Co-evolution vs. Neural Networks; An Evaluation of UK Risky Money

The performance of a "capital certain" Divisia index constructed using the same components included in the Bank of England"s MSI plus national savings; a "risky" Divisia index constructed by adding bonds, shares and unit trusts to the list of assets included in the first index; and a capital certain simple sum index for comparison is compared. nce suggests that co-evolutionary strategies are superior to neural networks in the majority of cases. The risky money index performs at least as well as the Bank of England Divisia index when combined with interest rate information. Notably, the provision of long term interest rates improves the out-of-sample forecasting performance of the Bank of England Divisia index in all cases examinedEvolutionary Strategies, Risk Adjusted Divisia, Inflation, Neural Networks

Forecasting Consumer Spending from Purchase Intentions Expressed on Social Media

Consumer spending is a vital macroeconomic indicator. In this paper we present a novel method for predicting future consumer spending from social media data. In contrast to previous work that largely relied on sentiment analysis, the proposed method models consumer spending from purchase intentions found on social media. Our experiments with time series analysis models and machine-learning regression models reveal utility of this data for making short-term forecasts of consumer spending: for three- and seven-day horizons, prediction variables derived from social media help to improve forecast accuracy by 11% to 18% for all the three models, in comparison to models that used only autoregressive predictors

Non-linearities in mark-up on costs

Abstract This study employs an error-correction SETAR model to analyse the non-linearities in the behaviour of the mark-up on costs charged by the filling stations in the New York metropolitan area. While usual price transmission gained significant attention in the literature, the mark-up portion of the price has not been analysed to date. The results indicate that the adjustment to mark-ups to their long run values is non-linear, but the speeds with they adjust to their long-run values are equal across regimes for two out of three series analysed. For one of the series the adjustment is beneficial for the end consumers such that prices fall faster than they rise. The findings are somewhat surprising, indicating that there is no need for government intervention in the NY petroleum market.Rockets and feathers; asymmetry; petroleum; SETAR

Functional Structure and Approximation in Econometrics (book front matter)

This is the front matter from the book, William A. Barnett and Jane Binner (eds.), Functional Structure and Approximation in Econometrics, published in 2004 by Elsevier in its Contributions to Economic Analysis monograph series. The front matter includes the Table of Contents, Volume Introduction, and Section Introductions by Barnett and Binner and the Preface by W. Erwin Diewert. The volume contains a unified collection and discussion of W. A. Barnett's most important published papers on applied and theoretical econometric modelling.consumer demand, production, flexible functional form, functional structure, asymptotics, nonlinearity, systemwide models

Admissible monetary aggregates for the Euro area

We use the Fleissig and Whitney (2003) weak separability test to determine admissible levels of monetary aggregation for the Euro area. We find that the Euro area monetary assets in M2 and M3 are weakly separable and construct admissible Divisia monetary aggregates for these assets. We evaluate the Divisia aggregates as indicator variables, building on Nelson (2002), Reimers (2002), and Stracca (2004). Specifically, we show that real growth of the admissible Divisia aggregates enter the Euro area IS curve positively and significantly for the period from 1980 to 2005. Out of sample, we show that Divisia M2 and M3 appear to contain useful information for forecasting Euro area inflation

Analyzing Divisia Rules Extracted from a Feedforward Neural Network

This paper introduces a mechanism for generating a series of rules that characterize the money-price relationship, defined as the relationship between the rate of growth of the money supply and inflation. Division component data is used to train a selection of candidate feedforward neural networks. The selected network is mined for rules, expressed in human-readable and machine-executable form. The rule and network accuracy are compared, and expert commentary is made on the readability and reliability of the extracted rule set. The ultimate goal of this research is to produce rules that meaningfully and accurately describe inflation in terms of Divisia component dataset

Dynamics in systematic liquidity

We develop the principal component analysis (PCA) approach to systematic liquidity measurement by introducing moving and expanding estimation windows. We evaluate these methods along with traditional estimation techniques (full sample PCA and market average) in terms of ability to explain (1) cross-sectional stock liquidity and (2) cross-sectional stock returns. For several traditional liquidity measures our results suggest an expanding window specification for systematic liquidity estimation. However, for price impact liquidity measures we find support for a moving window specification. The market average proxy of systematic liquidity produces the same degree of commonality, but does not have the same ability to explain stock returns as the PCA-based estimates.Liquidity (Economics)