Efficient Tests for General Persistent Time Variation in Regression Coefficients

By Graham Elliott, Ulrich K. Müller. 2006

210 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/68b34dc3a8d84214e750812e622984f399df44b6

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Citations in the corpus

Exchange Rate Pass-Through in the Global Economy: The Role of Emerging Market Economies (2014, unknown): “This hypothesis was put forward by Taylor (2000), and tested by Gagnon and Ihrig (2004) for consumer prices in advanced economies, and by Frankel et al.”
Forecasting the Equity Risk Premium: The Role of Technical Indicators (2014, unknown): “9 We tested for structural breaks in all of the predictive regression models using the Elliott and Müller (2006) ̂qLL statistic, which is asymptotically efficient for a broad range of persistent breaking processes and has good size and power properties in the presence of heteroskedasticity.”
Detecting and Predicting Forecast Breakdowns (2009, methodology): “One can further see that a forecast breakdown is caused by breaks in parameters of greater magnitude than those considered by previous structural break tests such as, e.g., Elliott and Muller (2006) (i.e., here the breaks are of magnitude 1 4 rather than 1 2 ).”
Advances in Forecasting Under Instability (2012, unknown): “In fact, as shown by Elliott and Muller (2006), conditional on the average magnitude of breaks being the same, the power of several, widely used tests for structural breaks is close over a wide range of breaking processes; thus, ignorance of which particular type of instability a¤ects the data in…”
Non‐parametric detection and estimation of structural change (2012, unknown): “On the other hand, within the class of breaking processes of Elliott and Müller (2006), our test will in general be less powerful compared to parametric tests.”
Confidence Sets for the Date of a Single Break in Linear Time Series Regressions (2004, background): “As demonstrated in Elliott and Müller (2006), however, the power of tests for structural stability does not greatly depend on the specific choice of weights, at least as long as they do not concentrate too heavily on specific values for t0 and d.”
Estimation and forecasting in models with multiple breaks (2007, background): “If the number of break points is even moderately large, then computational costs can become overwhelming [see, for instance, the discussion in Elliott and Muller (2006)].”
Short Interest and Aggregate Stock Returns (2016, methodology): “Using the Elliott and Müller (2006) dqLL statistic to test H0: bt = b for all t, none of the dqLL statistics is significant at any horizon for the b̂ estimates for SII in Table 3.”
Tests for changing mean with monotonic power (2009, methodology): “For other tests used in econometrics, see, e.g., Andrews (1993), Andrews and Ploberger (1994), Perron (1991), Hansen (1992), and Elliott and Müller (2006).”
Credit Risk and the Macroeconomy: Evidence from an Estimated DSGE Model (2009, result): “All told, these results are consistent with those reported by GYZ for the 1990-2008 period: The information content of corporate credit spreads for macroeconomic outcomes (17)Although a full formal investigation of potential parameter instability is beyond the scope of this paper, we tested for time”