Products: Fuleky, Peter
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Forecasting in a Mixed Up World: Nowcasting Hawaii Tourism
We evaluate the short term forecasting performance of methods that systematically incorporate high frequency information via covariates. Our study provides a thorough introduction of these methods. We highlight the distinguishing features and limitations of each tool and evaluate their forecasting performance in two tourism-specific applications. The first uses monthly indicators to predict quarterly tourist arrivals to Hawaii; the second predicts quarterly labor income in the accommodations and food services sector. Our results indicate that compared to the exclusive use of low frequency aggregates, including timely intra-period data in the forecasting process results in significant gains in predictive accuracy. Anticipating growing popularity of these techniques among empirical analysts, we present practical implementation guidelines to facilitate their adoption.
Common correlated effects and international risk sharing
Existing studies of international risk sharing rely on the highly restrictive assumption that all economies are characterized by symmetric preferences and uniform transmission of global shocks. We relax these homogeneity constraints by modeling aggregate and idiosyncratic fluctuations as unobserved components, and we use Pesaran's (2006) common correlated effects estimator to control for common factors and cross-sectional heterogeneity. We compare the proposed approach with the conventional ones using data from Penn World Table 9.0 for 120 countries. While we do not detect a significant increase in risk sharing during the last four decades, our results confirm that consumption is only partially smoothed internationally and risk sharing is directly related to the level of development.
Estimating Demand Elasticities in Non-Stationary Panels: The Case of Hawai‘i Tourism
It is natural to turn to the richness of panel data to improve the precision of estimated tourism demand elasticities. However, the likely presence of common shocks shared across the underlying macroeconomic variables and across regions in the panel has so far been neglected in the tourism literature. We deal with the effects of cross-sectional dependence by applying Pesaran’s (2006) common correlated effects estimator, which is consistent under a wide range of conditions and is relatively simple to implement. We study the extent to which tourist arrivals from the US Mainland to Hawaii are driven by fundamentals such as real personal income and travel costs, and we demonstrate that ignoring cross-sectional dependence leads to spurious results.
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.
Published Version: Peter Fuleky and Carl S. Bonham (2015). FORECASTING WITH MIXED-FREQUENCY FACTOR MODELS IN THE PRESENCE OF COMMON TRENDS. Macroeconomic Dynamics, 19, pp 753-775. doi:10.1017/S136510051300059X.
On the Choice of the Unit Period in Time Series Models
When estimating the parameters of a process, researchers can choose the reference unit of time (unit period) for their study. Frequently, they set the unit period equal to the observation interval. However, I show that decoupling the unit period from the observation interval facilitates the comparison of parameter estimates across studies with different data sampling frequencies. If the unit period is standardized across these studies, then the parameters will represent the same attributes of the underlying process, and their interpretation will be independent of the sampling frequency.
Indirect Inference Based on the Score
The Efficient Method of Moments (EMM) estimator popularized by Gallant and Tauchen (1996) is an indirect inference estimator based on the simulated auxiliary score evaluated at the sample estimate of the auxiliary parameters. We study an alternative estimator that uses the sample auxiliary score evaluated at the simulated binding function which maps the structural parameters of interest to the auxiliary parameters. We show that the alternative estimator has the same asymptotic properties as the EMM estimator but in finite samples behaves more like the distance-based indirect inference estimator of Gouri«eroux, Monfort and Renault (1993).