Estimating Structural Changes in the Vertical Price Relationships in U.S. Beef and Pork Markets

This paper examines structural breaks in the vertical price relationships in U.S. beef/cattle and pork/hog sectors using monthly data of the past 40 years. A major methodological issue addressed is how to estimate price relationships when data contain intermittent structural breaks with unknown break dates. The adopted procedures endogenously search for structural break dates while explicitly accounting for this search in statistical inferences. Four breaks for the beef/cattle price relationship and three breaks for the pork/hog price relationship are identified. The estimation results further confirm the importance of allowing for structural breaks in the analysis of vertical price relationships.farm cattle and hog prices, long-run price relationship, retail beef and pork prices, structural breaks of unknown timing, structural changes, vertical price relationship, Industrial Organization, Livestock Production/Industries,

Bayesian Model Averaging and Identification of Structural Breaks in Time Series

Bayesian model averaging is used for testing for multiple break points in uni- variate series using conjugate normal-gamma priors. This approach can test for the number of structural breaks and produce posterior probabilities for a break at each point in time. Results are averaged over speciÖcations including: station- ary; stationary around trend; and, unit root models, each containing di§ erent types and numbers of breaks and di§ erent lag lengths. The procedures are used to test for structural breaks on 14 annual macroeconomic series and 11 natural resource price series. The results indicate that there are structural breaks in al l of the natural resource series and most of the macroeconomic series. Many of the series had multiple breaks. Our Öndings regarding the existence of unit roots, having al lowed for structural breaks in the data, are largely consistent with previous work.Bayesian Model Averaging, Structural Breaks, Unit Root, Macro- economic Data, Natural Resource data

Structural Breaks in the Cointegrated Vector Autoregressive Model

We generalize the cointegrated vector autoregressive model of Johansen (1988, 1991) to allow for structural breaks. We derive the likelihood ratio test for structural breaks occurring at fixed points in time, and show that it is asymptotically chi-squared. Moreover, we show how inference can be made when the null hypothesis is presence of structural breaks. The estimation technique derived for this purpose can be applied to several other generalizations of the standard model, beyond the structural breaks treated here. For example, the new technique can be applied to estimate models with heteroskedasticity. We apply our generalized model to US term structure data, accounting for structural breaks that coincide with the changes in the Fed's policy in September 1979 and October 1982. Contrary to previous findings we cannot reject the long-run implications of the expectations hypothesis.

The KPSS Test with Two Structural Breaks

In this paper we generalize the KPSS-type test to allow for two structural breaks. Seven models have been de?ned depending on the way that the structural breaks a¤ect the time series behaviour. The paper derives the limit distribution of the test both under the null and the alternative hypotheses and conducts a set of simulation experiments to analyse the performance in finite samples.Stationary tests, structural breaks, unit root.

Mind the Break! Accounting for Changing Patterns of Growth during Transition

We argue that econometric analyses based on transition countries’ data can be vulnerable to structural breaks across time and/or countries. We demonstrate this argument by identifying structural breaks in growth regressions estimated with data for 25 countries and 12 years. Our method allows identification of structural breaks at a-priori unknown points in space or time. The only prior assumption is that breaks occur in relation to progress in implementing market-oriented reforms. We find robust evidence that the pattern of growth in transition has changed at least two times, yielding thus three different models of growth associated with different stages of reform. The speed with which individual countries progress through these stages differs dramatically, however.Growth, reform, structural breaks, transition

Mind the Break! Accounting for Changing Patterns of Growth during Transition

We argue that econometric analyses based on transition countries’ data can be vulnerable to structural breaks across time and/or countries. We demonstrate this argument by identifying structural breaks in growth regressions estimated with data for 25 countries and 12 years. Our method allows identification of structural breaks at a-priori unknown points in space or time. The only prior assumption is that breaks occur in relation to progress in implementing market-oriented reforms. We find robust evidence that the pattern of growth in transition has changed at least two times, yielding thus three different models of growth associated with different stages of reform. The speed with which individual countries progress through these stages differs dramatically, however.http://deepblue.lib.umich.edu/bitstream/2027.42/40029/3/wp643.pd

Cointegration Analysis in the Presence of Structural Breaks in the Deterministic Trend

When analysing macro economic data it is often of relevance to allow for structural breaks in the statistical analysis. In particular cointegration analysis in the presence of structural breaks could be of interest. To do this a vector autoregressive model is proposed with known break points in the structural breaks. Within this model it is possible to test cointegration rank, restrictions on the cointegrating vector as well as restrictions on for instance the slopes of broken linear trend.

Testing for Structural Breaks in Nonlinear Dynamic Models Using Artificial Neural Network Approximations

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.Nonlinearity, Structural breaks, Neural networks

Testing for structural breaks in dynamic factor models

From time to time, economies undergo far-reaching structural changes. In this paper we investigate the consequences of structural breaks in the factor loadings for the specification and estimation of factor models based on principal components and suggest test procedures for structural breaks. It is shown that structural breaks severely inflate the number of factors identified by the usual information criteria. Based on the strict factor model the hypothesis of a structural break is tested by using Likelihood-Ratio, Lagrange-Multiplier and Wald statistics. The LM test which is shown to perform best in our Monte Carlo simulations, is generalized to factor models where the common factors and idiosyncratic components are serially correlated. We also apply the suggested test procedure to a US dataset used in Stock and Watson (2005) and a euro-area dataset described in Altissimo et al. (2007). We find evidence that the beginning of the so-called Great Moderation in the US as well as the Maastricht treaty and the handover of monetary policy from the European national central banks to the ECB coincide with structural breaks in the factor loadings. Ignoring these breaks may yield misleading results if the empirical analysis focuses on the interpretation of common factors or on the transmission of common shocks to the variables of interest. --Dynamic factor models,structural breaks,number of factors,Great Moderation,EMU

How Costly is it to Ignore Breaks when Forecasting the Direction of a Time Series?

Empirical evidence suggests that many macroeconomic and financial time series are subject to occasional structural breaks. In this paper we present analytical results quantifying the effects of such breaks on the correlation between the forecast and the realization and on the ability to forecast the sign or direction of a time-series that is subject to breaks. Our results suggest that it can be very costly to ignore breaks. Forecasting approaches that condition on the most recent break are likely to perform better over unconditional approaches that use expanding or rolling estimation windows provided that the break is reasonably large.sign prediction, estimation window, structural breaks