Carl Bonham

The Impact of 9/11 and Other Terrible Global Events on Tourism in the United States and Hawaii

This article reviews recent trends in travel and tourism in the United States and Hawaii to ascertain how the terrorist attacks of 9/11 and subsequent terrible global events affected tourism flows. United States tourism has not recovered fully from 9/11 and other international shocks; indeed, recovery may be a long way off. By contrast, Hawaii tourism is enjoying robust growth in the aftermath of 9/11 as growth in tourist arrivals from the mainland has offset declines in international visitors. We suggest that Hawaii’s current tourism boom is explained in part by the diversion of United States travel from foreign travel. The article demonstrates the usefulness of vector error correction models to generate dynamic visitor forecasts, which we use to determine whether tourism in Hawaii has recovered fully from 9/11 and other terrible international events. The article considers policy options for facilitating the recovery of international tourism to the United States.

To Aggregate, Pool, or Neither: Testing the Rational Expectations Hypothesis Using Survey Data

It is well known that, even if all forecasters are rational, unbiasedness tests using consensus forecasts are inconsistent because forecasters have private information. However, if all forecasters face a common realization, pooled estimators are also inconsistent. In contrast, we show that when predictions and realizations are integrated and cointegrated, microhomogeneity ensures that consensus and pooled estimators are consistent. Therefore, contrary to claims in the literature, in the absence of microhomogeneity, pooling is not a solution to the aggregation problem. We reject microhomogeneity for a number of forecasts from the Survey of Professional Forecasters. Therefore, for these variables unbiasedness can only be tested at the individual level.

Private versus Public Financing of State Destination Promotion

Until 1993 all U.S. state governments actively financed the promotion of travel to their states. In recent years, however, there has been growing public sentiment that governments should not directly engage in or fund tourism promotion. Colorado voters abolished their states tourism board in 1993, while four other states are also looking for ways to privately fund state travel promotion. This article examines whether current efforts to induce greater private funding of destination travel promotion are likely to succeed. It is suggested that a broad-based, dedicated travel industry promotion tax is an effective way to reduce free riding by travel businesses and increase private funding of destination promotion.

Intervention analysis with cointegrated time series: the case of the Hawaii hotel room tax.

Tourism taxes have become an important source of revenue or many tourist destinations in the USA. Among the most widely used is the hotel room tax, levied by 47 states and many localities. Room taxes are touted by proponents as a way to shift the local tax burden to non-residents, while the travel industry claims the levies significantly harm their competitiveness. Previous studies of room tax impacts have relied on ex ante estimates of demand and supply elasticities. In this study, we analyse the effect on hotel revenues of the Hawaii room tax using time series intervention analysis. We specify a time series model of revenue behaviour that captures the long-run cointegrating relationships among revenues and important income and relative price variables, as well as other short-run dynamic influences. We estimate the effect on Hawaii hotel room revenues of the 5% Hawaii hotel room tax introduced in January 1987. We find no evidence of statistically significant tax impacts.

Correct cointegration tests of the long-run relationship between nominal interest and inflation

The Fisher (1930) hypothesis suggests that a long run equilibrium relationship exists between the nonstationary series: nominal interest and expected inflation. Testing such a cointegrating relationship is complicated by the presence of the unobserved ex ante real rate of interest in residuals from the cointegrating regression. Assumptions concerning the stochastic properties of the expected real rate of interest are examined and two proxies for the ex ante real rate are employed in multivariate cointegration tests of the Fisher hypothesis.

In Search of a "Strictly Rational" Forecast

This paper proposes criteria for classifying time-series forecasts of inflation as weakly, sufficiently, strongly, and strictly rational. Forecasts taken from the ASA-NBER surveys, some well-known one-step-ahead forecasting techniques, and a novel variable length autoregressive moving average model are tested against these criteria. None of the forecasts series meets the criteria for strict rationality nor, even, the less demanding criteria for strong rationality. While agents forecast as best they can, their forecasts are not likely to meet stringent rationality criteria suggested by econometricians. Copyright 1991 by MIT Press.

Fragmentation and East Asia's Information Technology Trade

This article studies the growth and determinants of information technology (IT) trade in the Asia-Pacific region. We argue that the rise of IT trade must be understood within the context of increasing vertical fragmentation of production processes that has occurred over the past two decades. To evaluate this empirically, we estimate a set of pooled bilateral IT export equations for eight Asian countries, the USA and the EU, where foreign direct investment (FDI) inflows are introduced as a proxy for fragmentation. We apply a panel cointegration approach that allows for heterogeneity in short-run dynamics and in fixed effects. Consistent with production fragmentation, we find that the evolution of IT trade can be explained in part by traditional income and relative price effects but also by FDI inflows.

Forecasting Earnings Growth and Discount Rates: New Evidence From Time Series Analysis

Forensic economists can choose from a wide variety of forecasting techniques to predict earnings growth and discount rates when estimating the present value of damages in lost earnings cases. Michael Brookshire and Frank Slesnick (1991) surveyed forensic economists on their choice of forecasting methods for these two series and found that most economists use simple forecasting rules. A remarkable 72.6% of respondents predict earnings growth rates by taking the arithmetic or geometric mean of past rates, and 52.1% of respondents forecast discount rates by taking the mean of past rates? Economists forecast using sample means because the mean is simple to calculate and is easy to explain in judicial proceedings. On the other hand, such forecasts are often inconsistent with the stochastic properties of the data, thereby producing poor forecasts. Several economists (Harris, 1984; Hosek, 1982; Nowak, 1991; Pelaez, 1989; Parks, 1985; Schilling, 1985) have analyzed the stochastic properties of earnings growth and discount rates and have recommended a number of different forecasting methods other than the series’ mean. This paper extends their work by using recently developed statistical tests to evaluate the stochastic properties of several commonly used earnings growth rate and discount rate time series. We then use three different methods to forecast each time series. ARIMA forecasting techniques produce better forecasts of all series than the sample mean or the latest observation. Our empirical results lead us to the conclusion that economists should avoid using the mean of past rates to forecast future rates for discount and earnings growth rate series. Use of the sample mean is not only inconsistent with the stochastic properties of these time series, but another technique (an ARIMA model) performs significantly better. If the economist nonetheless chooses to use simple forecasting techniques, perhaps because they are easier to explain in judicial proceedings, forecasting accuracy can be markedly improved if the chosen methodology is consistent with the underlying stochastic properties of the time series. A simple rule of thumb is to use the most recent observation for nonstationary series and the sample mean for stationary series.

FORECASTING WITH MIXED-FREQUENCY FACTOR MODELS IN THE PRESENCE OF COMMON TRENDS

We analyze the forecasting performance of small mixed-frequency factor models when the observed variables share stochastic trends. The indicators are observed at various frequencies and are tied together by cointegration so that valuable high-frequency information is passed to low-frequency series through the common factors. Differencing the data breaks the cointegrating link among the series and some of the signal leaks out to the idiosyncratic components, which do not contribute to the transfer of information among indicators. We find that allowing for common trends improves forecasting performance over a stationary factor model based on differenced data. The “common-trends factor model†outperforms the stationary factor model at all analyzed forecast horizons. Our results demonstrate that when mixed-frequency variables are cointegrated, modeling common stochastic trends improves forecasts.