Testing and Modeling Threshold Autoregressive Processes

Tsay, Ruey S.

doi:10.2307/2289868

Cited by 330 publications

(303 citation statements)

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“…The parameters are estimated by conditional least squares (Tsay, 1989) Using each of the simulated sequences {y t+h,i } i=T i=1 and {x…”

Section: A Testing Predictive Ability With Midasmentioning

confidence: 99%

Changes in predictive ability with mixed frequency data

Galvão

2013

International Journal of Forecasting

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This paper proposes a new regression model-a smooth transition mixed data sampling (STMIDAS) approach-that captures recurrent changes in the ability of a high frequency variable in predicting a low frequency variable. The STMIDAS regression is employed for testing changes in the ability of financial variables in forecasting US output growth. The estimation of the optimal weights for aggregating weekly data inside the quarter improves the measurement of the predictive ability of the yield curve slope for output growth. Allowing for changes in the impact of the short-rate and the stock returns in future growth is decisive for finding in-sample and out-of-sample evidence of their predictive ability at horizons longer than one year.

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“…The parameters are estimated by conditional least squares (Tsay, 1989) Using each of the simulated sequences {y t+h,i } i=T i=1 and {x…”

Section: A Testing Predictive Ability With Midasmentioning

confidence: 99%

Changes in predictive ability with mixed frequency data

Galvão

2013

International Journal of Forecasting

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“…3 Before estimation, a nonlinearity test has to be implemented to determine whether threshold behavior is rejected or not. Four nonlinear tests including the F test proposed by Tsay (1986), the augmented F test proposed by Luukkonen et al (1988), the threshold test proposed by Tsay (1989), and a general nonlinearity test, also proposed by Tsay (1991), are therefore carried out here. Adhering to the nomenclature by Cao and Tsay (1992), these four test statistics are referred to as ORG-F, AUG-F, TAR-F and GEN-F, respectively.…”

Section: A Threshold Modelmentioning

confidence: 99%

“…The estimation of equation (1) requires knowledge of both the inflation threshold variable, ct , and the threshold value, r. The two methods which have been proposed to estimate threshold variables and values include the Tong (1983) grid search method and the Tsay (1989) arranged autoregression method. This study has employed the Tong (1983) method to search for the threshold variables and values, as the method is more widely used than the latter.…”

Section: A Threshold Modelmentioning

confidence: 99%

Retrieving the vanishing liquidity effect—a threshold vector autoregressive model

Shen

Chiang

1999

Journal of Economics and Business

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This paper employs a threshold vector autoregressive (TVAR) model where the data is subdivided into low and high inflation regimes. Monetary policy is endogenized in this framework and two different measures of monetary policy, viz. NBR and M1, are investigated. The interest rate is hypothesized to respond inversely to increased monetary growth in the low inflation regime and positively to increased monetary growth in the high inflation regime. In the low inflation regime, expansionary monetary policy shocks are found to depress the interest rate over 10 and 5 periods for nonborrowed reserves and M1 growth, respectively. Whereas, in the high inflation regime, both measures generate positive responses. It follows that the hypothesized threshold behavior between money and the interest rate is supported regardless of monetary measures.

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“…For example, Godby et al (2000) used the bootstrap Threshold Autoregressive (TAR) procedure of Hansen (1996) and cannot find any significant impact of asymmetry in the Canadian gasoline market. More recently, Douglas (2010) used a univariate TAR model by Tsay (1989) to test the asymmetry hypothesis using the same dataset as the one used in this paper. He similarly finds that prices do not display as much asymmetry as believed and therefore that there is "minimal consumer welfare loss from asymmetry.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Rising household diesel consumption in the United States: A cause for concern? Evidence on asymmetric pricing

Fosten¹

2012

Energy Economics

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Papers in the literature have thus far overlooked the projected increase in U.S. diesel car share when looking at asymmetries in petroleum pricing. This paper addresses this issue by comparing retail gasoline and diesel prices in order to see whether they rise faster than they fall given the price of their upstream input, crude oil. This phenomenon has been termed in the literature as "Rockets and Feathers." We apply the threshold vector error correction model (TVECM) of Hansen and Seo (2002) which has not yet been applied in the literature. We account for the 2008 structural break to crude oil and petroleum prices by splitting the sample using evidence from the recent structural break unit root test of Kim and Perron (2009). Both markets seem to price symmetrically before the 2008 break, but we find evidence of asymmetric pricing after 2008 in diesel prices, and not in gasoline prices. Given that the diesel market is small relative to the gasoline market and therefore more open to price exploitation, the ongoing cost increases associated with the policy of switching to Ultra Low Sulphur diesel (ULSD) from 2006 to 2010 could be at the heart of this asymmetry. With this in mind, the U.S. Federal Trade Commission should monitor diesel prices as the market share grows, in order to ensure that consumers are not adversely affected.

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Testing and Modeling Threshold Autoregressive Processes

Cited by 330 publications

References 0 publications

Changes in predictive ability with mixed frequency data

Changes in predictive ability with mixed frequency data

Retrieving the vanishing liquidity effect—a threshold vector autoregressive model

Rising household diesel consumption in the United States: A cause for concern? Evidence on asymmetric pricing

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