Antoni Espasa

This paper focuses on the econometric methodology developed by Professor David Hendry with his associates along several decades. The paper comments on the statistical foundations of the methodology, which are based on the probability approach in Econometrics introduced by Haavelmo. The paper proposes eleven main features to sum up the methodology and they are discussed with detailed. A pivotal point in the methodology is the, Local Data generation Process (LDGP), which is unknown at the beginning and to discover it, it must be nested in a suitable General Unrestricted Model (GUM). The GUM must include variables from possibly relevant economic theories and all other types of variables that may be necessary to represent the economic system under study, including the indicator variables employed in the Indicator Saturation Estimation (ISE). Professor Hendry invented the ISE to capture as “many contaminating influences as possible” affecting the data. From the GUM a reduction process to discover a final congruent model is carried out by the procedure from general-to-specific. Usually, there are more variables than observations and a sophisticated multiple-path search using segmentation by blocks has been designed. All the process is automated by a machine-learning program, Autometrics. This methodology has also been incorporated into Climate Econometrics.

The Bulletin of EU & US Inflation and Macroeconomic Analysis (BIAM) is a monthly publication that has been reporting real time analysis and forecasts for inflation and other macroeconomic aggregates for the Euro Area, the US and Spain since 1994. The BIAM inflation forecasting methodology stands on working with useful disaggregation schemes, using leading indicators when possible and applying outlier correction. The paper relates this methodology to corresponding topics in the literature and discusses the design of disaggregation schemes. It concludes that those schemes would be useful if they were formulated according to economic, institutional and statistical criteria aiming to end up with a set of components with very different statistical properties for which valid single-equation models could be built. The BIAM assessment, which derives from a new observation, is based on (a) an evaluation of the forecasting errors (innovations) at the components’ level. It provides information on which sectors they come from and allows, when required, for the appropriate correction in the specific models. (b) In updating the path forecast with its corresponding fan chart. Finally, we show that BIAM real time Euro Area inflation forecasts compare successfully with the consensus from the ECB Survey of Professional Forecasters, one and two years ahead.

In this article we propose a methodology for estimating the GDP of a country’s different regions, providing quarterly profiles for the annual official observed data. Thus the article offers a new instrument for short-term monitoring that allows the analysts to quantify the degree of synchronicity among regional business cycles. Technically, we combine time-series models with benchmarking methods to process short-term quarterly indicators and to estimate quarterly regional GDPs ensuring their temporal and transversal consistency with the National Accounts data. The methodology addresses the issue of nonadditivity, explicitly taking into account the transversal constraints imposed by the chain-linked volume indexes used by the National Accounts, and provides an efficient combination of structural as well as short-term information. The methodology is illustrated by an application to the Spanish economy, providing real-time quarterly GDP estimates, that is, with a minimum compilation delay with respect to the national quarterly GDP. The estimated quarterly data are used to assess the existence of cycles shared among the Spanish regions.

This article studies the performance of different modelling strategies for 969 and 600 monthly price indexes disaggregated by sectors and geographical areas in Spain, regions and in the Euro Area 12 (EA12) countries. We also provide, by means of spatial bi-dimensional vector equilibrium correction models for all pairs of prices between neighbours, a description of spatial cointegration restrictions that could be useful for understanding price setting within an economy. We study the relevance of the regional disaggregation by using the proposed models to forecast the corresponding headline inflation and testing whether it is more accurate than alternative forecasts based on aggregated models. The results for Spain show that this is the case. Country disaggregation forecasts are also reliable for the EA12, but only because derived headline inflation forecasting is not significantly worse than alternative forecasts. The models in this article can be used for competitive analysis and other macro and regional analysis.

The authors elaborate on three basic ideas that should guide the implementation of business intelligence tools. First, the authors advocate for closing the gap between structured information and contextual information. Second, they emphasize the need for adopting the point of view of the organization to assess the relevance of any proposal. In the third place, they remark that any new tool is expected to become a relevant instrument to enhance the learning of the organization and to generate explicit knowledge. To illustrate their point, they discuss how to set up a forecasting support system to predict electricity consumption that converts raw time series data into market intelligence, to meet the needs of a major organization operating at the Spanish electricity markets.