Author: Alaa Abi Morshed
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Author: Yaein Baek
Publisher:
ISBN:
Category :
Languages : en
Pages : 176

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Book Description
Instability of parametric models is a common problem in many fields of economics. In econometrics, these changes in the underlying data generating process are referred to as structural breaks. Although there is an extensive literature on estimation and statistical tests of structural breaks, existing methods fail to adequately capture a break. This dissertation consists of three papers on developing econometric methods for structural breaks and forecasting. The first chapter develops a new method in estimating the location of a structural break in a linear model and provide theoretical results and empirical applications of the estimator. In finite sample the conventional least-squares estimates a break occurred at either ends of the sample with high probability, regardless of the true break point. I suggest an estimator of the break point that resolves this pile up issue and thus, provide a more accurate estimate of the break. The second chapter constructs a statistical test to test existence of a structural break when the direction of the parameter shift is known. In practice it is likely that a researcher is interested in testing for a structural break in a particular direction because the direction is known, such as policy change or historical data. We incorporate this information in constructing three tests that have higher power when direction is correctly specified. The last chapter proposes a multi-period forecasting method that is robust to model misspecification. When we are interested in obtaining long horizon ahead forecasts, the direct forecast method is more favorable than the iterated forecast because it is more robust to misspecification. However, direct forecast estimates tend to have jagged shapes across horizons. I use a mechanism analogous to ridge regression on the direct forecast model to maintain robustness while smoothing out erratic estimates.

Author: Eric Neumayer
Publisher: Cambridge University Press
ISBN: 1108415393
Category : Business & Economics
Languages : en
Pages : 269

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Book Description
This highly accessible book presents robustness testing as the methodology for conducting quantitative analyses in the presence of model uncertainty.

Author: Uwe Hassler
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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Book Description
In this paper, several seasonal unit root tests are analysed in the context of structural breaks at known time and a new break corrected test is suggested. We show that the widely used HEGY test, as well as an LM variant thereof, are asymptotically robust to seasonal mean shifts of finite magnitude. In finite samples, however, experiments reveal that such tests suffer from severe size distortions and power reductions when breaks are present. Hence, a new break corrected LM test is proposed to overcome this problem. Importantly, the correction for seasonal mean shifts bears no consequence on the limiting distributions, thereby maintaining the legitimacy of canonical critical values. Moreover, although this test assumes a breakpoint a priori, it is robust in terms of misspecification of the time of the break. This asymptotic property is well reproduced in finite samples. Based on a Monte-Carlo study, our new test is compared with other procedures suggested in the literature and shown to hold superior finite sample properties.

Author: Sergei Antoshin
Publisher: International Monetary Fund
ISBN:
Category : Computers
Languages : en
Pages : 34

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Book Description
In a recent paper, Bai and Perron (2006) demonstrate that their approach for testing for multiple structural breaks in time series works well in large samples, but they found substantial deviations in both the size and power of their tests in smaller samples. We propose modifying their methodology to deal with small samples by using Monte Carlo simulations to determine sample-specific critical values under the each time the test is run. We draw on the results of our simulations to offer practical suggestions on handling serial correlation, model misspecification, and the use of alternative test statistics for sequential testing. We show that, for most types of data generating processes in samples with as low as 50 observations, our proposed modifications perform substantially better.

Author: George Kapetanios
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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Book Description
In this paper we suggest a number of statistical tests based on neural network models, that are designed to be powerful against structural breaks in otherwise stationary time series processes while allowing for a variety of nonlinear specifications for the dynamic model underlying them. It is clear that in the presence of nonlinearity standard tests of structural breaks for linear models may not have the expected performance under the null hypothesis of no breaks because the model is misspecified. We therefore proceed by approximating the conditional expectation of the dependent variable through a neural network. Then, the residual from this approximation is tested using standard residual based structural break tests. We investigate the asymptoptic behaviour of residual based structural break tests in nonlinear regression models. Monte Carlo evidence suggests that the new tests are powerful against a variety of structural breaks while allowing for stationary nonlinearities.

Author: Paramsothy Silvapulle
Publisher:
ISBN:
Category : Monte Carlo method
Languages : en
Pages : 30

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Author: Walter Krämer
Publisher: Springer Science & Business Media
ISBN: 3642484123
Category : Business & Economics
Languages : en
Pages : 134

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Book Description
Econometric models are made up of assumptions which never exactly match reality. Among the most contested ones is the requirement that the coefficients of an econometric model remain stable over time. Recent years have therefore seen numerous attempts to test for it or to model possible structural change when it can no longer be ignored. This collection of papers from Empirical Economics mirrors part of this development. The point of departure of most studies in this volume is the standard linear regression model Yt = x;fJt + U (t = I, ... , 1), t where notation is obvious and where the index t emphasises the fact that structural change is mostly discussed and encountered in a time series context. It is much less of a problem for cross section data, although many tests apply there as well. The null hypothesis of most tests for structural change is that fJt = fJo for all t, i.e. that the same regression applies to all time periods in the sample and that the disturbances u are well behaved. The well known Chow test for instance assumes t that there is a single structural shift at a known point in time, i.e. that fJt = fJo (t

Author: Eric Neumayer
Publisher: Cambridge University Press
ISBN: 1108247547
Category : Political Science
Languages : en
Pages : 269

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Book Description
The uncertainty that researchers face in specifying their estimation model threatens the validity of their inferences. In regression analyses of observational data, the 'true model' remains unknown, and researchers face a choice between plausible alternative specifications. Robustness testing allows researchers to explore the stability of their main estimates to plausible variations in model specifications. This highly accessible book presents the logic of robustness testing, provides an operational definition of robustness that can be applied in all quantitative research, and introduces readers to diverse types of robustness tests. Focusing on each dimension of model uncertainty in separate chapters, the authors provide a systematic overview of existing tests and develop many new ones. Whether it be uncertainty about the population or sample, measurement, the set of explanatory variables and their functional form, causal or temporal heterogeneity, or effect dynamics or spatial dependence, this book provides guidance and offers tests that researchers from across the social sciences can employ in their own research.

Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Book Description
The unit root revolution in time series modeling has created substantial interest in non- stationarity and its implications for empirical modeling. Beyond the original interest in trend vs. difference non-stationarity, there has been renewed interest in testing and modeling structural breaks. The focus of my dissertation is on testing for departures from stationarity in a broader framework where unit root, mean trends and structural break non-stationarity constitute only a small subset of the possible forms of non-stationarity. In the first chapter the most popular testing procedures for the assumption, in view of the fact that general forms of non-stationarity render each observation unique, I develop a testing procedure using a resampling scheme which is based on a Maximum Entropy replication algorithm. The proposed misspecification testing procedure relies on resampling techniques to enhance the informational content of the observed data in an attempt to capture heterogeneity 'locally' using rolling window estimators of the primary moments of the stochastic process.