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FORECASTING TOURISM WITH TARGETED PREDICTORS

IN A DATA-RICH ENVIRONMENTNuno Lourenço | Carlos Melo Gouveia

António Rua

WORKING PAPERS 2020

05

Working Papers | Lisbon 2020 • Banco de Portugal Av. Almirante Reis, 71 | 1150-012 Lisboa • www.bportugal.pt •

Edition Economics and Research Department • ISBN (online) 978-989-678-728-8 • ISSN (online) 2182-0422

Forecasting tourism with targeted predictors in adata-rich environment

Nuno LourençoBanco de Portugal

Nova SBE

Carlos Melo GouveiaBanco de Portugal

António RuaBanco de Portugal

Nova SBE

March 2020

AbstractAlong with the deepening of globalization and economic integration, economic agents facethe challenge on how to extract useful information from large panels of data for forecastingpurposes. Herein, we lay out a modelling strategy to explore the predictive content of largedatasets for tourism forecasting. In particular, we assess the role of multi-country datasets tonowcast and forecast tourism by resorting to factor models with targeted predictors to copewith such a data-rich environment. Drawing on business and consumer surveys for Portugaland its main tourism source markets, we document the usefulness of factor models to forecasttourism exports up to several months ahead. Moreover, we find that forecast performance isenhanced if predictors are chosen before factors are estimated.

JEL: C53, C55, F47.Keywords: Forecasting, Tourism, Factor models, Large datasets.

Disclaimer: The opinions expressed in this article are those of the authors and do not necessarilycoincide with those of Banco de Portugal or the Eurosystem. Any errors and omissions are the soleresponsibility of the authors.

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1. Introduction

The past five decades saw an increasing interest in tourism econometric modellingand forecasting techniques. One of the reasons relates to the rapid growthof the tourism sector, which is often referred as one of the most prominenteconomic trends for many countries. According to the World Tourism Organization,international tourist arrivals attained 1,323 million in 2017 and grew for the eighthconsecutive year, a series of continuous growth not observed since the 1960s.

Given the increasing importance of tourism within the ongoing globalizationprocess, it is natural that a lot of effort is being devoted to enhance andimprove tourism forecasting models. Besides the interest of forecasting tourismdevelopments, which is important per se for private and public managers, moreaccurate forecasts for tourism can also be valuable for improving the forecastingperformance of economic activity as a whole. This turns out to be particularlyrelevant for central banks and international institutions or private professionalforecasters when nowcasting and short-term forecasting GDP. In fact, there isevidence that a bottom-up approach may deliver a better forecasting performancethan forecasting GDP directly. In this respect, see Perevalov and Maier (2010) forthe United States, Esteves (2013) for the euro area and, more recently, Dias et al.(2018a) for Portugal.

Early contributions to the tourism forecasting literature date back to the 1960s,focusing mainly on static regressions or univariate models that build on previousvalues of the forecast variable. Recent empirical applications along these linesinclude Chu (2004) for Singapore, Coshall (2005) for the United Kingdom, Gil-Alana (2005) for the United States or Chu (2008, 2009) for several countries inthe Asian-Pacific region. Notable progress has been made since then, with the riseof vector autoregressions or cointegration techniques. In this regard, multivariateforecasting models have received increasing attention (see, inter alia, Gonzálezand Moral (1995) for Spain, Song et al. (2003) for Denmark, Veloce (2004) forCanada, Han et al. (2006) for the United States, Song and Witt (2006) for Macau,Athanasopoulos and Hyndman (2008) for Australia or Song et al. (2011) for HongKong). For a comprehensive review of the early literature on tourism forecastingsee Witt and Witt (1995). More recently, Li et al. (2005), Song and Li (2008), Gohand Law (2011), Athanasopoulos et al. (2011) and Peng et al. (2014) provide anencompassing review of studies with emphasis on the latest advances on tourismeconometric modelling and forecasting.

Notwithstanding the modelling techniques applied, all these studies operatewithin the framework of small datasets. However, with the enlargement and rapiddissemination of statistical information observed in the recent past, the informationset available to private and public managers has become progressively larger. Sucha data-rich environment poses challenges as to how all the available data can betaken into account, which can comprise a large number of series. In particular,qualitative surveys of economic activity conducted in the European Union have

3 Forecasting tourism with targeted predictors in a data-rich environment

been widely available (e.g., business and consumer surveys released by the EuropeanCommission for different sectors).

A key advantage of using survey-based indicators arises from their timeliness, asin general surveys are published just a few days after (or even a few days before) thereference period, which contrasts with the considerable release lag of ‘hard’ data.These surveys usually encompass a wide range of sectors and as several questionsare forward-looking in nature, they may signal future developments. Furthermore,qualitative indicators are not revised, thus, real-time reliability can also be granted.Previous literature has highlighted the importance of resorting to ‘soft’ data forforecasting macroeconomic variables (see, for example, Hansson et al. (2005) for anapplication to forecast GDP in Sweden, Schumacher (2007) for Germany, Rünstleret al. (2009) for several European countries, Angelini et al. (2011) and Bańburaand Rünstler (2011) for the euro area).

Forecasting macroeconomic variables in a data-rich environment correspondsto extract valuable information from a wide variety of series. The predominantframework to exploit the predictive content embedded in large datasets is throughfactor models. This type of models have proved to be effective to summarize theinformational content of the dataset into a few factors used ex-post for forecasting.In essence, factor models allow to circumvent the curse of dimensionality in thepresence of a large panel of series by reducing the number of variables to amanageable scale. On the use of factor models, one should mention, inter alia,the seminal contributions by Stock and Watson (1999b, 2002a,b) to forecast USmacroeconomic aggregates, Marcellino et al. (2003) for the euro area, Artis et al.(2005) for the United Kingdom, Schumacher (2007) and Schumacher and Breitung(2008) for Germany, Giannone et al. (2008) for the United States, Rünstler et al.(2009) for a cross-country study comprising several European countries, Barhoumiet al. (2010) for France or den Reijer (2013) for the Netherlands.

We depart from previous literature on tourism forecasting by resorting to largedatasets. However, enlarging a dataset for factor estimation might not enhanceforecast accuracy. In fact, forecast performance can be mitigated if the additionalpredictors are noisy or if predictive power stems from a factor that is dominantin a smaller dataset but is a dominated factor in a larger one (see Boivin and Ng(2006)). Hence, it is important to reduce the influence of uninformative predictors.Bai and Ng (2008) suggest the use of penalized regression to target predictorsnamely by resorting to Least-Angle Regression with Elastic Net, henceforth LARS-EN, where a subset of variables is selected before factors are estimated. Theirempirical application focuses on US inflation. The relevance of screening predictorsprior to factor estimation is reinforced by the work of Schumacher (2007), whoforecasts German GDP growth and Li and Chen (2014) who concentrate on theUS economy. Such an approach may be particularly useful in the context of tourismforecasting as one can easily end up with datasets that include hundreds of series,especially if one intends to cover economic indicators regarding both the destinationand origin countries.

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In a data-rich setting, the number of studies assessing the importance of takingon board foreign data to forecast domestic macroeconomic series is rather limited.In this respect, it is worth mentioning the work by Brisson et al. (2003) whoevaluate the usefulness of variables regarding the United States and other countriesto forecast real GDP and inflation in Canada. Within the euro area, Schumacher(2010) assesses the role played by the euro area and the G7 economies to forecastactivity in Germany. More recently, Dias et al. (2018b) forecast exports of goodsin Portugal by resorting to data on Portuguese main trading partners. We alsocontribute to this literature by investigating the role of international data to forecasttourism exports.

Herein, we focus on Portugal which is a small open economy where tourismhas become a major driver for GDP growth, namely in the aftermath of a severeeconomic and financial crisis. Besides considering domestic variables we extend thedataset to cover the country’s main tourism source markets namely the UnitedKingdom, France, Spain, Germany, the United States and the Netherlands. Bothfor Portugal and these countries, we focus on ‘soft’ data and collect business andconsumer surveys covering several sectors of economic activity. Given the largesize of the dataset, we use the LARS-EN based pre-selection of variables andassess the usefulness of selecting series before the estimation of factors to improveforecast accuracy. We use timely monthly variables to nowcast and forecast monthlyPortuguese tourism exports up to a 6-month ahead horizon. We find that theuse of targeted predictors improves forecasting performance in such a data-richenvironment. The results also reinforce the usefulness of taking on board economicdata from the countries of origin for forecasting tourism.

The remainder of the paper proceeds as follows. In section 2, we present theeconometric approach pursued. Section 3 describes the data considered in theempirical application. The empirical results are discussed in section 4. Section 5concludes.

2. Econometric methodology

2.1. Factor model representation and estimation

In this subsection, we lay out the representation of factor models underlying thepursued method for forecasting with large datasets. Define Xt as a N -dimensionalcolumn vector containing the N predictors observed throughout time t = 1, ..., T .We assume that both the predictors and the forecasted variable, y, are stationary.

The factor model considers that each and every variable in Xt is represented asthe sum of two orthogonal components: a common component, driven by a smallnumber of unobserved common factors and an idiosyncratic component, drivenby variable-specific shocks. Formally, the data generating process for Xt can berepresented through a static factor representation given by

Xt = ΛFt + ξt (1)


with Ft = (f1t, ..., frt)′ denoting an (r× 1) vector of latent factors, Λ correspondsto an (N × r) matrix of unknown factor loadings and ξt is a N -dimensional vectorof idiosyncratic terms.1

The space spanned by the latent factors can be estimated through the principalcomponents estimator which has been shown to be consistent under relativelygeneral assumptions (see Stock and Watson (1998, 2002b), Bai and Ng (2002)and Amengual and Watson (2007)).

Once the factors are estimated, the estimation of the forecasting equation forthe variable of interest follows. Hence, to obtain forecasts for variable y at horizonh, one should regress yt+h on the r estimated factors and eventually on lags of y,that is

yt+h = α0 +

r∑i=1

αiF̂t,i +

p∑j=0

δjyt−j + εt+h (2)

where α0 is a constant term, αi denotes the coefficients associated with theestimated factors F̂t, yt−j denotes the autoregressive terms of the model, whereδj are the respective coefficients and p is the number of autoregressive terms.

2.2. The Elastic Net optimization problem

Although a small set of r estimated factors may account for a considerable shareof the communality of the series within the dataset, the estimation of the factorsis completely independent of the series to be forecasted or the forecast horizonat stake. Hence, potentially useful information contained in the dataset may endup being disregarded. In this regard, Bai and Ng (2008) suggest to estimatethe factor space from a set of targeted predictors. Drawing on the relationshipbetween yt+h and Xt, a subset of predictors Xt,A ⊆ Xt is selected prior to factorestimation. The proposed method relies on penalized regressions and conductssubset selection and shrinkage by removing uninformative predictors. Basically,the regression coefficients of less informative predictors to forecast the variable ofinterest are more penalized. In line with Zou and Hastie (2005), Bai and Ng (2008)consider the following EN optimization problem

minβ

RSS + λ1N∑j=1

|βj |+ λ2N∑j=1

β2j

(3)where RSS denotes the residual sum of squares from a regression of yt+h on allpredictors, βj is the regression coefficient of regressor j, and the parameters λ1 andλ2 control the penalties associated with the L1- and L2-norm of β, respectively.

1This representation is without loss of generality as it can be shown that the dynamic factormodel representation has an equivalent static factor formulation (see, for instance Stock and Watson(2005a)). In addition, as argued by Bai and Ng (2007), such distinction is not relevant for forecastingpurposes.

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The L1 penalty solves

β̂ = arg minβ

RSS + λ1N∑j=1

|βj |

(4)where the tuning parameter λ1 controls for the degree of shrinkage, or equivalentlyfor the number of variables to be dropped. It augments the usual ordinary leastsquares regression with λ1 regularization, leading to solutions that are sparsein terms of the coefficients. Such method is also known as the Least AbsoluteShrinkage and Selection Operator (LASSO) (see Tibshirani (1996)).

The L2 penalty leads to

β̂ = arg minβ

RSS + λ2N∑j=1

β2j

(5)where for 0 ≤ λ2


the vector of current correlations. Define A as the set of indices corresponding tothe variables with the largest absolute current correlations,

Ĉ = maxj{|ĉj |} and A =

{j : |ĉj | = Ĉ

}. (7)

Setting sj = sign {ĉj} for j ∈ A, one computes XA, AA, uA as well as the innerproduct vector

a ≡ X ′uA. (8)

For A a subset of indices, define the matrix

XA = (sjXj)j∈A (9)

where the signs sj equal ±1.

Define

GA = X′

AXA and AA = (1′

AG−1A 1A)

−1/2, (10)

where 1A is a vector of ones of length equaling |A|, the size of A. The equiangularvector uA = XAwA, where wA = AAG−1A 1A is the unit vector making equalangles less than 90◦, with the columns of XA,

X ′AuA = AA1A and ||uA||2 = 1. (11)

Then, the LARS algorithm updates µ̂A to

µ̂A+ = µ̂A + γ̂uA, (12)

where

γ̂ = min+j∈Ac

{Ĉ − ĉjAA − aj

,Ĉ + ĉjAA + aj

}. (13)

The plus sign indicates that the minimum is taken over positive entries only withineach choice of j.

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3. Data

3.1. Tourism exports

The empirical application consists in forecasting the growth rate of nominal tourismexports for Portugal (see Figure 1).2 Tourism flows are released on a monthlybasis by the Portuguese central bank, Banco de Portugal, without any seasonal orcalendar adjustment. As depicted below, the series exhibits a high volatility, withyear-on-year rates of change varying from -20 per cent to close to 40 per cent forthe period under analysis. The spikes in June 2004 and April 2017 reflect the UEFAEuropean Championship hosted in Portugal and the Pope’s visit to the country,respectively.

Jan-01 Jan-03 Jan-05 Jan-07 Jan-09 Jan-11 Jan-13 Jan-15 Jan-17 Dec-18-20

-10

0

10

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30

40

Y-o-y

rate

ofchan

ge(%

)

Figure 1: Portuguese tourism exports.Source: Statistical Bulletin, Banco de Portugal.

We forecast the year-on-year growth rate of Portuguese tourism exports.By considering year-on-year growth rates, one purges the effect of deterministicseasonality and avoids the high volatility of the month-on-month growth rates oftourism flows. Furthermore, there is widespread evidence of larger resemblance

2In balance of payments data, this variable is recorded under the heading travel exports.


between macro variables measured in changes from the previous year and theevolution of survey-based indicators. It also downplays the irregular componentpresent in the series. Nevertheless, one should address calendar effects or movingholidays in model estimation and forecasting, as these are likely to influence theyear-on-year growth rate of tourism exports.

3.2. The multi-country dataset

In this study, we compile a comprehensive dataset for Portugal which is augmentedto account for its main tourism source markets, specifically the United Kingdom,France, Spain, Germany, the United States and the Netherlands. These countriesexplain two thirds of the inbound tourism revenues in Portugal. The UnitedKingdom and France account for the largest share, representing more than 16per cent each in 2018. These are followed by Spain and Germany, which accountfor around 13 and 11 per cent of tourism exports, respectively. The share of theUnited States stands close to 6 per cent in 2018 whereas the Netherlands has ashare slightly above 4 per cent.

Data for Portugal and its main European Union source markets draws onthe business and consumer surveys released by the European Commission. Thepanel of variables encompasses qualitative data covering different sectors of theeconomies. Representatives of the industry (manufacturing), services, retail tradeand construction sectors, as well as to consumers are asked on several domains.Questions in the industry survey include assessments of recent and future trendsin production, of the current levels of order books and stocks, selling priceexpectations and employment. In the services survey, managers are asked abouttheir assessment on the business situation, of the past and future changes in theircompany’s turnover and employment and of their expecting selling prices. Theretail trade survey is focused on assessments of recent developments in managers’business situation, of the current level of stocks, and their expectations regardingproduction, new orders, prices charged and employment. Similar questions are askedin the construction survey to infer on the short-term developments in this sector.Finally, the consumers’ survey collects information on households’ spending andsavings intentions and measures their understanding of the factors that affectthose decisions. Hence, questions are grouped around four topics: general economicsituation, households’ financial situation, savings and intentions with regard tomajor purchases. Questions concerning perceived and expected price changes arealso included.

For the United States, the business surveys in the manufacturing and non-manufacturing sectors released by the Institute for Supply Management are used, inaddition to the Conference Board and the University of Michigan consumer surveys.These surveys include, inter alia, questions on customer inventories, orders, pricespaid, employment expectations as well as consumers’ sentiment or expectations.

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The sample period spans January 2000 to December 2018. On average, 40series per country (20 regarding the United States) are covered, amounting to 257series overall.3

For Portugal and Spain, we use the Expectation-Maximization algorithmsuggested by Stock and Watson (2002a) to balance the dataset at the beginning ofthe sample, as a few series were not available for the full sample period. FollowingStock and Watson (2005a), the series were also screened for outliers.

4. Empirical application

4.1. Design of the forecasting exercise

The forecasting exercise is performed in a fully recursive way. This means that,for each time period t, predictors are selected from the large dataset throughthe LARS-EN algorithm using data available up to t. Then, drawing on principalcomponents, factors are estimated from the set of selected predictors in the previousstage. Afterwards, the forecasting equation in (2) is estimated with the number ofestimated factors, r, chosen according to a modified version of the BIC criterionas in Stock and Watson (1998). Given the previous discussion on calendar effects,we also include in the model specification a deterministic variable to account forthe number of working days in each month and dummy variables to control forthe two moving holidays (Easter and Carnival) as well as for the above-mentionedevents hosted in Portugal. Finally, the fitted model is used to produce h-step aheadout-of-sample forecasts.

One should note that, in this way, we are not imposing the same set of predictorsover time and neither across forecast horizons. Since the model specification andestimation are allowed to be updated conditional on the information availableup to time period t, we replicate what one could actually do at each point intime. Furthermore, to deal with the potential varying informational content of thedataset, we considered a rolling window estimation scheme so as to enhance modelflexibility. In particular, we have chosen a window size such that the estimationperiod always encompasses a full cycle and therefore it is not influenced only byupward or downward movements. As it has been standard in the literature toconsider as business cycles the fluctuations that last up to 8 years (see, for example,Stock and Watson (1999a, 2005b)), we set the window size to 96 months.4

We also consider the case where no pre-selection of predictors prior to factorestimation is done, which boils down to the standard factor model approach. Theusual AR(p) model, where p is the number of autoregressive terms chosen by the

3The list of all series is detailed in the appendix.4As a sensitivity analysis, we have also considered other rolling window sizes and the results are

qualitatively similar.


BIC criterion, is used as benchmark. Naturally, this model also includes the abovementioned deterministic and dummy variables.

To infer on the relative behaviour of the factor model vis-à-vis the benchmark,the out-of-sample forecasts are compared. The forecast evaluation period runs fromJanuary 2009 to December 2018, which corresponds to half of the sample period.We consider forecast horizons from 1 to 6-month ahead. However, since businessand consumer surveys are released one month before tourism statistics, that is,data for time t is already available for the former whereas for the latter the lastfigure refers to time t− 1, one can also consider nowcasting (i.e., h = 0).

Forecast accuracy is assessed through the Mean-Squared Forecast Error(MSFE) and the relative MSFE is calculated using the autoregressive model asbenchmark. Thus, if this ratio is below one, the competing model outperforms thebenchmark. We examine the statistical significance of the forecasting gains usingthe Clark and West (2007) test.

4.2. Results

In what follows, we consider ‘soft’ data driven forecasts, i.e., we resort to surveydata for Portugal and its main source markets (amounting to a total of 257 series).As mentioned earlier, besides all the advantages inherent to ‘soft’ data, it allowsone to assess model performance for both nowcasting and forecasting.

As discussed in section 2, the practical use of the LARS-EN procedure involvessetting two parameters, λ1 and λ2. The parameter λ1 controls the number ofpredictors to be selected, that is, NA. Given that there is no a priori aboutthe optimal number of predictors, we considered a range of alternatives namelyNA = {30, 40, ..., 200}. Regarding λ2, which controls the importance of the penaltyof the L2-norm of β, we set λ2 = 0.25 in line with Bai and Ng (2008) andSchumacher (2010).

In Table 1, we present the relative MSFE of the factor model vis-à-visthe univariate autoregressive model for the different forecast horizons (h ={0, 1, ..., 6}). At the bottom of the table, we also report the relative MSFE forthe case of a factor model without pre-selection, i.e., considering all 257 series forfactor estimation. The shaded entry denotes the minimum relative MSFE for eachforecast horizon.

The empirical results obtained convey the following findings. Firstly, since mostentries in Table 1 are below one, the factor model yields greater forecast accuracythan the univariate benchmark regardless of the horizon or the number of predictorsconsidered.

Secondly, the results suggest that to forecast at shorter horizons it is preferableto take on board more predictors than at longer horizons. In particular, fromh = 0 to h = 4, greater forecasting gains are delivered for a number of pre-selected predictors between 140 and 170, whereas for h= {5, 6}, a lower number oftargeted predictors (between 80 and 110) yields greater forecasting accuracy gains.As the forecast horizon increases, the number of variables that convey informational

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Forecast horizon

0 1 2 3 4 5 6

Targeted predictors (NA)30 1.07 0.94 0.93 0.93 0.93 1.06 1.1140 1.05 1.00 0.89 0.92 0.98 1.04 1.1050 1.00 0.99 0.82 0.91 0.95 0.97 1.0160 1.01 0.96 0.81 0.92 0.98 0.91 0.9470 0.99 0.96 0.79 0.92 0.98 0.87 0.8980 0.96 0.95 0.78 0.89 0.99 0.82 0.8890 0.98 0.96 0.79 0.92 1.01 0.92 0.89100 0.96 0.93 0.79 0.88 0.98 0.89 0.86110 0.90 0.94 0.78 0.91 0.99 0.92 0.83120 0.90 0.94 0.82 0.84 0.98 0.97 0.92130 0.87 0.94 0.83 0.84 0.99 0.95 0.89140 0.85 0.93 0.81 0.81 0.92 1.00 0.90150 0.86 0.91 0.76 0.83 0.91 0.99 0.95160 0.85 0.85 0.76 0.85 0.91 0.97 0.96170 0.84 0.85 0.77 0.84 0.94 0.93 0.97180 0.84 0.91 0.79 0.85 0.95 0.92 0.95190 0.87 0.91 0.80 0.88 0.96 0.96 0.93200 0.85 0.88 0.81 0.88 0.93 1.00 0.93

No pre-selectionAll series 0.87 0.87 0.80 0.87 0.95 0.99 0.87

Table 1. Relative MSFE.

content about future developments tends to decrease. We also find that aroundhalf of the selected predictors are common across adjacent horizons.

Thirdly, such selection of predictors leads to forecasting gains, on average,of 17 per cent when compared with the univariate benchmark. The statisticalsignificance of such an improvement is corroborated by the Clark and West (2007)test procedure.

To provide additional details about the best performing models (denoted by theshaded entries in Table 1) we report in Table 2 the average number of autoregressiveterms and the average number of factors selected over the evaluation period (alongwith the standard deviation over time). Moreover, we report the R2 to characterizethe in-sample fit. In terms of specification, these models include, on average, oneautoregressive term while the average number of factors ranges between six andeight with most cases including seven factors. The in-sample fit is quite noteworthywith a R2 of around 0.85.

Finally, when no pre-selection is done, forecasting gains are lower than thoseobtained with targeted predictors, reaching 11 per cent. We have also computedthe Clark and West (2007) test to compare the pre-selection and no pre-selectioncases and found supporting evidence of statistically larger gains in the case of pre-selection. Hence, pre-selection of predictors before factors are estimated plays arole to forecast tourism exports.


h = 0 h = 1 h = 2 h = 3 h = 4 h = 5 h = 6NA = 170 NA = 170 NA = 160 NA = 140 NA = 150 NA = 80 NA = 110

No. of factors 7 8 7 7 7 6 6(1.78) (1.65) (1.62) (1.85) (1.71) (1.66) (1.42)

No. of AR terms 1 1 1 1 1 1 1(1.76) (1.57) (1.78) (1.73) (1.64) (1.59) (1.28)

R2 0.85 0.85 0.85 0.85 0.84 0.85 0.85(0.04) (0.05) (0.04) (0.04) (0.05) (0.06) (0.05)

Table 2. Model specification and goodness-of-fit for the best performing model at eachhorizon.Note: Standard deviations in parentheses.

We also examine the sensitivity of the results to the choice of λ2. In particular,following Bai and Ng (2008), we consider λ2 = {0.5, 1.5}. One can conclude thatthe main findings highlighted above do not seem to be sensitive to the choice ofλ2. In this respect, Bai and Ng (2008) and Schumacher (2010) also find that thechoice for this parameter is not critical for the results.

Up to now the analysis has been based on ‘soft’ data. As a robustness check, weextend the dataset further to cover the main quantitative indicators of economicactivity, namely industrial production, retail trade, activity in the services sectorand labour market outcomes for Portugal and its main tourism source markets,amounting to 615 series overall. The results show that augmenting the dataset withquantitative data, or considering only ‘hard’ data, does not lead to an improvementof forecast accuracy. Moreover, the results with ‘hard’ data tend to deteriorateif one takes into account the publication lags. Hence, these results reinforce theusefulness of ‘soft’ data, in line with previous literature, and in particular for tourismforecasting.

4.3. Unveiling the targeted predictors

In this subsection, we intend to provide some insights regarding the selectedpredictors underlying the results presented in Table 1. Given the large dimensionof the dataset, it is not feasible to detail the predictors. Hence, we focus on twoimportant groupings of the ‘soft’ dataset. On the one hand, we have the countryto which the variable belongs to, that is, if it refers to Portugal or to one of itssource markets. On the other hand, we have the survey dimension, that is, fromwhich survey comes the predictor. Beginning with the country analysis, we presentin Figure 2 plots for the average share of selected predictors country, for differentnumber of targeted predictors (NA) and forecast horizons (h). A visual inspectionimmediately highlights that most of the series are selected from Portugal’s sourcemarkets, which emphasizes the role of foreign data to forecast inbound tourismflows.

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Figure 2: Average share of selected predictors by country for different number of predictorsand forecast horizons.

In the case of Portugal, when the number of selected predictors is small, theaverage share is low (close to 10 per cent), while increasing up to 15 per cent asmore predictors are allowed to be selected. For the United Kingdom, the averageshare of series is high for smaller datasets (25 per cent). Although it shows some


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Figure 2: Average share of selected predictors by country for different number of predictorsand forecast horizons (continued).

decrease with the number of predictors, the share of UK series is no less than15 per cent. Such a role is grounded on the importance of the UK as the mainsource market for Portugal in what concerns tourism. Regarding the other mainsource countries, namely France, Spain and Germany, a similar pattern is observed,with average shares varying between 10 and 20 per cent. In turn, the UnitedStates presents a low share of targeted predictors, standing below 10 per cent.Finally, even though the Netherlands weighs less in Portuguese tourism exports,a noteworthy share of predictors belong to this country, particularly for a smallnumber of predictors. This may reflect the fact that, as a small open economy, theNetherlands is exposed to the same drivers that affect Portugal’s tourism flows.Lastly, the above mentioned shares do not seem to vary much with the forecasthorizon, especially for larger datasets.

We now turn to the analysis by survey. Figure 3 displays the average shareof selected predictors by survey, for different number of targeted predictors andforecast horizons. The results clearly reveal that the consumers’ survey accountsfor the largest average share regardless of the size of the dataset or the forecasthorizon. Notwithstanding a share usually above 30 per cent in the case of theconsumers’ survey, its share is even higher for shorter horizons and smaller datasets,being close to 50 per cent. Hence, among the several available surveys, the onethat captures the current and prospective assessment by consumers is the morerelevant to nowcast and forecast tourism flows. In turn, the industry survey showsan increasing share with the number of predictors, from less than 5 per cent toslightly above 20 per cent. The surveys regarding services and retail trade display asimilar behavior, with shares between 10 and 20 per cent. The construction sectorsurvey evidences a slightly lower importance standing above 10 per cent, whereasthe miscellaneous category turns out to be unimportant.

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Figure 3: Average share of selected predictors by survey for different number of predictorsand forecast horizons.


To complement the above graphical analysis, we report in Table 3 thecomposition of the set of targeted predictors, by country and survey, for the bestperforming model for each horizon (which corresponds to a specific pair (h,NA)in each graph of Figures 2 and 3). The results reinforce the above discussion andhighlight the importance of foreign data to forecast tourism developments. In fact,domestic variables only account for around 15 per cent of the targeted predictorsand for almost all horizons data regarding Portugal is surpassed by some othercountry. One can also see from Table 3 that the consumers’ survey represents themain source of targeted predictors accounting for around one third of the set ofpredictors across all forecast horizons. The industry survey presents a share slightlyabove 20 per cent for h = {0, 1} but loses importance for longer horizons. Incontrast, the surveys concerning services and retail trade are more relevant forlonger than shorter horizons.

h = 0 h = 1 h = 2 h = 3 h = 4 h = 5 h = 6NA = 170 NA = 170 NA = 160 NA = 140 NA = 150 NA = 80 NA = 110

By countryPortugal 14.5 15.8 16.9 13.5 11.6 15.0 15.4United Kingdom 16.7 16.1 14.9 16.7 17.3 15.2 16.9France 15.1 14.1 15.5 15.4 14.4 16.1 18.3Spain 16.2 17.4 16.7 15.4 18.5 16.3 17.0Germany 15.6 13.4 13.4 13.0 13.2 13.6 11.4United States 6.5 7.1 7.9 9.2 8.8 7.1 6.1The Netherlands 15.3 16.0 14.7 16.8 16.3 16.8 14.7

By surveyConsumers 33.6 31.1 32.0 32.2 31.6 32.7 29.6Industry 21.5 20.3 16.4 17.4 12.8 8.7 14.7Services 13.8 14.4 16.8 13.8 17.6 14.7 17.8Retail trade 15.5 18.4 16.6 17.8 17.3 21.3 21.6Construction 12.0 12.0 13.9 13.8 16.0 19.8 13.1Others 3.6 3.8 4.2 5.1 4.7 2.8 3.1

Table 3. Composition of the set of targeted predictors by country and survey for the bestperforming model at each horizon.

In addition, we report in Table 4 the five most selected predictors throughouttime for each horizon. The results highlight the value of resorting to internationaldata to forecast tourism exports, in line with the previous discussion.

18

Forecast horizon Country Survey Series

h = 0 The Netherlands Consumer Financial situation of households over the last 12 monthsThe Netherlands Retail trade Retail trade confidence indicatorThe Netherlands Retail trade Business activity over the last 3 monthsThe Netherlands Construction Price expectations over the next 3 monthsUnited Kingdom Construction Overall order books

h = 1 Portugal Services Business situation over the last 3 monthsFrance Retail trade Employment expectations over the next 3 monthsThe Netherlands Retail trade Orders placed with suppliers over the next 3 monthsThe Netherlands Retail trade Retail trade confidence indicatorThe Netherlands Construction Price expectations over the next 3 months

h = 2 Portugal Industry Employment expectations over the next 3 monthsThe Netherlands Consumer Financial situation of households over the last 12 monthsThe Netherlands Construction Price expectations over the next 3 monthsUnited Kingdom Construction Overall order booksUnited Kingdom Services Employment expectations over the next 3 months

h = 3 France Consumer General economic situation over last 12 monthsUnited Kingdom Services Employment expectations over the next 3 monthsThe Netherlands Services Business situation over the last 3 monthsThe Netherlands Retail trade Orders placed with suppliers over the next 3 monthsThe Netherlands Construction Price expectations over the next 3 months

h = 4 The Netherlands Consumer Major purchases at presentThe Netherlands Services Business activity over the last 3 monthsUnited Kingdom Services Employment expectations over the next 3 monthsUnited Kingdom Construction Construction confidence indicatorThe Netherlands Construction Price expectations over the next 3 months

h = 5 France Consumer General economic situation over the last 12 monthsGermany Consumer Consumer confidence indicatorThe Netherlands Consumer Major purchases at presentThe Netherlands Services Business situation over the last 3 monthsUnited Kingdom Construction Construction confidence indicator

h = 6 Germany Consumer Consumer confidence indicatorThe Netherlands Consumer General economic situation over the last 12 monthsThe Netherlands Consumer Major purchases at presentSpain Services Business situation over the last 3 monthsSpain Services Services confidence indicator

Table 4. Most selected predictors for each forecast horizon.

5. Concluding remarks

In the past decades, the tourism industry has paved the way in driving theprosperity of nations, with direct impact on economic growth, job creation orbusiness investment. Given the importance of tourism worldwide, reinforced bythe strong dynamics recently observed in several countries, it is of utmost interestto forecast its developments by private and public managers. Furthermore, moreaccurate forecasts for tourism can also be valuable to enhance the forecast accuracyof economic activity as a whole. In this respect, there is by now evidence that abottom-up approach may lead to better forecasting performance than forecastingGDP directly. This is particularly important for central banks and internationalinstitutions or private professional forecasters when forecasting GDP.

Monitoring and forecasting tourism developments poses a challenge foreconomic agents in a context marked by increasing data availability. Hence,


decision-makers require new methods and tools to take advantage of theinformational content embedded in large datasets. In contrast with previousliterature on tourism forecasting, we pursue an approach able to cope with sucha data-rich environment. At the same time, our strategy allows to mitigate theinfluence of uninformative variables for forecasting purposes.

In our empirical application, we exploit the role of a multi-country dataset tonowcast and forecast Portuguese tourism exports on a monthly basis. We makeuse of factor models with targeted predictors to cope with such a large dataset.Drawing on business and consumer surveys for Portugal and its main tourism sourcemarkets, namely the United Kingdom, France, Spain, Germany, the United Statesand the Netherlands, we find significant forecasting gains up to 6-month ahead.Furthermore, we show that forecast performance is enhanced if predictors are pre-selected from the large dataset through the LARS-EN algorithm before factorsare estimated. Hence, our results reinforce the usefulness of relying on survey-based data for tourism forecasting. Although the empirical exercise has focusedon Portugal, where there has been a striking increase of tourism importance,the framework outlined in this study can be easily extended to other countriesor regions.

20

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Appendix

Table 5. List of series and data sources.

Series SourcePortugalEconomic Sentiment Indicator European CommissionConsumer Confidence Indicator European Commission, Consumers surveyFinancial situation over last 12 months European Commission, Consumers surveyFinancial situation over next 12 months European Commission, Consumers surveyGeneral economic situation over last 12 months European Commission, Consumers surveyGeneral economic situation over next 12 months European Commission, Consumers surveyMajor purchases at present European Commission, Consumers surveyMajor purchases over next 12 months European Commission, Consumers surveyUnemployment expectations over next 12 months European Commission, Consumers surveySavings at present European Commission, Consumers surveySavings over next 12 months European Commission, Consumers surveyPrice trends over last 12 months European Commission, Consumers surveyPrice trends over next 12 months European Commission, Consumers surveyStatement on financial situation of household European Commission, Consumers surveyConstruction Confidence Indicator European Commission, Construction surveyBuilding activity development over the past 3 months European Commission, Construction surveyEvolution of your current overall order books European Commission, Construction surveyEmployment expectations over the next 3 months European Commission, Construction surveyPrices expectations over the next 3 months European Commission, Construction surveyIndustrial Confidence Indicator European Commission, Industry surveyProduction trend observed in recent months European Commission, Industry surveyAssessment of order-book levels European Commission, Industry surveyAssessment of export order-book levels European Commission, Industry surveyAssessment of stocks of finished products European Commission, Industry surveyProduction expectations for the months ahead European Commission, Industry surveySelling price expectations for the months ahead European Commission, Industry surveyEmployment expectations for the months ahead European Commission, Industry surveyRetail trade Confidence Indicator European Commission, Retail trade surveyBusiness activity (sales) development over the past 3 months European Commission, Retail trade surveyVolume of stock currently hold European Commission, Retail trade surveyBusiness activity expectations over the next 3 months European Commission, Retail trade surveyOrders expectations over the next 3 months European Commission, Retail trade surveyEmployment expectations over the next 3 months European Commission, Retail trade surveyServices confidence indicator European Commission, Services surveyBusiness situation development over the past 3 months European Commission, Services surveyEvolution of the demand over the past 3 months European Commission, Services surveyExpectation of the demand over the next 3 months European Commission, Services surveyEvolution of the employment over the past 3 months European Commission, Services surveyExpectations of the employment over the next 3 months European Commission, Services survey

United KingdomEconomic Sentiment Indicator European CommissionConsumer Confidence Indicator European Commission, Consumers surveyFinancial situation over last 12 months European Commission, Consumers surveyFinancial situation over next 12 months European Commission, Consumers surveyGeneral economic situation over last 12 months European Commission, Consumers surveyGeneral economic situation over next 12 months European Commission, Consumers surveyMajor purchases at present European Commission, Consumers surveyMajor purchases over next 12 months European Commission, Consumers surveyUnemployment expectations over next 12 months European Commission, Consumers surveySavings at present European Commission, Consumers surveySavings over next 12 months European Commission, Consumers surveyStatement on financial situation of household European Commission, Consumers surveyPrice trends over last 12 months European Commission, Consumers surveyPrice trends over next 12 months European Commission, Consumers surveyIndustrial Confidence Indicator European Commission, Industry surveyProduction trend observed in recent months European Commission, Industry surveyProduction expectations for the months ahead European Commission, Industry surveyAssessment of order-book levels European Commission, Industry surveyAssessment of export order-book levels European Commission, Industry surveyAssessment of stocks of finished products European Commission, Industry surveyEmployment expectations for the months ahead European Commission, Industry surveySelling price expectations for the months ahead European Commission, Industry surveyConstruction Confidence Indicator European Commission, Construction surveyEvolution of your current overall order books European Commission, Construction surveyEmployment expectations over the next 3 months European Commission, Construction surveyBuilding activity development over the past 3 months European Commission, Construction surveyPrices expectations over the next 3 months European Commission, Construction surveyRetail trade Confidence Indicator European Commission, Retail trade surveyBusiness activity (sales) development over the past 3 months European Commission, Retail trade surveyVolume of stock currently hold European Commission, Retail trade surveyBusiness activity expectations over the next 3 months European Commission, Retail trade surveyOrders expectations over the next 3 months European Commission, Retail trade surveyEmployment expectations over the next 3 months European Commission, Retail trade surveyServices confidence indicator European Commission, Services survey

Continued on next page

24

Table 5 – Continued from previous pageSeries SourceBusiness situation development over the past 3 months European Commission, Services surveyEvolution of the demand over the past 3 months European Commission, Services surveyExpectation of the demand over the next 3 months European Commission, Services surveyEvolution of the employment over the past 3 months European Commission, Services surveyExpectations of the employment over the next 3 months European Commission, Services survey

FranceEconomic Sentiment Indicator European CommissionConsumer Confidence Indicator European Commission, Consumers surveyFinancial situation over last 12 months European Commission, Consumers surveyFinancial situation over next 12 months European Commission, Consumers surveyGeneral economic situation over last 12 months European Commission, Consumers surveyGeneral economic situation over next 12 months European Commission, Consumers surveyMajor purchases at present European Commission, Consumers surveyMajor purchases over next 12 months European Commission, Consumers surveyUnemployment expectations over next 12 months European Commission, Consumers surveySavings at present European Commission, Consumers surveySavings over next 12 months European Commission, Consumers surveyStatement on financial situation of household European Commission, Consumers surveyPrice trends over last 12 months European Commission, Consumers surveyPrice trends over next 12 months European Commission, Consumers surveyIndustrial Confidence Indicator European Commission, Industry surveyProduction trend observed in recent months European Commission, Industry surveyProduction expectations for the months ahead European Commission, Industry surveyAssessment of order-book levels European Commission, Industry surveyAssessment of export order-book levels European Commission, Industry surveyAssessment of stocks of finished products European Commission, Industry surveyEmployment expectations for the months ahead European Commission, Industry surveySelling price expectations for the months ahead European Commission, Industry surveyConstruction Confidence Indicator European Commission, Construction surveyEvolution of your current overall order books European Commission, Construction surveyEmployment expectations over the next 3 months European Commission, Construction surveyBuilding activity development over the past 3 months European Commission, Construction surveyRetail trade Confidence Indicator European Commission, Retail trade surveyBusiness activity (sales) development over the past 3 months European Commission, Retail trade surveyVolume of stock currently hold European Commission, Retail trade surveyBusiness activity expectations over the next 3 months European Commission, Retail trade surveyOrders expectations over the next 3 months European Commission, Retail trade surveyEmployment expectations over the next 3 months European Commission, Retail trade surveyPrices expectations over the next 3 months European Commission, Retail trade surveyServices confidence indicator European Commission, Services surveyBusiness situation development over the past 3 months European Commission, Services surveyEvolution of the demand over the past 3 months European Commission, Services surveyExpectation of the demand over the next 3 months European Commission, Services surveyEvolution of the employment over the past 3 months European Commission, Services surveyExpectations of the employment over the next 3 months European Commission, Services surveyExpectations of the prices over the next 3 months European Commission, Services survey

SpainEconomic Sentiment Indicator European CommissionConsumer Confidence Indicator European Commission, Consumers surveyFinancial situation over last 12 months European Commission, Consumers surveyFinancial situation over next 12 months European Commission, Consumers surveyGeneral economic situation over last 12 months European Commission, Consumers surveyGeneral economic situation over next 12 months European Commission, Consumers surveyMajor purchases at present European Commission, Consumers surveyMajor purchases over next 12 months European Commission, Consumers surveyUnemployment expectations over next 12 months European Commission, Consumers surveySavings at present European Commission, Consumers surveySavings over next 12 months European Commission, Consumers surveyStatement on financial situation of household European Commission, Consumers surveyPrice trends over last 12 months European Commission, Consumers surveyPrice trends over next 12 months European Commission, Consumers surveyIndustrial Confidence Indicator European Commission, Industry surveyProduction trend observed in recent months European Commission, Industry surveyProduction expectations for the months ahead European Commission, Industry surveyAssessment of order-book levels European Commission, Industry surveyAssessment of export order-book levels European Commission, Industry surveyAssessment of stocks of finished products European Commission, Industry surveyEmployment expectations for the months ahead European Commission, Industry surveySelling price expectations for the months ahead European Commission, Industry surveyConstruction Confidence Indicator European Commission, Construction surveyEvolution of your current overall order books European Commission, Construction surveyEmployment expectations over the next 3 months European Commission, Construction surveyBuilding activity development over the past 3 months European Commission, Construction surveyPrices expectations over the next 3 months European Commission, Construction surveyRetail trade Confidence Indicator European Commission, Retail trade surveyBusiness activity (sales) development over the past 3 months European Commission, Retail trade surveyVolume of stock currently hold European Commission, Retail trade surveyBusiness activity expectations over the next 3 months European Commission, Retail trade surveyOrders expectations over the next 3 months European Commission, Retail trade surveyEmployment expectations over the next 3 months European Commission, Retail trade surveyPrices expectations over the next 3 months European Commission, Retail trade surveyServices confidence indicator European Commission, Services surveyBusiness situation development over the past 3 months European Commission, Services survey



Table 5 – Continued from previous pageSeries SourceEvolution of the demand over the past 3 months European Commission, Services surveyExpectation of the demand over the next 3 months European Commission, Services surveyEvolution of the employment over the past 3 months European Commission, Services surveyExpectations of the employment over the next 3 months European Commission, Services surveyExpectations of the prices over the next 3 months European Commission, Services survey

GermanyEconomic Sentiment Indicator European CommissionConsumer Confidence Indicator European Commission, Consumers surveyFinancial situation over last 12 months European Commission, Consumers surveyFinancial situation over next 12 months European Commission, Consumers surveyGeneral economic situation over last 12 months European Commission, Consumers surveyGeneral economic situation over next 12 months European Commission, Consumers surveyMajor purchases at present European Commission, Consumers surveyMajor purchases over next 12 months European Commission, Consumers surveyUnemployment expectations over next 12 months European Commission, Consumers surveySavings at present European Commission, Consumers surveySavings over next 12 months European Commission, Consumers surveyStatement on financial situation of household European Commission, Consumers surveyPrice trends over last 12 months European Commission, Consumers surveyPrice trends over next 12 months European Commission, Consumers surveyIndustrial Confidence Indicator European Commission, Industry surveyProduction trend observed in recent months European Commission, Industry surveyProduction expectations for the months ahead European Commission, Industry surveyAssessment of order-book levels European Commission, Industry surveyAssessment of export order-book levels European Commission, Industry surveyAssessment of stocks of finished products European Commission, Industry surveyEmployment expectations for the months ahead European Commission, Industry surveySelling price expectations for the months ahead European Commission, Industry surveyConstruction Confidence Indicator European Commission, Construction surveyEvolution of your current overall order books European Commission, Construction surveyEmployment expectations over the next 3 months European Commission, Construction surveyBuilding activity development over the past 3 months European Commission, Construction surveyPrices expectations over the next 3 months European Commission, Construction surveyRetail trade Confidence Indicator European Commission, Retail trade surveyBusiness activity (sales) development over the past 3 months European Commission, Retail trade surveyVolume of stock currently hold European Commission, Retail trade surveyBusiness activity expectations over the next 3 months European Commission, Retail trade surveyOrders expectations over the next 3 months European Commission, Retail trade surveyEmployment expectations over the next 3 months European Commission, Retail trade surveyPrices expectations over the next 3 months European Commission, Retail trade surveyServices confidence indicator European Commission, Services surveyBusiness situation development over the past 3 months European Commission, Services surveyEvolution of the demand over the past 3 months European Commission, Services surveyExpectation of the demand over the next 3 months European Commission, Services surveyEvolution of the employment over the past 3 months European Commission, Services survey

United StatesCapacity utilization rate - industry The Conference BoardConsumer Confidence Index The Conference BoardConsumer Confidence Index - Expectations The Conference BoardISM Manufacturers Survey: Backlog of orders index Institute for Supply ManagementISM Manufacturers Survey: Customer inventories index Institute for Supply ManagementISM Manufacturers Survey: Employment index Institute for Supply ManagementISM Manufacturers Survey: Imports index Institute for Supply ManagementISM Manufacturers Survey: New export orders index Institute for Supply ManagementISM Manufacturers Survey: New orders index Institute for Supply ManagementISM Manufacturers Survey: Prices paid index Institute for Supply ManagementISM Manufacturers Survey: Production index Institute for Supply ManagementISM Manufacturers Survey: Supplier delivery index Institute for Supply ManagementISM Nonmanufacturers Survey: Business activity index Institute for Supply ManagementISM Nonmanufacturers Survey: Employment index Institute for Supply ManagementISM Nonmanufacturers Survey: Imports index Institute for Supply ManagementISM Nonmanufacturers Survey: New orders index Institute for Supply ManagementISM Nonmanufacturers Survey: Prices paid index Institute for Supply ManagementISM Purchasing Managers Index Institute for Supply ManagementUMICH CSS: Consumer Sentiment - Expectations University of Michigan Surveys of ConsumersUMICH CSS: Consumer Sentiment Index University of Michigan Surveys of Consumers

The NetherlandsEconomic Sentiment Indicator European CommissionConsumer Confidence Indicator European Commission, Consumers surveyFinancial situation over last 12 months European Commission, Consumers surveyFinancial situation over next 12 months European Commission, Consumers surveyGeneral economic situation over last 12 months European Commission, Consumers surveyGeneral economic situation over next 12 months European Commission, Consumers surveyMajor purchases at present European Commission, Consumers surveyMajor purchases over next 12 months European Commission, Consumers surveyUnemployment expectations over next 12 months European Commission, Consumers surveySavings at present European Commission, Consumers surveySavings over next 12 months European Commission, Consumers surveyStatement on financial situation of household European Commission, Consumers surveyPrice trends over last 12 months European Commission, Consumers surveyPrice trends over next 12 months European Commission, Consumers surveyIndustrial Confidence Indicator European Commission, Industry survey


26

Table 5 – Continued from previous pageSeries SourceProduction trend observed in recent months European Commission, Industry surveyProduction expectations for the months ahead European Commission, Industry surveyAssessment of order-book levels European Commission, Industry surveyAssessment of export order-book levels European Commission, Industry surveyAssessment of stocks of finished products European Commission, Industry surveyEmployment expectations for the months ahead European Commission, Industry surveySelling price expectations for the months ahead European Commission, Industry surveyConstruction Confidence Indicator European Commission, Construction surveyEvolution of your current overall order books European Commission, Construction surveyEmployment expectations over the next 3 months European Commission, Construction surveyBuilding activity development over the past 3 months European Commission, Construction surveyPrices expectations over the next 3 months European Commission, Construction surveyRetail trade Confidence Indicator European Commission, Retail trade surveyBusiness activity (sales) development over the past 3 months European Commission, Retail trade surveyVolume of stock currently hold European Commission, Retail trade surveyBusiness activity expectations over the next 3 months European Commission, Retail trade surveyOrders expectations over the next 3 months European Commission, Retail trade surveyEmployment expectations over the next 3 months European Commission, Retail trade surveyPrices expectations over the next 3 months European Commission, Retail trade surveyServices confidence indicator European Commission, Services surveyBusiness situation development over the past 3 months European Commission, Services surveyEvolution of the demand over the past 3 months European Commission, Services surveyExpectation of the demand over the next 3 months European Commission, Services surveyEvolution of the employment over the past 3 months European Commission, Services survey

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05 - Banco de Portugal · 2020. 4. 1. · 05 Working Papers | Lisbon 2020 •Banco de Portugal Av. Almirante Reis, 71 | 1150-012 Lisboa • • Edition Economics and Research Department