Survey data as coincident or leading indicators

Frale, Cecilia; Marcellino, Massimiliano; Mazzi, Gian Luigi; Proietti, Tommaso

doi:10.1002/for.1142

Cited by 37 publications

(25 citation statements)

References 12 publications

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“…Examples are the Consumer Confidence Indicator (Białowolski 2015;Gelper et al 2007), and the Economic Sentiment Indicator (Gelper and Christophe 2010) constructed by the European Commission (European Economy, 2014) by aggregating the sectoral indicators (Frale et al 2010;Taylor and McNabb 2007 Robinzonov et al (2012) used the IBCI and other aggregate indicators from surveys as exogenous variable for industrial production forecasting.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Approach to Construct Survey-Based Indicators by Means of Evolutionary Algorithms

2016

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In this paper we propose a data-driven approach for the construction of survey-based indicators using large data sets. We make use of agents' expectations about a wide range of economic variables contained in the World Economic Survey, which is a tendency survey conducted by the Ifo Institute for Economic Research. By means of genetic programming we estimate a symbolic regression that links survey-based expectations to a quantitative variable used as a yardstick, deriving mathematical functional forms that approximate the target variable. We use the evolution of GDP as a target. This set of empirically-generated indicators of economic growth, are used as building blocks to construct an economic indicator. We compare the proposed indicator to the Economic Climate Index, and we evaluate its predictive performance to track the evolution of the GDP in ten European economies. We find that in most countries the proposed indicator outperforms forecasts generated by a benchmark model.

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Section: Introductionmentioning

confidence: 99%

A Data-Driven Approach to Construct Survey-Based Indicators by Means of Evolutionary Algorithms

2016

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“…To summarize managers' subjective assessment of economic variables, there are different approaches in using and interpreting business survey results as leading or coincidence indicators [7]. These indicators are used to explain and predict changes in macroeconomic variables.…”

Section: Business and Consumer Survey -Croatian Experience And Literamentioning

confidence: 99%

“…Many researches and papers incorporate the psychological sentiment (obtained by Business and Consumer Surveys; BCS) in macroeconomic modelling [19,11,13,7,4,21,17]. Namely, BCS offer direct assessments of the otherwise intangible factors such as economic agents' perceptions and expectations.…”

Section: Introductionmentioning

confidence: 99%

Can confidence indicators forecast the probability of expansion in Croatia?

Čižmešija

Erjavec

2016

Croat. Oper. Res. Rev.

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Abstract. The aim of this paper is to investigate how reliable are confidence indicators in forecasting the probability of expansion. We consider three Croatian Business Survey indicators: the Industrial Confidence Indicator (ICI), the Construction Confidence Indicator (BCI) and the Retail Trade Confidence Indicator (RTCI). The quarterly data, used in the research, covered the periods from 1999/Q1 to 2014/Q1. Empirical analysis consists of two parts. The non-parametric Bry-Boschan algorithm is used for distinguishing periods of expansion from the period of recession in the Croatian economy. Then, various nonlinear probit models were estimated. The models differ with respect to the regressors (confidence indicators) and the time lags. The positive signs of estimated parameters suggest that the probability of expansion increases with an increase in Confidence Indicators. Based on the obtained results, the conclusion is that ICI is the most powerful predictor of the probability of expansion in Croatia.

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“…Application of dynamic factor models to forecasting macroeconomic time series has been extensively developed in the literature (Baranowski et al, 2010;Boivin and Ng, 2006;Reijer, 2012;Stock and Watson, 2002). Nevertheless, with minor exceptions it has rarely focused on defining the dynamic factors with tendency survey data (Frale et al, 2010;Hansson et al, 2005;Kaufmann and Scheufele, 2013). However, it should be underlined that dynamic factor models have significant advantages over other approaches to modelling.…”

Section: _________________________mentioning

confidence: 99%

Bayesian Averaging vs. Dynamic Factor Models for Forecasting Economic Aggregates with Tendency Survey Data

2015

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The article compares forecast quality from two atheoretical models. Neither method assumed a priori causality and forecasts were generated without additional assumptions about regressors. Tendency survey data was used within the Bayesian averaging of classical estimates (BACE) framework and dynamic factor models (DFM). Two methods for regressor selection were applied within the BACE framework: frequentist averaging (BA) and frequentist (BF) with a collinearity-corrected version of the latter (BFC). Since models yielded multiple forecasts for each period, an approach to combine them was implemented. Results were assessed using in-and out-of-sample prediction errors. Although results did not vary significantly, best performance was observed from Bayesian models adopting the frequentist approach. Forecast of the unemployment rate were generated with the highest precision, followed by rate of GDP growth and CPI. It can be concluded that although these methods are atheoretical, they provide reasonable forecast accuracy, no worse to that expected from structural models. A further advantage to this approach is that much of the forecast procedure can be automated and much influence from subjective decisions avoided. JEL C10 C38 C83 E32 E37

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Survey data as coincident or leading indicators

Cited by 37 publications

References 12 publications

A Data-Driven Approach to Construct Survey-Based Indicators by Means of Evolutionary Algorithms

A Data-Driven Approach to Construct Survey-Based Indicators by Means of Evolutionary Algorithms

Can confidence indicators forecast the probability of expansion in Croatia?

Bayesian Averaging vs. Dynamic Factor Models for Forecasting Economic Aggregates with Tendency Survey Data

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