This Is AuburnElectronic Theses and Dissertations

Three Essays on Applied Economics




Durmaz, Nazif

Type of Degree



Agricultural Economics and Rural Sociology


This dissertation consists of three essays in housing, trade, and time series econometrics. The first essay empirically investigates cointegrating relation between housing prices and economic fundamental variables in the US housing market. Employing simple yet rigorous econometric techniques, the present paper finds strong evidence in favor of cointegrating relations in most US states when both the demand and supply side fundamental variables are included in the cointegrating regression. This casts doubt on the previous empirical work that reported weak or no cointegrating relation of housing prices with mostly demand-side fundamental variables, which may have a misspecification problem. Further, cointegrating vector estimates seem consistent with economic theories only when both side fundamental variables are used. The second essay estimates exchange rate elasticities of US cotton exports to China, Indonesia, Thailand, South Korea, and Taiwan, five textile producing cotton importers with floating or regularly adjusting exchange rates since the 1970s. A model is developed with US exports depending on the exchange rate, US production cost, mill use, and cotton inventories. The role of inventories in cotton consumption is examined in Southeast and East Asia countries with Asian financial crisis. Aggregating the five importers disguises exchange rate effects. The lesson is that exchange rate effects should be examined for each separate market. Changes in rates of depreciation have stronger effects than changes in exchange rates in the present sample. The third essay evaluates relative forecast performances of two bias-correction methods. The least squares (LS) estimator suffers from significant downward bias in autore-gressive models that include an intercept. By construction, the LS estimator yields the best in-sample t among a class of linear estimators notwithstanding its bias. Then, why do we need to correct for the bias? To answer this question, we evaluate the usefulness of the two popular bias correction methods, proposed by Hansen (1999) and So and Shin (1999), by comparing their out-of-sample forecast performances with that of the LS estimator. We find that bias-corrected estimators overall outperform the LS estimator. Especially, Hansen's grid bootstrap estimator combined with a rolling window method performs the best.