126 research outputs found
New proposals for the quantification of qualitative survey data
No full textHASH(0x1009bfd18)spectral envelope , non‐Gaussian state space models , cumulative logit model ,
Revisions in official data and forecasting
No full textThis paper deals with the topic of revisions in macroeconomic Italian data with the aim of investigating whether consecutive vintages published by the National Statistical Institute contain useful information for economic analysis and forecasting. The rationality of the revisions process is tested considering the complete history of data and an application to show the usefulness of revisions for improving the precision of forecasts is proposed. The results on Italian GDP show that embedding the revision process in a dynamic factor model helps to reduce the forecast error in the short term
New proposals for the quantification of qualitative survey data
No full textIn this paper we deal with several issues related to the quantification of business
surveys. In particular, we propose and compare new ways of scoring the ordinal
responses concerning the qualitative assessment of the state of the economy, such
as the spectral envelope and cumulative logit unobserved components models, and
investigate the nature of seasonality in the series. We conclude with an evaluation of
the type of business cycle fluctuations that is captured by the qualitative surveys
Survey data as coincident or leading indicators
No full textIn this paper we propose a monthly measure for the euro area gross domestic product (GDP) based on a small-scale factor model for mixed-frequency data, featuring two factors: the first is driven by hard data, whereas the second captures the contribution of survey variables as coincident indicators. Within this framework we evaluate both the in-sample contribution of the second survey-based factor, and the short-term forecasting performance of the model in a pseudo-real-time experiment. We find that the survey-based factor plays a significant role for two components of GDP: industrial value added and exports. Moreover, the two-factor model outperforms in terms of out-of-sample forecasting accuracy the traditional autoregressive distributed lags (ADL) specifications and the single-factor model, with few exceptions. Copyright © 2009 John Wiley & Sons, Ltd.
EUROMIND: A monthly indicator of the euro area economic conditions
No full textContinuous monitoring of the evolution of the economy is fundamental for the decisions of public and private decision makers. The paper proposes EUROMIND, which is a new monthly indicator of the euro area economic conditions, based on tracking real gross domestic product monthly, relying on information provided in the Eurostat Euro-IND database. EUROMIND has several original economic and statistical features. First, it considers both the output and the expenditure sides of the economy, as it provides a monthly estimate of the value added of the six branches of economic activity and of the main gross domestic product components by type of expenditure (final consumption, gross capital formation and net exports), and combines the estimates with optimal weights reflecting their relative precision. Second, the indicator is based on information at both the monthly and the quarterly level, modelled with a dynamic factor specification cast in state space form. Third, since estimation of the multivariate dynamic factor model with mixed frequency data can be numerically complex, computational efficiency is achieved by implementing univariate filtering and smoothing procedures. Finally, special attention is paid to chain linking and its implications, via a multistep procedure that exploits the additivity of the volume measures expressed at the prices of the previous year
EuroMInd-C: a Disaggregate Monthly Indicator of Economic Activity for the Euro Area and member countries
No full textThe paper deals with the estimation of monthly indicators of economic activity for the Euro area and
its largest member countries that possess the following attributes: relevance, representativeness and
timeliness. Relevance is determined by comparing our monthly indicators to the gross domestic product
at chained volumes, as the most important measure of the level of economic activity. Representativeness
is achieved by considering a very large number of (timely) time series of monthly indicators relating to
the level of economic activity, providing a more or less complete coverage. The indicators are modelled
using a large-scale parametric factor model. We discuss its specification and provide details of the
statistical treatment. Computational efficiency is crucial for the estimation of large-scale parametric
factor models of the dimension used in our application (considering about 170 series). To achieve it,
we apply state-of-the-art state space methods that can handle temporal aggregation, and any pattern of
missing values
A Systemic Approach to Estimating the Output Gap for the Italian Economy
No full textEstimating potential output and the corresponding output gap plays a key role, not only for inflation forecasting and the assessment of the economic cycle, but also for the fiscal governance of the European Union (EU). Potential output is, however, an unobservable and extremely uncertain variable. Empirical measurements differ considerably depending on the econometric approach adopted, the specification of the data generating process and the dataset used. The method adopted at the EU level, which was agreed within the Output Gap Working Group, has been subject to considerable debate. The fiscal councils of the various Member States contribute to the discussion over the output gap modelling. This paper aims at estimating the potential output of the Italian economy, using a combination of five different models proposed by the relevant literature. More specifically, in addition to a statistical filter, we use unobserved components models based on the Phillips curve, the Okun law and the production function. The approach adopted allows to reconcile the parsimony of the econometric specification with the economic interpretation of the results. Estimates of the output gap obtained with the five selected models present important properties: low pro-cyclicity, stability with respect to the preliminary data and consistency with the economic theory. The use of multiple models also enables the construction of confidence bands for the output gap estimates, which are helpful for policy analysis. In the empirical application for Italy, estimates and forecasts of the output gap recently produced by relevant organisations tend to fall within the confidence interval calculated on the basis of the five selected models
Do Surveys Help in Macroeconomic Variables Disaggregation and Estimation?
No full textThis paper explores the potential of Business Survey data for the estimation and disaggregation of macroeconomic variables at higher frequency. We propose a multivariate approach which is an extension of the Stock and Watson (1991) dynamic factor model, considering more than one common factor and low-frequency cycles. The multivariate model is cast in State Space Form and the temporal aggregation constraint is converted into a problem of missing values. An application in real time for the value added of the Industry sector in the Euro area is presented.Temporal Disaggregation. Multivariate State Space Models. Dynamic factor
FaMIDAS: A Mixed Frequency Factor Model with MIDAS structure
No full textIn this paper a dynamic factor model with mixed frequency is proposed (FaMIDAS), where the past observations of high frequency indicators are used following the MIDAS approach. This structure is able to represent with richer dynamics the information content of the economic indicators and produces smoothed factors and forecasts. In addition, it is particularly suited for real time forecast as it reduces the problem of the unbalanced data set and of the revisions in preliminary data. In the empirical application we specify and estimate a FaMIDAS to forecast Italian quarterly GDP. The short-term forecasting performance is evaluated against other mixed frequency models in a pseudo-real time experiment, also allowing for pooled forecast from factor models.Mixed frequency models, Dynamic factor Models, MIDAS, Forecasting
Identifying the current and future status of freshwater connectivity corridors in the Amazon Basin
No full textThis repository contains supplementary data and technical documentation for the research article by
B. Caldas, M.L. Thieme, N. Shahbol, M. Eduarda Coelho, G. Grill, P.A. Van Damme, R. Aranha, C. Cañas, C. Fagundes, N. Frale, E.E. Herrera-Collazos, C. Jézéquel, M. Montoya, F. Mosquera- Guerra, M. Oliveira-da-Costa, M. Paschoalini, P. Petry, T. Oberdorff, F. Trujillo, P.A. Tedesco, M. Lambert Ribeiro. Identifying the current and future status of freshwater connectivity corridors in the Amazon Basin. Conservation Science and Practice. DOI: 10.1111/csp2.12853</p
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