Three essays on empirical macroeconomics

• Main Author: Douglas, Christopher C
• Format: Dissertation
• Language: English
• Published: ProQuest Dissertations & Theses 01.01.2007
• Subjects: Adjustments; Asymmetry; Business costs; Business to business commerce; Central banks; Commerce-Business; Consumers; Copyright; Costs; Econometrics; Economics; Essays; Federal funding; Foreign exchange markets; Foreign exchange rates; Gasoline prices; Graduate students; Information processing; International finance; Intervention; Investigations; Learning; Macroeconomics; Market structure; Monetary policy; Pricing policies; Probability; Regression analysis; Retail stores; Service stations; Studies; Teaching; Variables; Wholesalers; Windfall profits
• ISBN: 0549239235; 9780549239239

Chapter 1, "What is the Best Way to Control for the Clustering in Central Bank Intervention Data?", investigates the "clustering" property of this data. That is, successive days of intervention are followed by successive days of no intervention. Clearly, this clustering contains information as to when an intervention will occur and must be dealt with in the time-series econometric specification. I apply two time series econometric specifications that are designed to control for this property, the Autoregressive Conditional Hazard (ACH) of Hamilton and Jordá and a modified version of the Autoregressive Conditional Multinomial (ACM) of Engle and Russell, both to test for traditional motivations for intervention and to find which model performs better. I find that the performance of the ACH is tied to the significance of the duration in a standard static model (such as a probit or logit). I show that the ACM outperforms the ACH in terms of goodness-of-fit, the significance of the duration in a logit model disappears with the inclusion of dynamics in the ACM model (a static ACM model reduces to a probit or logit), and find that the significance of explanatory variables is tied to the econometric specification. I argue that these results are relevant to applications using similarly clustered data. Chapter 2, "Why are Gasoline Prices Sticky? A Test of Alternative Models of Price Adjustment", applies the modified ACM to a unique data set consisting of daily price and cost observations for nine Philadelphia gasoline wholesalers to examine why gasoline wholesalers change their price less frequently than their costs change. Unlike Davis and Hamilton (who apply the ACH), I find statistically significant time dependence for all wholesalers. I use the results from Chapter 1 to illustrate why this is the case; the lagged duration is insignificant for all wholesalers but one. Comparing this finding of time dependence to competing theoretical models of price adjustment suggests that a menu cost model is not broadly consistent with the data. But, the time dependence is consistent with strategic considerations related to the idea of "fair pricing" as well as somewhat supportive of bounded rationality explanations related to the costs of obtaining and processing information. Chapter 3, "Dynamic Pricing and Asymmetries in Retail Gasoline Markets: What Can We Learn About Price Stickiness?" uses a data set consisting of daily observations of fifteen Philadelphia retail gasoline stations to examine the issues in Chapter 2 on the retail level. The retail price of gasoline changes less frequently than the wholesale price of gasoline. I find that a variable threshold model fits the data well, and the dynamics in a station's pricing decision are almost exclusively in the lower threshold. Prices are more flexible in the upward direction compared to the downward direction, as the distance between the upper and lower thresholds narrows as prices rise and widens as prices fall, and stations are more likely to make price increases rather than price decreases. I argue that this evidence is consistent with "search costs". Additionally, the dynamics and asymmetry differs for that of wholesalers as found in Chapter 2. I argue that differences in market structure can likely explain the difference.