Gasoline Prices, State Gasoline Excise Taxes, and the Size of Urban Areas

A monocentric model of land use that assumes urban spatial size is determined by a set of exogenous variables is employed to test the hypothesis that urban land size is negatively related to gasoline prices. Using a model from Brueckner and Fansler (1983), the amount of urbanized land area is shown to be a function of population, income, agricultural land prices and commuting costs. The latter is measured as the price of gasoline, broken into two components; the amount of any state gasoline excise tax imposed plus the total price paid by consumers minus such a tax. Using 1990 data, an empirical model that defines the dependent variable as the average-sized urban area in each state is estimated. The results confirm that higher levels of population and income contribute to larger amounts of urbanized land area, while higher land prices are primarily associated with smaller urban size. Of importance to this study, the results indicate that states with higher gas prices, mainly due to increased state gasoline excise taxes, are associated with smaller urban size

An Analysis of the Interest Elasticity of Financial Asset Holdings by Income

A Keynesian money demand model is used to examine the interest elasticity of financial asset holdings by income level. In this model, once an individual receives income, they first make transactions, and any leftover income goes for speculative purposes. Since only speculative balances are assumed to change with interest rates, individuals with income used mainly for transactions purposes are theorized to have asset holdings that are unresponsive to interest rates, while higher income individuals with speculative balances are expected to be more responsive to interest rates. The results support the Keynesian model, as lower income households are found to have the smallest interest elasticity, and the estimated elasticity rises with income

Evaluating the Long-run Impacts of the 9/11 Terrorist Attacks on US Domestic Airline Travel

Although the US airline industry began 2001 with 24 consecutive profitable quarters, including net profits in 2000 totaling $7.9 billion, the impact of the 9/11 event on the industry was substantial. Whereas the recession that began in early 2001 signaled the end of profitability, the 9/11 terrorist attacks pushed the industry into financial crisis after air travel dropped 20% over the September–December 2001 period compared to the same period in 2000. Given the decline in domestic air travel, an important question is whether the detrimental impact of the attacks was temporary or permanent. That is, did airline travel return to the trend that existed prior to the terrorist attacks? There are theoretical reasons to the believe that it would not. Economists have long viewed travel-mode choices as the outcome of a comparison of opportunity costs and benefits. Thus, anything that permanently raises the opportunity cost of travel, holding benefits constant, should reduce the level of travel volume. To determine whether air travel was permanently reduced, we use econometric and time-series forecasting models to generate a counter-factual forecast of air travel volume in the absence of the terrorist attacks. These dynamic forecasts are compared to actual air travel levels to determine the impact of the terrorist attacks. The findings suggest that domestic air travel did not return to the levels that would have existed in the absence of the attack

Does Money Matter? An Empirical Investigation

This paper uses a simultaneous-equations model of the new consensus macroeconomic model to examine whether the inclusion of the money stock in the aggregate demand function improves the statistical fit of the model. The results indicate that the consensus model is accurate for the U.S. in that the inclusion of money does not increase the predictive power of the model. However, the results reveal that the estimated coefficients are more robust when money is included as an instrumental variable in the simultaneous equations consensus modelConsensus Macro Model; Monetary Policy; Phillips Curve; Taylor Rule

(WP 2010-09) Does Money Matter? An Empirical Investigation

This paper uses a simultaneous-equations model of the new consensus macroeconomic model to examine whether the inclusion of the money stock in the aggregate demand function improves the statistical fit of the model. The results indicate that the consensus model is accurate for the U.S. in that the inclusion of money does not increase the predictive power of the model. However, the results reveal that the estimated coefficients are more robust when money is included as an instrumental variable in the simultaneous equations consensus model

Do lower mortgage rates mean higher housing prices?

Much research has shown that mortgage rates exert a negative influence on housing prices. This study analyses the long- and short-run relationships between housing prices and mortgage rates using advanced nonstructural estimation methods. As expected, a bivariate specification and a four-variable housing demand specification both show that these variables have a long-run relationship, and that there is a rather inelastic response of housing prices to changes in mortgage rates. However, contrary to previous research, the results from Granger non-causality tests, impulse response functions and variance decompositions reveal that there is virtually no short-run influence from mortgage rates to housing prices.

Tax Rate Changes, Interest Rate Volatility, And The Decline In Velocity, 1981-1983

One of the most puzzling economic events in the U.S. during this decade has been the dramatic decline in the growth rate of the income velocity of money.  From the fourth quarter of 1981 to the second quarter of 1983, the velocity growth rate fell by nearly 4% as opposed to its 3% trend growth rate.  Traditional models have been unable to fully capture this unusual behavior of velocity, over predicting its rate of growth.  The present study is concerned with this over prediction problem and attempts to more accurately explain the decline in the velocity growth rate in the 1981-1983 period.  It examines the extent to which increased interest rate volatility in the early 1980’s and the Reagan tax cuts may be contributed to the decline of the velocity growth rate from 1981 to 1983