A Classifying Procedure for Signaling Turning Points

A Hidden Markov Model (HMM) is used to classify an out of sample observation vector into either of two regimes. This leads to a procedure for making probability forecasts for changes of regimes in a time series, i.e. for turning points. Instead o maximizing a likelihood, the model is estimated with respect to known past regimes. This makes it possible to perform feature extraction and estimation for different forecasting horizons. The inference aspect is emphasized by including a penalty for a wrong decision in the cost function. The method is tested by forecasting turning points in the Swedish and US economies, using leading data. Clear and early turning point signals are obtained, contrasting favourable with earlier HMM studies. Some theoretical arguments for this are given.Business Cycle; Feature Extraction; Hidden Markov Switching-Regime Model; Leading Indicator; Probability Forecast.

Density forecasting of the Dow Jones share index

The distribution of differences in logarithms of the Dow Jones share index is compared to the normal (N), normal mixture (NM) and a weighted sum of a normal and an Assymetric Laplace distribution (NAL). It is found that the NAL fits best. We came to this result by studying samples with high, medium and low volatility, thus circumventing strong heteroscedasticity in the entire series. The NAL distribution also fitted economic growth, thus revealing a new analogy between financial data and real growth.Density forecasting, heteroscedasticity, mixed Normal- Asymmetric Laplace distribution, Method of Moments estimation, connection with economic growth.

Factors Influencing the Expression of Gap Junction Forming Connexin Proteins in the Retina of Vertebrate Animals

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On the Probability Distribution of Economic Growth

Normality is often mechanically and without sufficient reason assumed in econometric models. In this paper three important and significantly heteroscedastic GDP series are studied. Heteroscedasticity is removed and the distributions of the filtered series are then compared to a Normal, a Normal-Mixture and Normal-Asymmetric Laplace (NAL) distributions. NAL represents a reduced and empirical form of the Aghion and Howitt (1992) model for economic growth, based on Schumpeter's idea of creative destruction. Statistical properties of the NAL distributions are provided and it is shown that NAL competes well with the alternatives.The Aghion-Howitt model, asymmetric innovations, mixed normal- asymmetric Laplace distribution, Kernel density estimation, Method of Moments estimation.

One-loop calculations for SUSY processes

Strategy and results for complete one-loop computations in the Minimal Supersymmetric Standard Model are reviewed, with applications to the calculation of SUSY mass spectra and SUSY-particle processes. Determination of renormalization constants and counterterms are described in the on-shell renormalization scheme, and a translation between $\bar{\rm DR}$ and on-shell parameters is given. As an example, cross sections for chargino and neutralino pair production in $e^+e^-$ annihilation are presented, complete at the one-loop level.Comment: Talk presented at Loops and Legs in Quantum Field Theory, Zinnowitz, Germany, April 200

Random Matrix Theory and the Failure of Macroeconomic Forecasts

By scientific standards, the accuracy of short-term economic forecasts has been poor, and shows no sign of improving over time. We form a delay matrix of time-series data on the overall rate of growth of the economy, with lags spanning the period over which any regularity of behaviour is postulated by economists to exist. We use methods of random matrix theory to analyse the correlation matrix of the delay matrix. This is done for annual data from 1871 to 1994 for 17 economies, and for post-war quarterly data for the US and the UK. The properties of the eigenvalues and eigenvectors of these correlation matrices are similar, though not identical, to those implied by random matrix theory. This suggests that the genuine information content in economic growth data is low, and so forecasting failure arises from inherent properties of the data.Comment: 15 Pages, 2 Figure

A classifying procedure for signaling turning points

A Hidden Markov Model (HMM) is used to classify an out of sample observation vector into either of two regimes. This leads to a procedure for making probability forecasts for changes of regimes in a time series, i.e. for turning points. Instead o maximizing a likelihood, the model is estimated with respect to known past regimes. This makes it possible to perform feature extraction and estimation for different forecasting horizons. The inference aspect is emphasized by including a penalty for a wrong decision in the cost function. The method is tested by forecasting turning points in the Swedish and US economies, using leading data. Clear and early turning point signals are obtained, contrasting favourable with earlier HMM studies. Some theoretical arguments for this are given. Business Cycle ; Feature Extraction ; Hidden Markov Switching-Regime Model ; Leading Indicator ; Probability Forecas

Analysis of the chargino and neutralino mass parameters at one-loop level

In the Minimal Supersymmetric Standard Model (MSSM) the masses of the neutralinos and charginos depend on the gaugino and higgsino mass parameters M, M' and $\mu$. If supersymmetry is realized, the extraction of these parameters from future high energy experiments will be crucial to test the underlying theory. We present a consistent method how on-shell parameters can be properly defined at one-loop level and how they can be determined from precision measurements. In addition, we show how a GUT relation for the parameters M and M' can be tested at one-loop level. The numerical analysis is based on a complete one-loop calculation. The derived analytic formulae are given in the appendix.Comment: 16 pages, 8 figure

Two-loop SUSY QCD corrections to the chargino masses in the MSSM

We have calculated the two-loop strong interaction corrections to the chargino pole masses in the DRbar'-scheme in the Minimal Supersymmetric Standard Model (MSSM) with complex parameters. We have performed a detailed numerical analysis for a particular point in the parameter space and found corrections of a few tenths of a percent. We provide a computer program which calculates chargino and neutralino masses with complex parameters including the one-loop corrections and all two-loop SQCD effects.Comment: 12 pages, 11 figures, references modified, clarifications adde