Breaking and Nonlinear Trends

2019 ◽  
pp. 103-119
Author(s):  
Terence C. Mills
Keyword(s):  
Nonlinear Trends

scholarly journals Non-Parametric Generalized Additive Models as a Tool for Evaluating Policy Interventions

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 299
Author(s):  
Jaime Pinilla ◽  
Miguel Negrín
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Linear Trend ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Linear Regression Models ◽  
Non Linear ◽  
Nonlinear Trends ◽  
The Impact ◽  
Non Parametric

The interrupted time series analysis is a quasi-experimental design used to evaluate the effectiveness of an intervention. Segmented linear regression models have been the most used models to carry out this analysis. However, they assume a linear trend that may not be appropriate in many situations. In this paper, we show how generalized additive models (GAMs), a non-parametric regression-based method, can be useful to accommodate nonlinear trends. An analysis with simulated data is carried out to assess the performance of both models. Data were simulated from linear and non-linear (quadratic and cubic) functions. The results of this analysis show how GAMs improve on segmented linear regression models when the trend is non-linear, but they also show a good performance when the trend is linear. A real-life application where the impact of the 2012 Spanish cost-sharing reforms on pharmaceutical prescription is also analyzed. Seasonality and an indicator variable for the stockpiling effect are included as explanatory variables. The segmented linear regression model shows good fit of the data. However, the GAM concludes that the hypothesis of linear trend is rejected. The estimated level shift is similar for both models but the cumulative absolute effect on the number of prescriptions is lower in GAM.

scholarly journals The Impact of Vibrational Entropy on the Segregation of Cu to Antiphase Boundaries in Fe3Al

2021 ◽  
Vol 7 (8) ◽  
pp. 108
Author(s):  
Martin Friák ◽  
Miroslav Černý ◽  
Mojmír Šob
Keyword(s):  
Magnetic Moments ◽  
Energy Difference ◽  
Extended Defects ◽  
Vibrational Entropy ◽  
Phonon Modes ◽  
Antiphase Boundaries ◽  
Related Energy ◽  
Quantum Mechanical Study ◽  
Nonlinear Trends ◽  
The Impact

We performed a quantum mechanical study of segregation of Cu atoms toward antiphase boundaries (APBs) in Fe3Al. The computed concentration of Cu atoms was 3.125 at %. The APBs have been characterized by a shift of the lattice along the ⟨001⟩ crystallographic direction. The APB energy turns out to be lower for Cu atoms located directly at the APB interfaces and we found that it is equal to 84 mJ/m2. Both Cu atoms (as point defects) and APBs (as extended defects) have their specific impact on local magnetic moments of Fe atoms (mostly reduction of the magnitude). Their combined impact was found to be not just a simple sum of the effects of each of the defect types. The Cu atoms are predicted to segregate toward the studied APBs, but the related energy gain is very small and amounts to only 4 meV per Cu atom. We have also performed phonon calculations and found all studied states with different atomic configurations mechanically stable without any soft phonon modes. The band gap in phonon frequencies of Fe3Al is barely affected by Cu substituents but reduced by APBs. The phonon contributions to segregation-related energy changes are significant, ranging from a decrease by 16% at T = 0 K to an increase by 17% at T = 400 K (changes with respect to the segregation-related energy difference between static lattices). Importantly, we have also examined the differences in the phonon entropy and phonon energy induced by the Cu segregation and showed their strongly nonlinear trends.

VECTOR AUTOREGRESSIVE PROCESSES WITH NONLINEAR TIME TRENDS IN COINTEGRATING RELATIONS

2001 ◽  
Vol 5 (4) ◽  
pp. 577-597 ◽  
Author(s):  
Antti Ripatti ◽  
Pentti
Keyword(s):  
Structural Changes ◽  
Asymptotic Theory ◽  
Var Model ◽  
Level Shift ◽  
Autoregressive Processes ◽  
Short Term ◽  
Vector Autoregressive ◽  
Market Rate ◽  
Nonlinear Trends ◽  
Vector Autoregressive Processes

We extend the conventional cointegrated VAR model to allow for general nonlinear deterministic trends. These nonlinear trends can be used to model gradual structural changes in the intercept term of the cointegrating relations. A general asymptotic theory of estimation and statistical inference is reviewed and a diagnostic test for the correct specification of an employed nonlinear trend is developed. The methods are applied to Finnish interest-rate data. A smooth level shift of the logistic form between the own-yield of broad money and the short-term money market rate is found appropriate for these data. The level shift is motivated by the deregulation of issuing certificates of deposit and its inclusion in the model solves the puzzle of the “missing cointegration vector” found in a previous study.

Multiscaled Neural Autoregressive Distributed Lag: A New Empirical Mode Decomposition Model for Nonlinear Time Series Forecasting

2020 ◽  
Vol 30 (08) ◽  
pp. 2050039 ◽  
Author(s):  
Foued Saâdaoui ◽  
Othman Ben Messaoud
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Empirical Mode Decomposition ◽  
Multivariate Time Series ◽  
Seasonal Effects ◽  
Real World Data ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag ◽  
Mode Decomposition ◽  
Nonlinear Trends

Forecasting has always been the cornerstone of machine learning and statistics. Despite the great evolution of the time series theory, forecasters are still in the hunt for better models to make more accurate decisions. The huge advances in neural networks over the last years has led to the emergence of a new generation of effective models replacing classic econometric models. It is in this direction that we propose, in this paper, a new multiscaled Feedforward Neural Network (FNN), with the aim of forecasting multivariate time series. This new model, called Empirical Mode Decomposition (EMD)-based Neural ARDL, is inspired from the well-known Autoregressive Distributed Lag (ARDL) model being our proposal founded upon the concepts of nonlinearity, EMD-multiresolution and neural networks. These features give the model the ability to effectively capture many nonlinear patterns like the ones often present in econophysical time series, such as nonlinear trends, seasonal effects, long-range dependency, etc. The proposed algorithm can be summarized into the following four basic tasks: (i) EMD breaking-down multivariate time series into different resolution levels, (ii) feeding EMD components from the same levels into a number of feedforward neural ARDL models, (iii) from one level to the next, extrapolating the component corresponding to the response variable (scalar output) a number of steps ahead, and finally, (iv) recombining level-by-level forecasts into a single output. An optimal learning scheme is rigorously designed for efficiently training the new proposed architecture. The approach is finally tested and compared to a number of powerful benchmark models, where experiments are conducted on real-world data.

scholarly journals Multi-scale analysis of teleconnection indices: climate noise and nonlinear trend analysis

2009 ◽  
Vol 16 (1) ◽  
pp. 65-76 ◽  
Author(s):  
C. Franzke
Keyword(s):  
Time Series ◽  
Decadal Variability ◽  
Statistical Significance ◽  
Stationary Time Series ◽  
Intrinsic Mode Functions ◽  
Multi Scale ◽  
The North ◽  
Teleconnection Indices ◽  
Nonlinear Trends ◽  
Climate Noise

Abstract. The multi-scale nature and climate noise properties of teleconnection indices are examined by using the Empirical Mode Decomposition (EMD) procedure. The EMD procedure allows for the analysis of non-stationary time series to extract physically meaningful intrinsic mode functions (IMF) and nonlinear trends. The climatologically relevant monthly mean teleconnection indices of the North Atlantic Oscillation (NAO), the North Pacific index (NP) and the Southern Annular Mode (SAM) are analyzed. The significance of IMFs and trends are tested against the null hypothesis of climate noise. The analysis of surrogate monthly mean time series from a red noise process shows that the EMD procedure is effectively a dyadic filter bank and the IMFs (except the first IMF) are nearly Gaussian distributed. The distribution of the variance contained in IMFs of an ensemble of AR(1) simulations is nearly χ2 distributed. To test the statistical significance of the IMFs of the teleconnection indices and their nonlinear trends we utilize an ensemble of corresponding monthly averaged AR(1) processes, which we refer to as climate noise. Our results indicate that most of the interannual and decadal variability of the analysed teleconnection indices cannot be distinguished from climate noise. The NP and SAM indices have significant nonlinear trends, while the NAO has no significant trend when tested against a climate noise hypothesis.

scholarly journals Macroeconomic effects on mortality revealed by panel analysis with nonlinear trends

2013 ◽  
Vol 7 (3) ◽  
pp. 1362-1385 ◽  
Author(s):  
Edward L. Ionides ◽  
Zhen Wang ◽  
José A. Tapia Granados
Keyword(s):  
Panel Analysis ◽  
Nonlinear Trends ◽  
Macroeconomic Effects

In the Founder's Shadow: Conflict in the Family Firm

1999 ◽  
Vol 12 (4) ◽  
pp. 311-323 ◽  
Author(s):  
Peter S. Davis ◽  
Paula D. Harveston
Keyword(s):  
Family Business ◽  
Family Firms ◽  
Telephone Survey ◽  
Business Owners ◽  
Next Generation ◽  
Independent Variables ◽  
The Family ◽  
Nonlinear Trends ◽  
Using Data ◽  
Two Independent Variables

This paper examines the extent to which conflict across generations of family firms is due to the effects of two independent variables—generation and generational shadow. The presence of a generational shadow was indicated by whether either or both of the parents continued to influence the company once the next generation assumed control. Hypotheses predicted nonlinear trends in conflict and interactions between generation and generational shadow. Using data from a national telephone survey of over 1,000 family business owners, the results of an ANOVA test confirmed that the presence of generational shadow, in particular, that of the founder, increases organizational conflict.

scholarly journals Interannual-to-multidecadal Hydroclimate Variability and its Sectoral Impacts in northeastern Argentina

2018 ◽  
Author(s):  
Miguel A. Lovino ◽  
Omar V. Müller ◽  
Gabriela V. Müller ◽  
Leandro C. Sgroi ◽  
Walter E. Baethgen
Keyword(s):  
Water Resources ◽  
Time Scales ◽  
Southern Oscillation ◽  
Singular Spectrum Analysis ◽  
Principal Component ◽  
Economic Sectors ◽  
Discharge Temperature ◽  
Hydroclimate Variability ◽  
Nonlinear Trends ◽  
Northeastern Argentina

Abstract. This study examines the relation between hydroclimate variability (precipitation, river discharge, temperature) and water resources, agriculture and human settlements at different time scales in northeastern Argentina. It also discusses the impacts on these productive and socio-economic sectors. The leading patterns of variability, their nonlinear trends, and cycles are identified by means of a Principal Component Analysis (PCA) complemented with a Singular Spectrum Analysis (SSA). Interannual hydroclimatic variability centres on two broad frequency bands: one of 2.5–6.5 years corresponding to El Niño Southern Oscillation (ENSO) periodicities and the second of about 9 years. Interdecadal variability is characterized by low-frequency trends and multidecadal oscillations that have induced a transition to wetter and warmer climate starting in the mid-twentieth century. The hydroclimate variability at all time scales had significant sectoral impacts. Frequent wet events between 1970 and 2005 favoured floods that affected agricultural and livestock productivity and forced population displacements. On the other hand, agricultural droughts produced soil moisture deficits affecting crops at critical growth periods. Hydrological droughts affected surface water resources causing water and food scarcity and stressed the capacity for hydropower generation. Lastly, increases in minimum temperature reduced wheat and barley yields.

scholarly journals Ultrasonic Speed, Density, Viscosity and Associated Acoustical Parameters of Chloroform Solutions of Symmetric Double Schiff Bases at 308.15K

2015 ◽  
Vol 45 ◽  
pp. 16-23
Author(s):  
B.J. Gangani ◽  
Parsotam H. Parsania
Keyword(s):  
Relaxation Time ◽  
Schiff Bases ◽  
Molecular Interactions ◽  
Path Length ◽  
Adiabatic Compressibility ◽  
Free Path Length ◽  
Solvation Number ◽  
Ultrasonic Speed ◽  
Acoustical Parameters ◽  
Nonlinear Trends

The density, viscosity and ultrasonic speed (2MHz) of chloroform and symmetric double Schiff bases have been investigated at 308.15K. Various acoustical parameters such as specific acoustical impedance (Z), adiabatic compressibility (Кa), Rao’s molarsound function (Rm), Vander Waals constant (b), internal pressure (π), free volume (Vf), intermolecular free path length (Lf), classical absorption coefficient (α/f2)Cl) and viscous relaxation time (τ) were determined using ultrasonic speed (U), viscosity (η) and density (ρ) data of Schiff bases solutions and correlated with concentration. Increasing linear or nonlinear trends of (Z, Rm, b, τ and (α/f2)Cl) and decreasing trend of Кa, Lf,, π and Vf with increasing concentration of Schiff bases suggested presence of strong molecular interactions in the solutions and solvophilic nature of the Schiff bases, which is further supported by the positive values of solvation number. The nature and position of substituent also affected the strength of molecular interactions.

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