TY - BOOK ID - 84543295 TI - Testing for Structural Breaks in Small Samples AU - Antoshin, Sergei. AU - Berg, Andrew. AU - Souto, Marcos. PY - 2008 SN - 1462351778 145279247X 1282391909 9786613820334 1451913907 PB - Washington, D.C. : International Monetary Fund, DB - UniCat KW - Econometrics. KW - Time-series analysis. KW - Monte Carlo method. KW - Artificial sampling KW - Model sampling KW - Monte Carlo simulation KW - Monte Carlo simulation method KW - Stochastic sampling KW - Games of chance (Mathematics) KW - Mathematical models KW - Numerical analysis KW - Numerical calculations KW - Stochastic processes KW - Analysis of time series KW - Autocorrelation (Statistics) KW - Harmonic analysis KW - Mathematical statistics KW - Probabilities KW - Economics, Mathematical KW - Statistics KW - Data Processing KW - Data Collection and Data Estimation Methodology KW - Computer Programs: General KW - Data capture & analysis KW - Data processing KW - Electronic data processing UR - https://www.unicat.be/uniCat?func=search&query=sysid:84543295 AB - 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. ER -