The outlook of the Spanish economy in the first quarter of 1993.

The Spanish economy has experienced a quick integration into the European framework since the entrance into the EC in 1986. Thus, the percentage of total Spanish exports bound to Europe increased from 49.7% in 1980-1985 to 71.2% in 1992, and the percentage of total Spanish imports coming from Europe increased from 33% to 60.7%. This mean s that an analysis of the Spanish economy should be done by connecting it with the evolution experienced by the other European partners.Coyuntura económica; Crisis económica; España; S. XX; Comunidad Europea;

Comments on time-series analysis, forecasting and econometric modelling: The structural econometric modelling, time-series analysis (SEMTSA) approach, by A. Zellner.

Professor Zellner has greatly contributed to econometrics in many aspects. This paper compiles a line of research developed by him and associates tha t goes back to the early 1970s, in which time-series techniques and Bayesian analysis are used in the construction of econometric models.Modelo econométrico; Modelo matemático; Análisis de series temporales;

Econometric modelling for short-term inflation forecasting in the EMU.

Inflation forecasts are in great demand by agents in financial markets and monetary authorities that also require frequent updates. In the case of the EMU, these can be done monthly using Harmonised Indices of Consumer Prices (HICP). Analysing the HICP it was detected in a previous paper that breaking down the HICP in a vector of n sectors so that each price index component corresponds to a group of relatively homogeneous markets, or in a vector of n countries, there are in both cases fewer than (n-1) cointegration relationships. It can then be said that the components of the index are not fully cointegrated in the sense that there is more than one common trend in the HICP vector. In such a case, one way to increase sample information about the HICP trend is to consider the n price components and approach disaggregated econometric modelling. The paper shows that the breakdown that joins both criteria by considering a price index for each large group of markets in each country improves EMU inflation forecasts and establishes a framework in which general and specific explanatory variables and non-linear structures can be introduced for further improvements. The paper shows that VEqCM of ten price indices " two sectors by five geographical areas " including three cointegration relationships, with a sector-block diagonal restriction, generates forecasts of the year-on-year inflation rate in the HICP such that their error variances are one third or one fifth of the forecast errors from an aggregate ARIMA model, depending whether the horizon is three or twelve months. This vector model also provides better forecasts than single-equation models or alternative vector models for the components. A successful formulation of the vector model requires the inclusion of dummy variables to take account of special events such as seasonality changes due to sales, the introduction of the euro, Greece becoming a member of the EMU, the introduction of ecological taxes, bad weather periods and others events altering the evolution of unprocessed food prices, etc. and the inclusion of international Brent prices in euros. With the breakdown used in the paper it is shown that a usual measure of core inflation is not a good predictor of total inflation, but the interest in core inflation could lie in the fact that its corresponding price index is constructed with price indices in which innovations are more persistent than those in the other consumer price indexes excluded from the core. The disaggregated forecasts presented in this paper are useful for policy-making because they tell us which sectors have the highest expected inflation rates and how persistent are the shocks affecting different sectors

Parsimony and omitted factors: the airline model and the Census X-11 assumptions

El tipo de modelo Arima para el que el metodo de ajuste estacional X-11 es adecuado se ha identificado como (1-L)(1-L12)Xt=G(L)at, (CX), en donde G(L) es de orden 26. En este documento se aproxima el modelo CX mediante un modelo Arima (1,1,2)(0,1,1), con raices complejas en el factor MA regular y se demuestra que tal modelo tiene un factor de estabilidad -mayor potencia espectral en frecuencias bajas- que no esta presente en el "modelo de lineas aereas" propuesto por Box y Jenkins

Using high-frequency data and time series models to improve yield management

We show the potential contribution of time series models (TSM) to the analysis of high frequency (less than monthly) time series of economic activity. The evolution of the series is induced by stable patterns of behavior of economic agents; but these patterns are so complex that simple smoothing techniques or subjective forecasting can not consider all underlying factors and TSM are needed if a full efficient analysis is to be carried out. The main ideas are illustrated with an apllication to Spanish daily electricity consumption

Model based measures of contemporaneous economic growth

In short term economic reports the use of different growth rate measures is misleading. This paper studies the question of using an unique measure -underlying growth-, which should be smoothed and in phase with the monthly increments of the corresponding variable. All possible solutions require the use of forecasts at the end of the sample. The paper proposes the use of models to obtain forecasts¡ then the contemporaneous underlying growth is a model based measure. An evaluation of the effects of the last innovations in the underlying growth can be obtained by comparing its last estimation with previous one. An example of its application, based on inflation analysis, is presented

Forecasting aggregate and disaggregates with common features.

The paper is focused on providing joint consistent forecasts for an aggregate and all its components and in showing that this indirect forecast of the aggregate is at least as accurate as the direct one. The procedure developed in the paper is a disaggregated approach based on single-equation models for the components, which take into account common stable features which some components share between them. The procedure is applied to forecasting euro area, UK and US inflation and it is shown that its forecasts are significantly more accurate than the ones obtained by the direct forecast of the aggregate or by dynamic factor models. A by-product of the procedure is the classification of a large number of components by restrictions shared between them, which could be also useful in other respects, as the application of dynamic factors, the definition of intermediate aggregates or the formulation of models with unobserved componentsCommon trends; Common serial correlation; Inflation; Euro Area; UK; US; Cointegration; Single-equation econometric models;

Forecasting monthly us consumer price indexes through a disaggregated I(2) analysis

In this paper we carry a disaggregated study of the monthly US Consumer Price Index (CPI). We consider a breakdown of US CPI in four subindexes, corresponding to four groups of markets: energy, food, rest of commodities and rest of services. This is seen as a relevant way to increase information in forecasting US CPI because the supplies and demands in those markets have very different characteristics. Consumer prices in the last three components show I(2) behavior, while the energy subindex shows a lower order of integration, but with segmentation in the growth rate. Even restricting the analysis to the series that show the same order of integration, the trending behavior of prices in these markets can be very different. An I(2) cointegration analysis on the mentioned last three components shows that there are several sources of nonstationarity in the US CPI components. A common trend analysis based on dynamic factor models confirms these results. The different trending behavior in the market prices suggests that theories for price determinations could differ through markets. In this context, disaggregation could help to improve forecasting accuracy. To show that this conjecture is valid for the non-energy US CPI, we have performed a forecasting exercise of each component, computed afterwards the aggregated value of the non energy US CPI and compared it with the forecasts obtained directly from a model for the aggregate. The improvement in one year ahead forecasts with the disaggregated approach is more than 20%, where the root mean squared error is employed as a measure of forecasting performance

Laudatio on the occasion of the Investure of Professor John Denis Sargan with the Degree of Doctor Honoris Causa of the Universidad Carlos III, 2 de Febrero 1993

Publicad