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One of the prominent features of the Natural Rubber (NR) market is its price variability, and the aim of this study is to project accurate short-term NR prices. This is accomplished by exploiting the use of forecasting techniques and information sets to seek the combination with the best forecasts, and exploring best ways of combining forecasts. We evaluate the relative performance of 19 models based upon three different forecasting techniques, and four information sets. In addition, we compare their forecasts with 13 other forecasts combined in various different ways, and taking the Naive forecast as benchmark. The generalised autoregressive conditional heteroscedasticity regression (or ARCH-type) models, though more complex, are generally better than the simpler regression models. In general, the performance of the various techniques seems to perform consistently well (or poorly) over the forecasting horizons, with alternations in performance due mainly to the type of information set used. We also adopted a simple trading rule to find out the economic values of our forecasts, and the results are most promising. Importantly, the forecasts generated from the alternative models developed in this study can potentially be beneficial to participants in the NR futures market.

Thesis (M. Phil.)--University of Hong Kong, 2006. / Title proper from title frame. Also available in printed format.

This thesis compares MIDAS regressions, introduced in Ghysels, Santa-Clara and Valkanov (2002), to the RAMSES model, used by the Riksbank,in forecasting Swedish quarterly output growth (GDP). Using Swedish GPDdata from 1993Q1 and onwards, we compare forecasts for the period 2010Q1to 2011Q4, and we show that for longer forecast horizons (commonly usedby the Riksbank for monetary policy decisions), such as two and three yearsahead in time, the MIDAS regressions clearly outperforms the RAMSESmodel. For shorter horizons the results are not conclusive.KEYWORDS: MIDAS, GDP, forecasting, Sveriges Riksbank

In the first essay, Distortions in the Neoclassical Growth Model: A Cross Country Analysis, I show that shocks that express themselves as total factor productivity and labor income taxes are comparably more synchronized than shocks that resemble distortions to the ability of allocating resources across time and states of the world. These two shocks are also the most important to model. Lastly, I document the importance of international channels of transmission for the shocks, given that these are spatially correlated and that international trade variables, such as trade openness correlate particularly well with them. The second essay is called Monetary Business Cycle Accounting for Sweden. Given that the analysis is focused in one country, I can extend the prototype economy to include a nominal interest rate setting rule and government bonds. As in the previous essay, distortions to the labor-leisure condition and total factor productivity are the most relevant margins to be modeled, now joined by deviations from the nominal interest rate setting rule. Also, distortions do not share a structural break during the Great Recession, but they do during the 1990’s.  Researchers aiming to model Swedish business cycles must take into account the structural changes the Swedish economy went through in the 1990’s, though not so during the last recession. The third essay, Consumer Confidence and Consumption Spending: Evidence for the United States and the Euro Area, we show that, the consumer confidence index can be in certain circumstances a good predictor of consumption. In particular, out-of-sample evidence shows that the contribution of confidence in explaining consumption expenditures increases when household survey indicators feature large changes, so that confidence indicators can have some increasing predictive power during such episodes. Moreover, there is some evidence of a confidence channel in the international transmission of shocks, as U.S. confidence indices help predicting consumer sentiment in the euro area.

Ten years (1997 - 2006) of summer (June, July, August) daytime (14 - 00 Z) Weather Surveillance Radar - 1988 Doppler data for Houston, TX were examined to determine the best radar-derived lightning forecasting predictors. Convective cells were tracked using a modified version of the Storm Cell Identification and Tracking (SCIT) algorithm and then correlated to cloud-to-ground lightning data from the National Lightning Detection Network (NLDN). Combinations of three radar reflectivity values (30, 35, and 40 dBZ) at four isothermal levels (-10, -15, -20, and updraft -10 degrees C) and a new radar-derived product, vertically integrated ice (VII), were used to optimize a radar-based lightning forecast algorithm. Forecasts were also delineated by range and the number of times a cell was identified and tracked by the modified SCIT algorithm. This study objectively analyzed 65,399 unique cells, and 1,028,510 to find the best lightning forecast criteria. Results show that using 30 dBZ at the -20 degrees C isotherm on cells within 75 km of the radar that have been tracked for at least 2 consecutive scan produces the best forecasts with a critical success index (CSI) of 0.71. The best VII predictor was 0.734 kg m-2 on cells within 75 km of the radar that have been tracked for at least 2 consecutive scans producing a CSI of 0.68. Results of this study further suggest that combining the radar reflectivity and VII methods can result in a more accurate lightning forecast than either method alone.

Petrochemicals industry is one of the major industries in modern economy. Almost all synthetic chemicals or materials are related. Ethylene is the main product of this industry, which is produced from naphtha cracker. Butadiene is a by-product of naphtha cracking, but it is the most important raw material of synthetic rubbers. Most synthetic rubber plants do not have their own butadiene plant. All the materials were supplied from up-stream crackers. Due to the unstable supply and price surge recently, the cost of butadiene is higher than 70% of the total production cost in synthetic rubbers manufacturing. Huge price fluctuation in butadiene caused a big financial loss in 2008. These economic situations have made the price prediction an important issue for synthetic rubber manufacturers. In this research, we reviewed the historical price data of crude oil, naphtha, ethylene, propylene, butadiene and MTBE from the upper stream of petrochemicals in supply and the price of nature rubber, synthetic rubbers in demand. This study intends to find the petrochemicals price variation factors and uses butadiene as example. ¡§Time series¡¨ and OLS methods are used to build price prediction models to explain the price fluctuation in the past. Our research has found that the monthly price of butadiene is highly related to last month¡¦s price of butadiene, crude oil price and even related to last 9 month butadiene price. This may be because of the regular economical circulation and naphtha crackers annual turn around. The price of spot trading is related to the last week¡¦s price of butadiene and its co-product propylene. Those findings are valuable for synthetic rubber manufacturer in raw material procurement.

Thesis (M.S.)--West Virginia University, 2001. / Title from document title page. Document formatted into pages; contains viii, 121 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 84-86).

Thesis (M.S.)--Naval Postgraduate School, 2002. / Thesis advisor(s): Russell L. Elsberry, Patrick A. Harr. Includes bibliographical references (p. 69). Also available online.