A VAR Forecasting Model of a Regional Economy: Its Construction and Comparative Accuracy

Kinal, Terrence; Ratner, Jonathan

doi:10.1177/016001768601000202

Cited by 44 publications

(24 citation statements)

References 14 publications

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“…These are recommended in Doan (1990). The initial values for f(i, j ) , the relative weights of variables j in equation i, are taken from Kind andRatner (1986) andShoesmith (1992). The weight of a national variable in a national equation, k,, is 0.6.…”

Section: A Bvar Model For Connecticutmentioning

confidence: 99%

A BVAR model for the connecticut economy

Dua

Ray

1995

Journal of Forecasting

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A Bayesian vector autorepssive (BVAR) model is developed for the Connecticut economy to forecast the unemployment rate, nonagricultural employment, real personal income, and housing permits authorized. The model includes both national and state variables. The Bayesian prior is selected on the basis of the accuracy of the out-of-sample forecasts. We find that a loose prior generally produces more accurate forecasts. The out-of-sample accuracy of the BVAR forecasts is also compared with that of forecasts from an unrestricted VAR model and of benchmark forecasts generated from univariate ARIMA models. The BVAR model generally produces the most accurate short-and long-term out-of-sample forecasts for 1988 through 1992. It also correctly predicts the direction of change.

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Section: A Bvar Model For Connecticutmentioning

confidence: 99%

A BVAR model for the connecticut economy

Dua

Ray

1995

Journal of Forecasting

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“…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

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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

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“…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

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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.

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A VAR Forecasting Model of a Regional Economy: Its Construction and Comparative Accuracy

Cited by 44 publications

References 14 publications

A BVAR model for the connecticut economy

A BVAR model for the connecticut economy

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

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