Co‐integration, error correction and improved medium‐term regional VAR forecasting

Shoesmith, Gary L.

doi:10.1002/for.3980110202

Cited by 45 publications

(29 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the studies by LeSage (1990) and Shoesmith (1992), the ECM was found to outperform the VAR model in forecast accuracy, particularly over long horizons. As the underlying equilibrium relationship in the EC model is expected to hold only in the long run, better performance should be more evident in the long run (see Stock, 1995).…”

Section: Forecast Performancementioning

confidence: 93%

Forecasting the Nikkei spot index with fractional cointegration

Lien

Tse

1999

J. Forecast.

View full text Add to dashboard Cite

We investigate the forecast performance of the fractionally integrated error correction model against several competing models for the prediction of the Nikkei stock average index. The competing models include the martingale model, the vector autoregressive model and the conventional error correction model. We consider models with and without conditional heteroscedasticity. For forecast horizons of over twenty days, the best forecasting performance is obtained for the model when fractional cointegration is combined with conditional heteroscedasticity. Our results reinforce the notion that cointegration and fractional cointegration are important for long-horizon prediction.

show abstract

Section: Forecast Performancementioning

confidence: 93%

Forecasting the Nikkei spot index with fractional cointegration

Lien

Tse

1999

J. Forecast.

View full text Add to dashboard Cite

show abstract

“…with f(i, j) = 1, if i = j and ij k otherwise, with (  01 Kinal and Ratner (1986) and Shoesmith (1992) …”

Section: The Prior Variancesmentioning

confidence: 99%

Do we need a global VAR model to forecast inflation and output in South Africa?

2015

View full text Add to dashboard Cite

This study determines whether the global vector autoregressive (GVAR) approach provides better forecasts of key South African variables than a vector error correction model (VECM) and a Bayesian vector autoregressive (BVAR) model augmented with foreign variables. The paper considers both a small GVAR model and a large GVAR model in determining the most appropriate model for forecasting South African variables. We compare the recursive out-ofsample forecasts for South African GDP and inflation from six types of models: a general 33-country (large) GVAR, a customised small GVAR for South Africa, a VECM for South Africa with weakly exogenous foreign variables, a BVAR model, autoregressive (AR) models and random walk models. The results show that the forecast performance of the large GVAR is generally superior to the performance of the customised small GVAR for South Africa. The forecasts of both the GVAR models tend to be better than the forecasts of the augmented VECM, especially at longer forecast horizons. Importantly however, on average, the BVAR model performs the best when it comes to forecasting output, while the AR(1) model outperforms all the other models in predicting inflation. We also conduct ex ante forecasts from the BVAR and AR(1) models over 2010:Q1-2013:Q4, to highlight their ability to track turning points in output and inflation respectively. JEL Classification Codes: C51, C53

show abstract

“…Given that, we have domestic as well as foreign and world variables within the 266 data series used for the large-scale models, and realizing that South Africa is a small open economy, and, hence, domestic variables would have minimal, if any, effect on foreign and world variables, while, the latter set of variables is sure to have an influence on the South African variables, setting ij k = 0.5 could be quite far fetched from reality. Hence, borrowing from the BVAR models used for regional forecasting, involving both regional and national variables, and following Kinal and Ratner (1986), Shoesmith (1992) and Gupta and Kabundi (2008b) …”

Section: The Var and Bvarsmentioning

confidence: 99%

Forecasting macroeconomic variables in a small open economy: a comparison between small‐ and large‐scale models

Gupta

Kabundi

2010

Journal of Forecasting

View full text Add to dashboard Cite

This paper compares the forecasting ability of five alternative models in predicting four key macroeconomic variables, namely, per capita growth rate, the Consumer Price Index (CPI) inflation, the money market rate, and the growth rate of the nominal effective exchange rate for the South African economy. Unlike the theoretical Small Open Economy New Keynesian Dynamic Stochastic General Equilibrium (SOENKDSGE), the unrestricted VAR, and the small-scale Bayesian Vector Autoregressive (BVAR) models, which are estimated based on four variables, the Dynamic Factor Model (DFM) and the large-scale BVAR models use information from a data-rich environment containing 266 macroeconomic time series observed over the period of 1983:01 to 2002:04. The results, based on Root Mean Square Errors (RMSEs), for one-to four-quarters-ahead out-of-sample forecasts over the horizon of 2003:01 to 2006:04, show that, except for the one-quarter-ahead forecast of the growth rate of the of nominal effective exchange rate, large-scale BVARs outperform the other four models consistently and, generally, significantly.Journal of Economic Literature Classification: C11, C13, C33, C53.

show abstract

Co‐integration, error correction and improved medium‐term regional VAR forecasting

Cited by 45 publications

References 24 publications

Forecasting the Nikkei spot index with fractional cointegration

Forecasting the Nikkei spot index with fractional cointegration

Do we need a global VAR model to forecast inflation and output in South Africa?

Forecasting macroeconomic variables in a small open economy: a comparison between small‐ and large‐scale models

Contact Info

Product

Resources

About