Forecasting Brazilian output and its turning points in the presence of breaks: a comparison of linear and nonlinear models

This paper compares the forecasting performance of linear and nonlinear models under the presence of structural breaks for the Brazilian real GDP growth. The Markov switching models proposed by Hamilton (1989) and its generalized version by Lam (1990) are applied to quarterly GDP from 1975:1 to 2000:2 allowing for breaks at the Collor Plans. The probabilities of recessions are used to analyze the Brazilian business cycle. The in-sample and out-of-sample forecasting ability of growth rates of GDP of each model is compared with linear specifications and with a non-parametric rule. We find that the nonlinear models display a better forecasting performance than linear models. The specifications with the presence of structural breaks are important in obtaining a representation of the Brazilian business cycle and their inclusion improves considerably the models forecasting performance within and out-of-sample.

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A Comparison of Linear and Nonlinear Models in Forecasting Market Risk: The Evidence from Turkish Derivative Exchange

Journal of Economics and Behavioral Studies ◽

10.22610/jebs.v5i3.391 ◽

2013 ◽

Vol 5 (3) ◽

pp. 164-172

Author(s):

Yasemin Deniz AkarÄ±m

Keyword(s):

Risk Management ◽

Asset Pricing ◽

Nonlinear Models ◽

Investment Decisions ◽

Portfolio Allocation ◽

Garch Models ◽

Ann Model ◽

Volatility Forecasting ◽

Forecasting Performance ◽

Linear And Nonlinear

This paper aims to compare the volatility forecasting performance of linear and nonlinear models for ISE-30 future index which is traded in Turkish Derivatives Exchangefor the period between 04.02.2005-17.06.2011. As a result of analyses, we conclude that ANN model has better forecasting performance than traditional ARCH-GARCH models. This result is important in many fields of finance such as investment decisions, asset pricing, portfolio allocation and risk management

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The Out-of-Sample Forecasting Performance of Non-Linear Models of Regional Housing Prices in the US

SSRN Electronic Journal ◽

10.2139/ssrn.2138980 ◽

2012 ◽

Cited By ~ 1

Author(s):

Mehmet Balcilar ◽

Rangan Gupta ◽

Stephen M. Miller

Keyword(s):

Linear Models ◽

Housing Prices ◽

Forecasting Performance ◽

The Us ◽

Non Linear ◽

Out Of Sample

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Information criteria for nonlinear time series models

Studies in Nonlinear Dynamics & Econometrics ◽

10.1515/snde-2015-0026 ◽

2016 ◽

Vol 20 (3) ◽

Author(s):

Saskia Rinke ◽

Philipp Sibbertsen

Keyword(s):

Error Term ◽

Linear Models ◽

Nonlinear Models ◽

General Information ◽

Information Criteria ◽

Order Selection ◽

Simulation Studies ◽

Variance Estimator ◽

Linear And Nonlinear ◽

The Impact

AbstractIn this paper the performance of different information criteria for simultaneous model class and lag order selection is evaluated using simulation studies. We focus on the ability of the criteria to distinguish linear and nonlinear models. In the simulation studies, we consider three different versions of the commonly known criteria AIC, SIC and AICc. In addition, we also assess the performance of WIC and evaluate the impact of the error term variance estimator. Our results confirm the findings of different authors that AIC and AICc favor nonlinear over linear models, whereas weighted versions of WIC and all versions of SIC are able to successfully distinguish linear and nonlinear models. However, the discrimination between different nonlinear model classes is more difficult. Nevertheless, the lag order selection is reliable. In general, information criteria involving the unbiased error term variance estimator overfit less and should be preferred to using the usual ML estimator of the error term variance.

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Dating US business cycles with macro factors

Studies in Nonlinear Dynamics & Econometrics ◽

10.1515/snde-2015-0037 ◽

2016 ◽

Vol 20 (5) ◽

Cited By ~ 4

Author(s):

Sebastian Fossati

Keyword(s):

Markov Switching ◽

Dynamic Factor ◽

Markov Switching Model ◽

Switching Model ◽

Markov Switching Models ◽

Us Economy ◽

Switching Models ◽

The Us ◽

Out Of Sample ◽

Macroeconomic Indicators

AbstractLatent factors estimated from panels of macroeconomic indicators are used to generate recession probabilities for the US economy. The focus is on current (rather than future) business conditions. Two macro factors are considered: (1) a dynamic factor estimated by maximum likelihood from a set of 4 monthly series; (2) the first of eight static factors estimated by principal components using a panel of 102 monthly series. Recession probabilities generated using standard probit, autoregressive probit, and Markov-switching models exhibit very different properties. Overall, a simple Markov-switching model based on the big data macro factor generates the sequence of out-of-sample class predictions that better approximates NBER recession months. Nevertheless, it is shown that the selection of the best performing model depends on the forecaster’s relative tolerance for false positives and false negatives.

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Beta Autoregressive Transition Markov-Switching Models for Business Cycle Analysis

Studies in Nonlinear Dynamics & Econometrics ◽

10.2202/1558-3708.1856 ◽

2011 ◽

Vol 15 (4) ◽

Cited By ~ 7

Author(s):

Monica Billio ◽

Roberto Casarin

Keyword(s):

Business Cycle ◽

Markov Switching ◽

Markov Switching Models ◽

Switching Models ◽

Business Cycle Analysis

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Labor market dynamics over the business cycle: evidence from Markov switching models

Empirical Economics ◽

10.1007/s00181-011-0486-8 ◽

2011 ◽

Vol 43 (1) ◽

pp. 271-289 ◽

Cited By ~ 6

Author(s):

Jeremy Schwartz

Keyword(s):

Labor Market ◽

Business Cycle ◽

Markov Switching ◽

Market Dynamics ◽

Markov Switching Models ◽

Switching Models ◽

Labor Market Dynamics ◽

The Business Cycle

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A Survey of Some Recent Results on Nonlinear Fault Tolerant Control

Mathematical Problems in Engineering ◽

10.1155/2010/586169 ◽

2010 ◽

Vol 2010 ◽

pp. 1-25 ◽

Cited By ~ 34

Author(s):

Mouhacine Benosman

Keyword(s):

Control Theory ◽

Linear Models ◽

Fault Tolerant ◽

Nonlinear Models ◽

Fault Tolerant Control ◽

Operating Life ◽

Linear And Nonlinear ◽

The One

Fault tolerant control (FTC) is the branch of control theory, dealing with the control of systems that become faulty during their operating life. Following the systems classification, as linear and nonlinear models, FTC can be classified in two different groups, linear FTC (LFTC) dealing with linear models, and the one of interest to us in this paper, nonlinear FTC (NFTC), which deals with nonlinear models. We present in this paper a survey of some of the results obtained in these last years on NFTC.

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QUANTILE-BASED ASYMMETRIC DYNAMICS OF REAL GDP GROWTH

Macroeconomic Dynamics ◽

10.1017/s1365100519000063 ◽

2019 ◽

Vol 24 (8) ◽

pp. 1960-1988

Author(s):

Xiaochun Liu

Keyword(s):

Conditional Distribution ◽

Structural Breaks ◽

Gdp Growth ◽

Developed Economies ◽

Real Gdp ◽

Economic Downturns ◽

Economic Recessions ◽

The Mean ◽

Linear And Nonlinear ◽

Different Parts

This paper studies asymmetric dynamics of real GDP growth by estimating linear and nonlinear quantile persistence over different parts of the conditional distribution for six major developed economies. Several novel quantile-based hypotheses are motivated in this paper and tested for the steepness asymmetry of real GDP growth that hypothesizes that contractions are steeper than expansions. The empirical results show that quantile persistence is generally high at far lower tails, thus requiring much longer half-lives to reverting negative deviations to the mean of real GDP growth and hence leading to gradual economic recoveries. By contrast, less persistence in far upper tails tends to generate sharp and short economic downturns that adjust positive deviations towards the mean of real GDP growth so as to cause abrupt economic recessions. In particular, this asymmetry in quantile persistence strongly supports the steepness asymmetry conjecture, robust to the presence of structural breaks and potential nonlinearities in real GDP growth.

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The out-of-sample forecasting performance of nonlinear models of real exchange rate behavior

International Journal of Forecasting ◽

10.1016/j.ijforecast.2005.09.006 ◽

2006 ◽

Vol 22 (2) ◽

pp. 341-361 ◽

Cited By ~ 57

Author(s):

David E. Rapach ◽

Mark E. Wohar

Keyword(s):

Exchange Rate ◽

Real Exchange Rate ◽

Nonlinear Models ◽

Forecasting Performance ◽

Out Of Sample ◽

Rate Behavior

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Nonlinearities In Stock Return Prediction: Evidence From South Africa

Journal of Applied Business Research (JABR) ◽

10.19030/jabr.v28i6.7341 ◽

2012 ◽

Vol 28 (6) ◽

pp. 1253 ◽

Cited By ~ 1

Author(s):

Kathleen Hodnett ◽

Heng-Hsing Hsieh ◽

Paul Van Rensburg

Keyword(s):

Cross Section ◽

Linear Models ◽

Nonlinear Models ◽

Learning Rule ◽

Equity Returns ◽

Neural Network Modeling ◽

Font Size ◽

Stock Selection ◽

Linear And Nonlinear ◽

The Cross

This research investigates the relationship between firm-specific style attributes and the cross-section of equity returns on the JSE Securities Exchange (JSE) over the period from 1 January 1997 to 31 December 2007. Both linear and nonlinear stock selection models are constructed based on the cross-section of equity returns with firm-specific attributes as model inputs. Both linear and nonlinear models identify book-value-to-price and cash flow-to-price as significant styles attributes that distinguish near-term future share returns on the JSE. The risk-adjusted performance of the nonlinear models is found to be comparable with that of linear models. In terms of artificial neural network modeling, the extended Kalman filter learning rule is found to outperform the traditional backpropagation approach. This finding is consistent with our prior findings on global stock selection.

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