Econometric Methods of Signal Extraction

The Wiener-Kolmogorov signal extraction filters, which are widely used in econometric analysis, are constructed on the basis of statistical models of the processes generating the data. In this paper, such models are used mainly as heuristic devices that are to be specified in whichever ways are appropriate to ensure that the filters have the desired characteristics. The digital Butterworth filters, which are described and illustrated in the paper, are specified in this way. The components of an econometric time series often give rise to spectral structures that fall within well-defined frequency bands that are isolated from each other by spectral dead spaces. We find that the finite-sample Wiener-Kolmogorov formulation lends itself readily to a specialisation that is appropriate for dealing with band-limited components.Signal extraction, Linear filtering, Frequency-domain analysis, Trend estimation

Diffusion of Emissions Abating Technology

The environmental Kuznets curve (EKC) has been extensively criticized on econometric and theoretical grounds. Recent econometric results and case studies show that national emissions of important pollutants are monotonic in income but changes in technology can lead over time to reductions in pollution - a lowering of the EKC - and that pollution reducing innovations and standards may be adopted with relatively short time lags in some developing countries. This study combines the recent literature on measuring environmental efficiency and technological change using production frontier methods with the use of the Kalman filter - a time series method for signal extraction - to model the state of abatement technology in a panel of countries over time. The EKC is reformulated as the best practice technology frontier - countries' position relative to the frontier reflects the degree to which they have adopted best practice. The results are used to determine whether countries are converging to best practice over time and how many years it will take each country to achieve current best practice. The model is applied to sulfur dioxide emissions from sixteen mainly developed countries.

Extraction of the underlying structure of systematic risk from non-Gaussian multivariate financial time series using independent component analysis: Evidence from the Mexican stock exchange

Regarding the problems related to multivariate non-Gaussianity of financial time series, i.e., unreliable results in extraction of underlying risk factors -via Principal Component Analysis or Factor Analysis-, we use Independent Component Analysis (ICA) to estimate the pervasive risk factors that explain the returns on stocks in the Mexican Stock Exchange. The extracted systematic risk factors are considered within a statistical definition of the Arbitrage Pricing Theory (APT), which is tested by means of a two-stage econometric methodology. Using the extracted factors, we find evidence of a suitable estimation via ICA and some results in favor of the APT.Peer ReviewedPostprint (published version

Signal Extraction, Maximum Likelihood Estimation and the Start-up Problem

In this paper, we portray the essential features of the finite-sample signal extraction problem in both the stationary and the nonstationary cases. The computational procedures can be simplified in the light of our analysis. An important outcome of the analysis is a demonstration that the start-up problem can be handled far more easily that one might expect from a passing acquaintance with the usual practices.Signal extraction, Linear filtering, Trend estimation

Improved Frequency-selective Filters

This paper gives an account of some techniques for designing recursive frequency-selective filters which can be applied to data sequences of limited duration which may be nonstationary. The designs are based on the Wiener-Kolmogorov theory of signal extraction which employs a statistical model of the processes generating the data. The statistical model may be regarded as an heuristic device which is designed with a view to ensuring that the resulting signal-extraction filters have certain preconceived properties.Signal extraction, Linear filtering, Filter design, Trend estimation, Frequency-domain analysis

Are Inflation Expectations Rational?

Simple econometric tests reported in the literature consistently report what appears to be a bias in inflation expectations. These results are commonly interpreted as constituting evidence overturning the hypothesis of rational expectations. In this paper, we investigate the validity of such an interpretation. The main tool utilized in our investigation is a computational dynamic general equilibrium model capable of generating aggregate behavior similar to the data along a number of dimensions. By construction, the model embedded the assumption of rational expectations. Standard regressions run on equilibrium realizations of inflation and inflation expectations nevertheless reveal an apparent bias in inflation expectations. In these simulations, the null hypothesis of rational expectations is incorrectly rejected in a large percentage of cases; a result that casts some doubt on conventional interpretations of the evidence.

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

Extracting and analyzing the warming trend in global and hemispheric temperatures

This article offers an updated and extended attribution analysis based on recently published versions of temperature and forcing datasets. It shows that both temperature and radiative forcing variables can be best represented as trend stationary processes with structural changes occurring in the slope of their trend functions and that they share a common secular trend and common breaks, largely determined by the anthropogenic radiative forcing. The common nonlinear trend is isolated, and further evidence on the possible causes of the current slowdown in warming is presented. Our analysis offers interesting results in relation to the recent literature. Changes in the anthropogenic forcings are directly responsible for the hiatus, while natural variability modes such as the Atlantic Multidecadal Oscillation, as well as new temperature adjustments, contribute to weaken the signal. In other words, natural variability and data adjustments do not explain in any way the hiatus; they simply mask its presence