Forecasting Industrial Production and the Early Detection of Turning Points

In this paper we propose a simple model to forecast industrial production in Italy up to 6 months ahead. We show that the forecasts produced using the model outperform some popular forecasts as well as those stemming from an ARIMA model used as a benchmark and those from some single equation alternative models. We show how the use of these forecasts can improve the estimate of a cyclical indicator and the early detection of turning points for the manufacturing sector. This is of paramount importance for short-term economic analysis.Forecasting, VAR Models, Industrial production, Cyclical indicators.

Dating the Italian Business Cycle: A Comparison of Procedures

The problem of dating the business cycle has recently received many contributions, with a lot of proposed statistical methodologies, parametric and non parametric. Despite of this, only a few countries produce an official dating of the business cycle. In this work we try to apply some procedures for an automatic dating of the Italian business cycle in the last thirty years, checking differences among various methodologies and with the ISAE chronology. To this end parametric as well as non parametric methods are employed. The analysis is carried out both aggregating results from single time series and directly in a multivariate framework. The different methods are also evaluated with respect to their ability to timely track turning points. KEYWORDS: signal extraction, turning points, parametric methods, nonparametric methodssignal extraction, turning points, parametric methods, nonparametric methods

The Choice of Time Interval in Seasonal Adjustment: A Heuristic Approach

A typical problem of the seasonal adjustment procedures arises when the series to be adjusted is subject to structural breaks. In fact, using the full span of the series can result in a biased estimation of the ”true” seasonal adjusted series, with unclear evidence showed by the usual diagnostic tests. In these cases the researcher has to decide where to cut-o the observed series to obtain a homogeneous span; this is generally performed by a simple visual inspection studies of the graph of the series and/or using a-priori information about the occurrence of the break. In this paper we propose a statistical criterion based on a distance measure between filters, evaluating its performance with Monte Carlo experiments.Linear filters, Structural break, Distance.

Forecasting Industrial Production and the Early Detection of Turning Points

In this paper we propose a simple model to forecast industrial production in Italy. We show that the forecasts produced using the model outperform some popular forecasts as well as those stemming from a trading days- and outlier-robust ARIMA model used as a benchmark. We show that the use of appropriately selected leading variables allows to produce up to twelve-step ahead reliable forecasts. We show how and why the use of these forecasts can improve the estimate of a cyclical indicator and the early detection of turning points for the manufacturing sector. This is of paramount importance for short-term economic analysis.Forecasting, Forecast Encompassing, VAR Models, Industrial Production, Cyclical Indicators

All Sky Camera, LIDAR and Electric Field Meter: auxiliary instruments for the ASTRI SST-2M prototype

ASTRI SST-2M is the end-to-end prototype telescope of the Italian National Institute of Astro- physics, INAF, designed to investigate the 10-100 TeV band in the framework of the Cherenkov Telescope Array, CTA. The ASTRI SST-2M telescope has been installed in Italy in September 2014, at the INAF ob- serving station located at Serra La Nave on Mount Etna. The telescope is foreseen to be completed and fully operative in spring 2015 including auxiliary instrumentation needed to support both operations and data anal- ysis. In this contribution we present the current status of a sub-set of the auxiliary instruments that are being used at the Serra La Nave site, namely an All Sky Camera, an Electric Field Meter and a Raman Lidar devoted, together with further instrumentation, to the monitoring of the atmospheric and environmental conditions. The data analysis techniques under development for these instruments could be applied at the CTA sites, where similar auxiliary instrumentation will be installed.Comment: Proceedings of the 2nd AtmoHEAD Conference, Padova (Italy) May 19-21, 201

Consumer confidence and consumption forecast: a non-parametric approach

The consumer confidence index is a highly observed indicator among short-term analysts and news reporters and it is generally considered to convey some useful information about the short-term evolution of consumer expenditure. Notwithstanding this, its usefulness in forecasting aggregate consumption is sometimes questioned in empirical studies. Overall, the conclusions seem to be that the extensive press coverage about this indicator is somewhat undue. Nevertheless, from time to time, attention revamps on consumer confidence, especially when turns of the business cycle are expected and/or abrupt changes in this indicator occur. Some authors argue that in such events consumer confidence is a more relevant variable in predicting consumption. This fact can be a signal that a linear functional form is inadequate to explain the relationship between these two variables. Nevertheless, the choice of a suitable non-linear model is not straightforward. Here I propose that a non-parametric model can be a possible choice, in order to explore the usefulness of confidence in forecasting consumption, without making too restrictive assumptions about the functional form to use

Analysis of outsourcing conditions for freight transport and logistics among manufacturing companies: insights from a review of data and a field investigation

n/

Non-linear relation between industrial production and business surveys data

n this paper I compare different models, a linear and a non-linear one, for forecasting industrial production by means of some related indicators. I claim that the difficulties associated with the correct identification of a non-linear model could be a possible cause of the often observed worse performance of non-linear models with respect to linear ones observed in the empirical literature. To cope with this issue I use a non-linear non-parametric model. The results are promising, as the forecasting performance shows a clear improvement over the linear parametric model

Forecasting Using Functional Coefficients Autoregressive Models

The use of linear parametric models for forecasting economic time series is widespread among practitioners, in spite of the fact that there is a large evidence of the presence of non-linearities in many of such time series. However, the empirical results stemming from the use of non-linear models are not always as good as expected. This has been sometimes associated to the difficulty in correctly specifying a non-linear parametric model. I this paper I cope with this issue by using a more general non-parametric approach, which can be used both as a preliminary tool for aiding in specifying a suitable parametric model and as an autonomous modelling strategy. The results are promising, in that the non-parametric approach achieve a good forecasting record for a considerable number of series