Determining the number of breaks in a piecewise linear regression model

In this paper we propose a sequential method for determining the number of breaks in piecewise linear structural break models. An advantage of the method is that it is based on standard statistical inference. Tests available for testing linearity against switching regression type nonlinearity are applied sequentially to determine the number of regimes in the structural break model. A simulation study is performed in order to investigate the finite-sample behaviour of the procedure and to compare it with other alternatives. We find that our method works well in comparison for both single and multiple break cases.Model specification; multiple structural breaks.

Determining the Number of Regimes in a Threshold Autoregressive Model Using Smooth Transition Autoregressions

In this paper we propose a method for determining the number of regimes in threshold autoregressive models using smooth transition autoregression as a tool. As the smooth transition model is just an approximation to the threshold autoregressive one, no asymptotic properties are claimed for the proposed method. Tests available for testing the adequacy of a smooth transition autoregressive model are applied sequentially to determine the number of regimes. A simulation study is performed in order to find out the finite-sample properties of the procedure and to compare it with two other procedures available in the literature. We find that our method works reasonably well for both single and multiple threshold models.Model specification; model selection criterion; nonlinear modelling; sequential testing; switching regression

Modelling time-varying seasonality in quarterly industrial production series

https://www.ester.ee/record=b4137225*es

Testing the Granger Noncausality Hypothesis in Stationary Nonlinear Models of Unknown Functional Form

ADInternational audienceIn this article, we propose a general method for testing the Granger noncausality hypothesis in stationary nonlinear models of unknown functional form. These tests are based on a Taylor expansion of the nonlinear model around a given point in the sample space. We study the performance of our tests by a Monte Carlo experiment and compare these to the most widely used linear test. Our tests appear to be well-sized and have reasonably good power properties

Testing the Granger noncausality hypothesis in stationary nonlinear models of unknown functional form

In this paper we propose a general method for testing the Granger noncausality hypothesis in stationary nonlinear models of unknown functional form. These tests are based on a Taylor expansion of the nonlinear model around a given point in the sample space. We study the performance of our tests by a Monte Carlo experiment and compare these to the most widely used linear test. Our tests appear to be well-sized and have reasonably good power properties.Hypothesis testing, causality

The effects of institutional and technological change and business cycle fluctuations on seasonal patterns in quarterly industrial production series

Changes in the seasonal patterns of macroeconomic time series may be due to the effects of business cycle fluctuations or to technological and institutional change or both. We examine the relative importance of these two sources of change in seasonality for quarterly industrial production series of the G7 countries. We find compelling evidence that the effects of gradual institutional and technological change are much more important than the effects attributable to the business cycle.Nonlinear time series; seasonality; smooth transition autoregression; structural change; time-varying parameter

A sequential procedure for determining the number of regimes in a threshold autoregressive model

In this paper, we propose a sequential method for determining the number of regimes in threshold autoregressive models. The proposed method relies on the superconsistency of sequential threshold estimates and uses general linearity tests to determine the number of thresholds. A simulation study is performed in order to find out the finite-sample properties of our procedure and to compare it with two other procedures available in the literature. We find that our method works reasonably well for both single and multiple threshold models. Copyright Royal Economic Society 2006

The effects of institutional and technological change and business cycle fluctuations on seasonal patterns in quarterly industrial production series

Changes in the seasonal patterns of macroeconomic time series may be due to the effects of business cycle fluctuations or to technological and institutional change or both. We examine the relative importance of these two sources of change in seasonality for quarterly industrial production series of the G7 countries using time-varying smooth transition autoregressive models. We find compelling evidence that the effects of gradual institutional and technological change are much more important than the effects attributable to the business cycle. Copyright Royal Economic Society, 2003