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Essays on Trade Agreements, Agricultural Commodity Prices and Unconditional Quantile Regression

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dc.contributor.advisor Ker, Alan
dc.contributor.author Li, Na
dc.date.accessioned 2014-01-03T16:43:49Z
dc.date.available 2014-01-03T16:43:49Z
dc.date.copyright 2013
dc.date.created 2013-12-19
dc.date.issued 2014-01-03
dc.identifier.uri http://hdl.handle.net/10214/7741
dc.description.abstract My dissertation consists of three essays in three different areas: international trade; agricultural markets; and nonparametric econometrics. The first and third essays are theoretical papers, while the second essay is empirical. In the first essay, I developed a political economy model of trade agreements where the set of policy instruments are endogenously determined, providing a rationale for countervailing duties (CVDs). Trade-related policy intervention is assumed to be largely shaped in response to rent seeking demand as is often shown empirically. Consequently, the uncertain circumstance during the lifetime of a trade agreement involves both economic and rent seeking conditions. The latter approximates the actual trade policy decisions more closely than the externality hypothesis and thus provides scope for empirical testing. The second essay tests whether normal mixture (NM) generalized autoregressive conditional heteroscedasticity (GARCH) models adequately capture the relevant properties of agricultural commodity prices. Volatility series were constructed for ten agricultural commodity weekly cash prices. NM-GARCH models allow for heterogeneous volatility dynamics among different market regimes. Both in-sample fit and out-of-sample forecasting tests confirm that the two-state NM-GARCH approach performs significantly better than the traditional normal GARCH model. For each commodity, it is found that an expected negative price change corresponds to a higher volatility persistence, while an expected positive price change arises in conjunction with a greater responsiveness of volatility. In the third essay, I propose an estimator for a nonparametric additive unconditional quantile regression model. Unconditional quantile regression is able to assess the possible different impacts of covariates on different unconditional quantiles of a response variable. The proposed estimator does not require d-dimensional nonparametric regression and therefore has no curse of dimensionality. In addition, the estimator has an oracle property in the sense that the asymptotic distribution of each additive component is the same as the case when all other components are known. Both numerical simulations and an empirical application suggest that the new estimator performs much better than alternatives. en_US
dc.description.sponsorship the Canadian Agricultural Trade Policy and Competitiveness Research Network, the Structure and Performance of Agriculture and Agri-products Industry Network, and the Institute for the Advanced Study of Food and Agricultural Policy. en_US
dc.language.iso en en_US
dc.subject Trade agreements en_US
dc.subject political economy en_US
dc.subject contracting cost en_US
dc.subject GARCH en_US
dc.subject volatility en_US
dc.subject value at risk en_US
dc.subject normal mixture en_US
dc.subject out-of-sample en_US
dc.subject EM algorithm en_US
dc.subject quantile regression en_US
dc.subject unconditional quantile en_US
dc.subject nonparametric regression en_US
dc.subject additive model en_US
dc.title Essays on Trade Agreements, Agricultural Commodity Prices and Unconditional Quantile Regression en_US
dc.type Thesis en_US
dc.degree.programme Food, Agriculture and Resource Economics en_US
dc.degree.name Doctor of Philosophy en_US
dc.degree.department Department of Food, Agricultural and Resource Economics en_US


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