Testing for the Constancy of Parameters over Time

By J. Nyblom. 1989

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Citations in the corpus

Generalized random forests (2016, methodology): “(2008); conceptually, it is closely related to standard techniques for moment-based change-point detection (Andrews, 1993; Hansen, 1992; Hjort and Koning, 2002; Nyblom, 1989; Ploberger and Krämer, 1992; Zeileis, 2005; Zeileis and Hornik, 2007).”
Tests of stationarity against a change in persistence (2004, background): “Nyblom (1989). The same type of behaviour is expected for our tests.”
ARE EXCHANGE RATES REALLY RANDOM WALKS? SOME EVIDENCE ROBUST TO PARAMETER INSTABILITY (2005, background): “…walk, then, implies testing whether β equals zero versus the alternative that the parameters are different from zero (recall that x1,t is the rate of growth of the exchange rate, so that if β = 0 then the rate of growth is unpredictable and, thus, the exchange rate in levels is a random walk).”
China's Emergence in the World Economy and Business Cycles in Latin America (2011, methodology): “To conduct the empirical analysis we use a variant of the global vector autoregressive (GVAR) model originally proposed by Pesaran, Schuermann, and Weiner (2004) and further developed by Dees, di Mauro, Pesaran, and Smith (2007). This is a relatively novel approach to global macroeconomic modelling ”
SEQUENTIAL CHANGE-POINT DETECTION IN GARCH(p,q) MODELS (2004, methodology): “By considering the quasi-likelihood scores, our approach is related to that of Nyblom (1989), but it is different in that our focus is on controlling the probability of false alarms, whereas Nyblom (1989) concentrates on constructing locally most powerful tests against alternatives that assume that ”
Modelling structural breaks, long memory and stock market volatility: an overview (2005, methodology): “They show that both a fluctuation test and a Nyblom (1989)-type LM test for constancy of parameters can be used to test for the constancy of the parameters in the long-run relations.”
Structural changes in the cointegrated vector autoregressive model (2003, methodology): “See Seo (1998) who used the framework of Andrews and Ploberger (1994), or B. E. Hansen (1992) and H. Hansen and Johansen (1999) who applied the framework of Nyblom (1989) .”
Testing for Stochastic Trends in Series with Structural Breaks (2000, methodology): “For ' percentage points are tabulated in Anderson and Darling (1952), MacNeill (1978), Nyblom and Mäkeläinen (1983), Nyblom (1986), Nabeya and Tanaka (1988), Kwiatkowski HW DO (1992) when : percentage points are tabulated in Nyblom (1989), Canova and Hansen (1995) and Nyblom and Harvey (2000).”
Confidence Sets for the Date of a Single Break in Linear Time Series Regressions (2004, methodology): “Table 1 depicts the asymptotic coverage rates of nominal 95% confidence intervals (2) for k = 1, Ω = ΣX and various values of Ω−1/2d and r0, along with the asymptotic local power of a 5%-level Nyblom (1989) test for breaks.”
Forecasting and Estimating Multiple Change-Point Models with an Unknown Number of Change Points (2004, unknown): “other hand, Stock and Watson (2002) present evidence from a stochastic volatility model that the decline in variance might have been more gradual, a thesis first forward by Blanchard and Simon (2001).”
The Disappointing Recovery of Output after 2009 (2017, unknown): “the Nyblom (1989) tests that focuses power on small martingale variation in t, and the LFST”
The Causal Relationship Between Economic Policy Uncertainty and Stock Returns in China and India: Evidence from a Bootstrap Rolling Window Approach (2016, methodology): “…into consideration, we use the Sup-F, Mean-F and Exp-F tests developed by Andrews (1993) and Andrews and Ploberger (1994) and Lc test proposed by Nyblom (1989) and Hansen (1992) to assess parameter stability of short- and long-run relationships between EPU and stock returns in China and India.”
Tracking the Slowdown in Long-Run GDP Growth (2017, methodology): “To demonstrate this, a sequential application of the Nyblom (1989) and Bai and Perron (1998) tests using real-time data is presented in figure 1.”