Three Essays in Applied Econometrics: with Application to Natural Resource and Energy Markets

Abstract Chapter 1 This essay examines the effect of state renewable energy policies in inducing innovation and the spillover effect of these policies on innovation in neighboring states. The analysis is conducted with patent data related to renewable technology using wind power for the United States over the period 1983-2010. We run a panel data regression of a log transformation of states\u27 yearly patent counts on state renewable energy policies and spatially weighted average of renewable energy policies in neighboring states using the Tobit model with individual effects. The results show that renewable energy rules, regulation and mandates such as interconnection standards, net metering and renewable portfolio standard enacted in neighboring states have shown a statistically significant positive spillover effect in increasing the number of patent applications in that state. However, financial policies such as tax incentives and subsidy policies implemented by neighboring states have shown statistically significant negative effects on technological innovation within that state. Chapter 2 In this essay, we have conducted a Monte Carlo Study of the prediction performance of various nonparametric estimation methods for spatially dependent data, such as the nonparametric local linear kernel estimator, the Nadraya-Watson estimator, and the k-Nearest Neighbors method developed by Hallin et al. (2004b), Lu and Chen (2002), P.M. Robinson (2011) and Li and Tran (2009). With data sampled on a rectangular grid in a nonlinear random field, the results show that nonparametric local linear kernel method has the best performance in terms of mean squared prediction error. The Nadaraya-Watson estimation method also performs well. In general, these two nonparametric methods consistently outperform the k-Nearest Neighbors method and the maximum likelihood method regardless of the data generating process and sample size. However, the maximum likelihood method does not perform well because the spatial weight matrix can only be used to estimate linear structures while the true data generating process is nonlinear. This also gives some support to the idea of using nonparametric methods when various misspecification may exist either in the functional form or spatial weight matrix for spatially dependent data. We use these methods to predict county-level crop yields with spatially weighted precipitation. The results are generally consistent with the simulation results. The nonparametric local linear kernel estimator has the best prediction performance. The Nadaraya-Watson estimator also performs better than the k-Nearest Neighbors method and the maximum likelihood estimator. However, with an inverse distance weighting matrix, the maximum likelihood estimator outperforms the k-Nearest Neighbors method in predicting crop yield. Chapter 3 This essay uses the exceedances over high threshold model of Davidson and Smith (1990) to investigate the univariate tail distribution of the returns on various energy products such as Crude Oil, Gasoline, Heating Oil, Propane and Diesel. The bivariate threshold exceedance model of Ledford and Tawn (1996) is also used to study the tail dependence between returns on various pairs of selected energy products. Tail index estimates for univariate threshold exceedance models show that these returns generally have fat tails similar to those of a Student\u27s t-Distribution with 2 to 5 degrees of freedom except that for Crude Oil where the tail index estimates are closer to that of a normal distribution. We also estimate the tail dependence index for four pairs of energy products, crude oil/gasoline, crude oil/heating oil, crude oil/propane, crude oil/diese. The correlation coefficients implied by the dependence index estimates show that correlations conditional on threshold exceedance are generally higher than the unconditional correlation between crude oil/heating oil and crude oil/gasoline. However, there is some variation in the implied correlation between crude oil/propane and crude oil/diesel. Whether the extreme correlation will be higher or lower than the unconditional correlation depends on the threshold chosen

Natural disasters and household welfare : evidence from Vietnam

As natural disasters hit with increasing frequency, especially in coastal areas, it is imperative to better understand how much natural disasters affect economies and their people. This requires disaggregated measures of natural disasters that can be reliably linked to households, the first challenge this paper tackles. In particular, a methodology is illustrated to create natural disaster and hazard maps from first hand, geo-referenced meteorological data. In a second step, the repeated cross-sectional national living standard measurement surveys (2002, 2004, and 2006) from Vietnam are augmented with the natural disaster measures derived in the first phase, to estimate the welfare effects associated with natural disasters. The results indicate that short-run losses from natural disasters can be substantial, with riverine floods causing welfare losses of up to 23 percent and hurricanes reducing welfare by up to 52 percent inside cities with a population over 500,000. Households are better able to cope with the short-run effects of droughts, largely due to irrigation. There are also important long-run negative effects, in Vietnam mostly so for droughts, flash floods, and hurricanes. Geographical differentiation in the welfare effects across space and disaster appears partly linked to the functioning of the disaster relief system, which has so far largely eluded households in areas regularly affected by hurricane force winds.Natural Disasters,Hazard Risk Management,Disaster Management,Climate Change Mitigation and Green House Gases,Adaptation to Climate Change

Forecasting of electricity prices in the Spanish electricity market using machine learning tools

The objective of this research assignment was to forecast electricity prices in the Spanish electricity market using three different machine learning techniques: k-nearest neighbours, support vector regression and artificial neural networks. The achieved results were compared and the quality of developed models was evaluated. The project was implemented in Python3.Incomin

Pattern-based prediction of islanded power grid frequency

As part of achieving the climate goals from the Paris Agreement set by a united world community, the transition from non-renewable energy sources and electrification of transport and industrial sectors is central. The power grid faces new challenges as emerging renewable energy sources such as solar and wind fluctuate more than the more controllable traditional coal, gas, oil, and hydropower. In addition to more fluctuations on the supply side, an increase in emerging consumers like electric cars and data centers strengthens the need for good tools to maintain grid stability. Therefore, forecasting models and analysis to investigate both large and decentralized power systems’ dynamics are necessary to maintain control and reliability. Expanding upon a pattern-based prediction model called the Weighted-nearest neighbors (WNN) predictor, this thesis investigates the predictability of the power grid frequency for different European islanded power grids through several approaches. The selected islands include Ireland, the Balearic Islands, Iceland, and the Faroe Islands, with the Nordic region as a basis for comparison. The WNN predictor is successfully applied to all regions and performs 60 min predictions better than average daily profiles except for Iceland with its stochastic behavior. The Balearic Islands are the most deterministic region with precise predictions, while Ireland performs slightly worse than the Nordic region. The Faroe Islands exhibit similar performance to Iceland, but with significantly less data available. With varying population and geographical size, the regions cover the range of possible future grids, consisting of larger synchronous areas to small, isolated island systems and microgrids. All regions exclusively show that predictions improve with more data available. The predictor outperforms daily profiles with about a month of data available and with even less for more predictable regions. When electricity generation time series are included in an extended model approach, the performance slightly increases for parts of the predicted hour for several features, despite low time resolution and partially poor quality of the additional data. This suggests that there is further information to be gleaned from other power grid time series to improve the prediction of the power grid frequency.Som et ledd i å nå klimamålene fra Parisavtalen satt av et samlet verdenssamfunn, står overgangen fra ikke-fornybare energikilder og elektrifisering av transport- og industrisektorer sentralt. Kraftnettet står overfor nye utfordringer ettersom økende kraftproduksjon fra fornybare energikilder som sol og vind varierer mer enn regulerbare tradisjonelle kilder som kull, gass, olje og vannkraft. I tillegg til mer uregulerbar kraftproduksjon, styrkes behovet for gode verktøy for å opprettholde nettstabilitet gjennom blant annet økende energibehov til elbiler og satsning på̊ datasentre. Derfor er prognosemodeller og analyser for å undersøke både store og desentraliserte kraftsystemers dynamikk nødvendig for å videreføre kontroll og pålitelighet. Denne oppgaven tar utgangspunkt i, samt modifiserer, en eksisterende mønsterbasert prediksjonsmodell kalt Weighted-nearest neighbors (WNN), og undersøker hvorvidt strømnettets frekvens for ulike europeiske øybaserte strømnett er predikerbar. Oppgaven tar for seg øyene Irland, Balearene, Island og Færøyene, med Norden som sammenligningsgrunnlag. 60 min predikeringer med WNN-modellen gir bedre resultater enn gjennomsnittlig daglige frekvensprofiler for alle undersøkte regioner, bortsett fra Island med sine stokastiske trekk. Balearene er den mest deterministiske regionen med presise prediksjoner, mens Irland presterer litt dårligere enn nokså̊ gjennomsnittlige Norden. For Færøyene vises lignende resultater som Island, men med betydelig mindre data tilgjengelig. Med varierende befolkning og geografisk størrelse er regionene en naturlig representasjon av framtidens potensielle kraftsystemer som kan variere fra kontinentale synkronområder til små̊, isolerte øysystemer og mikronett. Alle regioner viser utelukkende at prediksjoner forbedres med mer tilgjengelig data. Generelt predikerer WNN-modellen bedre enn gjennomsnittlig daglige frekvensprofiler fra og med omtrent én måned med data tilgjengelig, og med enda mindre data for de mest predikerbare regionene. Til slutt inkluderes data for elektrisitetsproduksjon fra ulike energikilder i en utvidet versjon av modellen, og forbedrer resultatet for deler av den predikerte timen ytterligere, til tross for lav tidsoppløsning og delvis dårlig kvalitet på̊ produksjonsdataen. Dette tyder på̊ at det er ytterligere informasjon å hente fra annen kraftnettrelatert data for å forbedre prediksjonen av strømnetts frekvens.M-M

Pemilihan kerjaya di kalangan pelajar aliran perdagangan sekolah menengah teknik : satu kajian kes

This research is a survey to determine the career chosen of form four student in commerce streams. The important aspect of the career chosen has been divided into three, first is information about career, type of career and factor that most influence students in choosing a career. The study was conducted at Sekolah Menengah Teknik Kajang, Selangor Darul Ehsan. Thirty six form four students was chosen by using non-random sampling purpose method as respondent. All information was gather by using questionnaire. Data collected has been analyzed in form of frequency, percentage and mean. Results are performed in table and graph. The finding show that information about career have been improved in students career chosen and mass media is the main factor influencing students in choosing their career