Cointegration versus least squares regression

By N. Kulendran, Stephen F. Witt. 2001

249 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research, Tourism, Leisure and Hospitality Management

View Paper: https://www.semanticscholar.org/paper/85c2e6cdc8f556b1abd47c3f64bc53fc4e4cc00b

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Citations in the corpus

A theoretical approach to tourism sustainability (2002, methodology): “These econometric models have been employed with different techniques, either cointegration or least squares (Kulendran and Witt 2001), on data referring to various target places, ranging from the northeast of England (Seddighi and Shearing 1997) to Australia (Morley 1998) and from Barbados…”
A meta-analysis of international tourism demand forecasting and implications for practice (2014, background): “Kulendran and Witt (2001) also demonstrate that the ECM and CI models are more accurate than traditional econometric models in forecasting tourism demand from the UK to Germany, Greece, the Netherlands, Portugal, Spain, and the US.”
Modeling tourism: A fully identified VECM approach (2009, methodology): “When system approaches are used (for example Dritsakis, 2004; Gangnes & Bonham, 1998; Kulendran & King, 1997; Kulendran & Witt, 2001, 2003; Lim & McAleer, 2001; Song & Witt, 2003), identification is obtained exclusively using Johansen’s reduced rank regression technique, and identified…”
Tourism forecasting: accuracy of alternative econometric models (2003, methodology): “However, the application of the TVP data, whereas Kulendran and King (1997) and method to tourism has been rare (but see Riddington, Kulendran and Witt (2001) used quarterly data.”
Forecasting tourist arrivals using time-varying parameter structural time series models (2011, background): “…have been applied in the tourism demand forecasting context, and several studies have demonstrated their superior forecasting performance relative to other time series alternatives (see for example González & Moral, 1995, 1996; Kulendran & King, 1997; Kulendran & Witt, 2001; Turner & Witt, 2001).”
Determinants of Inbound Tourism to South Africa (2008, background): “Researchers such as Kulendran and Witt (2001), Lim and McAleer (2002) and Dritsakis (2004) also include long-run properties of the demand equation in their models via cointegration analysis and error correction models.”
Tourism demand for Italy and the business cycle (2010, background): “…comparisons are usually limited to complex models, it has been shown that naı̈ve models usually produce better forecasts for longer time horizons (see Kulendran & Witt, 2001, 2003) and need to be taken into account for a correct assessment of the trade-off between complexity and predictive power.”
Inbound international tourism to Canary Islands: a dynamic panel data model (2006, result): “However, several studies (Chan, Hui, & Yuen, (1999); Du Preez & Witt, 2003; Kulendran & Witt, 2001; Turner, Kulendran, & Pergat, 1995; Witt, Song, & Louvieris, 2003) compared the forecasting ability of the BoxJenkins approach to other modelling techniques and found that they generally outperform…”
Tourism demand modelling and forecasting—A review of recent research (2008, methodology): “For example, ECM was used in the studies of Kulendran, and Wilson (2000), Kulendran and Witt (2003b), Lim and McAleer (2001), Song and Witt (2000). The VAR model can be found in Shan and Wilson (2004), Song and Witt (2006), Witt et al (2004).”
Forecasting the Demand for International Business Tourism (2003, result): “González and Moral (1996) and Kulendran and Witt (2001) showed that the BSM outperformed the ARIMA1 model, but this contradicts the result obtained by Kulendran and King (1997).”
Tourism Forecasting: The General-to-Specific Approach (2003, unknown): “…of the published studies on tourism forecasting have been based on the first seven model specifications, the error correction model (ECM) has recently appeared in the tourism forecasting literature (Kim and Song 1998; Kulendran and King 1997; Kulendran and Witt 2001; Song, Romilly, and Liu 2000).”
Forecasting Tourism Using Univariate and Multivariate Structural Time Series Models (2001, result): “The empirical findings are consistent with previous studies indicating that univariate models provide the most accurate forecasts of tourism demand.(14) They also support previous studies that indicate that origin country income, transport costs and the degree of trade openness influence tourism dem”
Forecasting international tourist flows to Macau (2006, background): “Among these alternatives, real personal disposable income (PDI) is the best proxy to be included in the demand models that relate to holiday or visiting friends and relatives (VFR) travel (Kulendran and Witt, 2001; Song et al, 2000; Syriopoulos, 1995).”
Modeling Seasonality in Tourism Forecasting (2005, methodology): “Within the multiplicative seasonal ARIMA modeling context, to model stochastic nonstationary seasonal variation and deterministic seasonal variation, two variants of seasonal ARIMA models were considered (Kulendran and King 1997; Kulendran and Witt 2001).”