible) bias-corrected average forecast. Using panel-data sequential asymptotics we show that it is potentially superior to other techniques in several contexts. In particular, it is asymptotically equivalent to the conditional expectation, i.e., has an optimal limiting mean-squared error. We also develop a zeromean test for the average bias and discuss the forecast-combination puzzle in small and large samples. Monte-Carlo simulations are conducted to evaluate the performance of the feasible bias-corrected average forecast in finite samples. An empirical exercise, based upon data from a well known survey is also presented. Overall, these results show promise for the feasible bias-corrected average forecast.

Correcting the fixed-effect estimator for endogenous switching

In this paper, we propose a two-step estimator for panel data models in which a binary covariate is endogenous. In the first stage, a random-effects probit model is estimated, having the endogenous variable as the left-hand side variable. Correction terms are then constructed and included in the main regression.