Forecasting tourist arrivals using time-varying parameter structural time series models

Empirical evidence has shown that seasonal patterns of tourism demand and the effects of various influencing factors on this demand tend to change over time. To forecast future tourism demand accurately requires appropriate modelling of these changes. Based on the structural time series model (STSM) and the time-varying parameter (TVP) regression approach, this study develops the causal STSM further by introducing TVP estimation of the explanatory variable coefficients, and therefore combines the merits of the STSM and TVP models. This new model, the TVP-STSM, is employed for modelling and forecasting quarterly tourist arrivals to Hong Kong from four key source markets: China, South Korea, the UK and the USA. The empirical results show that the TVP-STSM outperforms all seven competitors, including the basic and causal STSMs and the TVP model for one- to four-quarter-ahead ex post forecasts and one-quarter-ahead ex ante forecasts.     

Are professional forecasters overconfident?

Do professional forecasters have an accurate sense of the uncertainties surrounding their own forecasts? This paper examines forecaster overconfidence by comparing ex ante, surveyed forecaster uncertainty with ex post, realised uncertainty based on the dispersion of an individual’s forecast errors. Unlike the literature that focuses on consensus forecasts, our focus is at the level of the individual forecaster. Using microdata from the three major surveys of professional forecasters (Euro Area, US and UK), we examine real GDP growth forecasts over the period 1999–2015. Our findings show that overconfidence dominates among individual forecasters, particularly for longer forecast horizons, and that individual forecasters appear to have little understanding of their own uncertainty.     

Modelling and Forecasting Fiscal Variables for the Euro Area*

In this paper, we assess the possibility of producing unbiased forecasts for fiscal variables in the Euro area by comparing a set of procedures that rely on different information sets and econometric techniques. In particular, we consider autoregressive moving average models, Vector autoregressions, small‐scale semistructural models at the national and Euro area level, institutional forecasts (Organization for Economic Co‐operation and Development), and pooling. Our small‐scale models are characterized by the joint modelling of fiscal and monetary policy using simple rules, combined with equations for the evolution of all the relevant fundamentals for the Maastricht Treaty and the Stability and Growth Pact. We rank models on the basis of their forecasting performance using the mean square and mean absolute error criteria at different horizons. Overall, simple time‐series methods and pooling work well and are able to deliver unbiased forecasts, or slightly upward‐biased forecast for the debt–GDP dynamics. This result is mostly due to the short sample available, the robustness of simple methods to structural breaks, and to the difficulty of modelling the joint behaviour of several variables in a period of substantial institutional and economic changes. A bootstrap experiment highlights that, even when the data are generated using the estimated small‐scale multi‐country model, simple time‐series models can produce more accurate forecasts, because of their parsimonious specification.     

Quasi‐rational and ex ante price expectations in commodity supply models: an empirical analysis of the US broiler market

A statistically optimal inference about agents' ex ante price expectations within the US broiler market is derived using futures prices of related commodities along with a quasi‐rational forecasting regression equation. The modelling approach, which builds on a Hamilton‐type framework, includes endogenous production and allows expected cash price to be decomposed into anticipated and unanticipated components. We therefore infer the relative importance of various informational sources in expectation formation. Results show that, in addition to the quasi‐rational forecast, the true supply shock, future prices, and ex post commodity price forecast errors have, at times, been influential in broiler producers' price expectations. Copyright © 2003 John Wiley & Sons, Ltd.     

ASSESSING THE OLYMPIC GAMES: THE ECONOMIC IMPACT AND BEYOND

This study reviews the literature on the evaluation of the Olympic Games, within the broader framework of their significance as cultural assets and opportunities for endogenous growth and sustainable development of the host city. The study reviews the main approaches to the economic assessment of the Games, from the point of view of the underlying economic concepts and methodologies, as well as of the empirical results obtained. It focuses on the effects that are measured and on those, which even though important, are generally neglected. The methodologies utilized for the quantitative assessments of the Games are reviewed with special emphasis on impact and cost–benefit analysis, both on ex ante and ex post basis. The studies surveyed are analysed from the point of view of different sets of effects on the host city, and for a limited number of cases, on the host country. While the major focus is on hosting the Summer Olympics, some attention is also paid to the bidding cities, the Winter Olympics and the Paralympics. The general findings appear to be controversial with some hints of positive overall effects, but also with a well‐documented tendency to exaggerate the benefits and underestimate the costs of holding the Games in the ex ante versus the ex post studies. The survey finally suggests that ex post cross‐country econometric studies tend to catch sizable differential and persistent benefits ignored by individual studies, especially on macroeconomic and trade variables.     

Effects of Target Timing and Contract Frame on Individual Performance

Abstract

This study experimentally examines the effects of target timing and contract frame on individual performance. We study four target-based incentive contracts, categorised by when participants learn the performance target (ex ante, or before production starts, vs. ex post, or after production ends) and how the incentive contract is framed (bonus vs. penalty). The performance target in this study is determined by a peer group's average productivity. We find that performance is significantly higher for bonus-framed contracts when the performance target is revealed ex post, as opposed to ex ante. In contrast, revealing the peer-based performance target ex ante or ex post has no impact on performance for penalty-framed contracts. We also find that the ex post, bonus-framed contract significantly outperforms the other three contracts. This finding supports practitioners' claim that employers should reward (as opposed to punish) individuals based on their performance, relative to that of their peers, ex post.     

Understanding the Fisher equation

It is argued that univariate long memory estimates based on ex post data tend to underestimate the persistence of ex ante variables (and, hence, that of the ex post variables themselves) because of the presence of unanticipated shocks whose short‐run volatility masks the degree of long‐range dependence in the data. Empirical estimates of long‐range dependence in the Fisher equation are shown to manifest this problem and lead to an apparent imbalance in the memory characteristics of the variables in the Fisher equation. Evidence in support of this typical underestimation is provided by results obtained with inflation forecast survey data and by direct calculation of the finite sample biases. To address the problem of bias, the paper introduces a bivariate exact Whittle (BEW) estimator that explicitly allows for the presence of short memory noise in the data. The new procedure enhances the empirical capacity to separate low‐frequency behaviour from high‐frequency fluctuations, and it produces estimates of long‐range dependence that are much less biased when there is noise contaminated data. Empirical estimates from the BEW method suggest that the three Fisher variables are integrated of the same order, with memory parameter in the range (0.75, 1). Since the integration orders are balanced, the ex ante real rate has the same degree of persistence as expected inflation, thereby furnishing evidence against the existence of a (fractional) cointegrating relation among the Fisher variables and, correspondingly, showing little support for a long‐run form of Fisher hypothesis. Copyright © 2004 John Wiley & Sons, Ltd.     

On the forecasting performance of a small-scale DSGE model

Dynamic stochastic general equilibrium (DSGE) models have recently become standard tools for policy analysis. Nevertheless, their forecasting properties have still barely been explored. In this article, we address this problem by examining the quality of forecasts of the key U.S. economic variables: the three-month Treasury bill yield, the GDP growth rate and GDP price index inflation, from a small-size DSGE model, trivariate vector autoregression (VAR) models and the Philadelphia Fed Survey of Professional Forecasters (SPF). The ex post forecast errors are evaluated on the basis of the data from the period 1994–2006. We apply the Philadelphia Fed “Real-Time Data Set for Macroeconomists” to ensure that the data used in estimating the DSGE and VAR models was comparable to the information available to the SPF.Overall, the results are mixed. When comparing the root mean squared errors for some forecast horizons, it appears that the DSGE model outperforms the other methods in forecasting the GDP growth rate. However, this characteristic turned out to be statistically insignificant. Most of the SPF's forecasts of GDP price index inflation and the short-term interest rate are better than those from the DSGE and VAR models.     

Forecasting realized volatility: a Bayesian model‐averaging approach

How to measure and model volatility is an important issue in finance. Recent research uses high‐frequency intraday data to construct ex post measures of daily volatility. This paper uses a Bayesian model‐averaging approach to forecast realized volatility. Candidate models include autoregressive and heterogeneous autoregressive specifications based on the logarithm of realized volatility, realized power variation, realized bipower variation, a jump and an asymmetric term. Applied to equity and exchange rate volatility over several forecast horizons, Bayesian model averaging provides very competitive density forecasts and modest improvements in point forecasts compared to benchmark models. We discuss the reasons for this, including the importance of using realized power variation as a predictor. Bayesian model averaging provides further improvements to density forecasts when we move away from linear models and average over specifications that allow for GARCH effects in the innovations to log‐volatility. Copyright © 2009 John Wiley & Sons, Ltd.     

Do tournaments solve the adverse selection problem?

This paper provides a solution to a puzzle in the analysis of tournaments, that of why there is no agent discrimination or differential contracting in certain business practice settings. The paper examines the problem of a principal contracting with multiple agents whose activities are subject to common shocks. The presence of common shocks invites the use of relative performance evaluation to minimize the costs of moral hazard. But, in the additional presence of adverse selection, the analysis shows that there may be no need for ex ante screening through menus of offers. This is so because the principal becomes better informed ex post about agent types, via the realization of common uncertainty, and can effectively penalize or reward the agents ex post. Thus, unlike the standard adverse selection problem without common uncertainty where the principal always benefits from ex ante screening, it is shown that ex post sorting through relative performance evaluation reduces the scope for ex ante screening through menus, and can eliminate it completely if agents are known to not be very heterogeneous. This is consistent with observed practice in industries where the primary compensation mechanism is a cardinal tournament which is uniform among employees. The analysis connotes that by using relative instead of absolute performance measures, firms with employees who are not substantially heterogeneous not only can alleviate the agency problem, but there is also no need to extract the agents' ex ante private information about their innate abilities via a screening menu.     

The growth of shiftwork in the British food,drink and tobacco industries

The authors develop and estimate an econometric model of shiftworking for male, manual employees in the food, drink and tobacco industries. In the demand equations TSLS techniques are used to take account of simultaneity in the determination of shiftworking, capital intensity and plant size. Market demand variables are also incorporated in the model to allow for the distinction between ex ante and ex post decisions to work shifts. A demand variable is also incorporated in the supply equations. The results confirm the diverse economic characteristics of these industries, suggesting that inter-industry studies may conceal a considerable degree of variation at MLH level.     

Growth,speculation and sprawl in a monocentric city

An economic theory of sprawl in a growing, monocentric city is presented. Where decision-makers have perfect foresight, leapfrog development and discontinuous land-rent functions may occur and be efficient in both an ex post and ex ante sense. Where the extent of future growth is uncertain, decision-makers become speculators and the spatial pattern of development is more complicated. Ex post inefficiency generally occurs.     

Model occurrence and model selection in panel data sets

In this study we focus attention on model selection in the presence of panel data. Our approach is eclectic in that it combines both classical and Bayesian techniques. It is also novel in that we address not only model selection, but also model occurrence, i.e., the process by which ‘nature’ chooses a statistical framework in which to generate the data of interest. For a given data subset, there exist competing models each of which have an ex ante positive probability of being the correct model, but for any one generated sample, ex post exactly one such model is the basis for the observed data set. Attention focuses on how the underlying model occurrence probabilities of the competing models depend on characteristics of the environments in which the data subsets are generated. Classical, Bayesian, and mixed estimation approaches are developed. Bayesian approaches are shown to be especially attractive whenever the models are nested.     

Forecasting the New York State economy: The coincident and leading indicators approach

Currently there are no reliable summary indicators of the economic and fiscal condition of states and localities. This deficiency has hampered the efforts of policy makers at the sub-national level to monitor changes in the economic environment and predict how those changes will impact the fiscal health of governments. This paper attempts to fill this analytical vacuum by providing summary indicators of economic and fiscal health for New York State. The models developed are based on the single-index methodology developed by Stock and Watson [(1991). A probability model of the coincident economic indicators. In K. Lahiri and G. H. Moore (eds.), Leading economic indicators: new approaches and forecasting records (pp. 63–85). New York: Cambridge University Press]. This approach allows us to date New York business cycles and compare local cyclical behavior with the nation as a whole. We develop a leading index of economic indicators which predicts future movements in the coincident indicator. The Stock and Watson approach is used to create a fiscal indicator which acts as a summary indicator of revenue performance for New York. In addition, we explore the ability of our economic indicator series to predict future changes in state revenues. We find that changes in the leading indicator series have significant predictive power in forecasting changes in our revenue index.     

Forecast encompassing tests and probability forecasts

We consider tests of forecast encompassing for probability forecasts, for both quadratic and logarithmic scoring rules. We propose test statistics for the null of forecast encompassing, present the limiting distributions of the test statistics, and investigate the impact of estimating the forecasting models' parameters on these distributions. The small‐sample performance is investigated, in terms of small numbers of forecasts and model estimation sample sizes. We show the usefulness of the tests for the evaluation of recession probability forecasts from logit models with different leading indicators as explanatory variables, and for evaluating survey‐based probability forecasts. Copyright © 2009 John Wiley & Sons, Ltd.     

Business and consumer expectations and macroeconomic forecasts

Business and consumer surveys have become an essential tool for gathering information about different economic variables. While the fast availability of the results and the wide range of variables covered have made them very useful for monitoring the current state of the economy, there is no consensus on their usefulness for forecasting macroeconomic developments.The objective of this paper is to analyse the possibility of improving forecasts for selected macroeconomic variables for the euro area using the information provided by these surveys. After analyzing the potential presence of seasonality and the issue of quantification, we tested whether these indicators provide useful information for improving forecasts of the macroeconomic variables. With this aim, different sets of models have been considered (AR, ARIMA, SETAR, Markov switching regime models and VAR) to obtain forecasts for the selected macroeconomic variables. Then, information from surveys has been considered for forecasting these variables in the context of the following models: autoregressive, VAR, Markov switching regime and leading indicator models. In all cases, the root mean square error (RMSE) has been computed for different forecast horizons.The comparison of the forecasting performance of the two sets of models permits us to conclude that, in most cases, models that include information from the surveys have lower RMSEs than the best model without survey information. However, this reduction is only significant in a limited number of cases. In this sense, the results obtained extend the results of previous research that has included information from business and consumer surveys to explain the behaviour of macroeconomic variables, but are not conclusive about its role.     

A note on simultaneous equation estimation techniques applications with a regional econometric model

A large-scale regional econometric model is estimated using six estimation techniques, including Iterated Instrumental Variables and Iterated Two-Stage Least-Squares. Following estimation the model is simulated and a seventh technique called PANGLOSS is derived. The seven techniques are then compared in ex post and ex ante tests.     

Predictive financial models of the euro area: A new evaluation test

This paper investigates the predictive ability of financial variables for euro area growth. Our forecasts are built from univariate autoregressive and single equation models. Euro area aggregate forecasts are constructed both by employing aggregate variables and by aggregating country-specific forecasts. The forecast evaluation is based on a recently developed test for equal predictive ability between nested models. Employing a monthly dataset from the period between January 1988 and May 2005 and setting the out-of-sample period to be from 2001 onwards, we find that the single most powerful predictor on a country basis is the stock market returns, followed by money supply growth. However, for the euro area aggregate, the set of most powerful predictors includes interest rate variables as well. The forecasts from pooling individual country models outperform those from the aggregate itself for short run forecasts, while for longer horizons this pattern is reversed. Additional benefits are obtained when combining information from a range of variables or combining model forecasts.     

Predicting recessions with a composite real-time dynamic probit model

In this paper we propose a composite indicator for real-time recession forecasting based on alternative dynamic probit models. For this purpose, we use a large set of monthly macroeconomic and financial leading indicators from the German and US economies. Alternative dynamic probit regressions are specified through automated general-to-specific and specific-to-general lag selection procedures on the basis of slightly different initial sets. The resulting recession probability forecasts are then combined in order to decrease the volatility of the forecast errors and increase their forecasting accuracy. This procedure features not only good in-sample forecast statistics, but also good out-of-sample performances, as is illustrated using a real-time evaluation exercise.     

Forecasting Euro‐Area Macroeconomic Variables Using a Factor Model Approach for Backdating

We suggest to use a factor model based backdating procedure to construct historical Euro‐area macroeconomic time series data for the pre‐Euro period. We argue that this is a useful alternative to standard contemporaneous aggregation methods. The article investigates for a number of Euro‐area variables whether forecasts based on the factor‐backdated data are more precise than those obtained with standard area‐wide data. A recursive pseudo‐out‐of‐sample forecasting experiment using quarterly data is conducted. Our results suggest that some key variables (e.g. real GDP, inflation and long‐term interest rate) can indeed be forecasted more precisely with the factor‐backdated data.