Forecasting UK GDP growth and inflation under structural change. A comparison of models with time-varying parameters

Barnett, Alina; Mumtaz, Haroon; Theodoridis, Konstantinos

doi:10.1016/j.ijforecast.2013.06.002

Cited by 51 publications

(45 citation statements)

References 32 publications

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“…Studies by Stock and Watson (2007) and Barnett et al (2014) show that the UCSV is appropriate for modeling and forecasting output growth and inflation rates. Studies by Stock and Watson (2007) and Barnett et al (2014) show that the UCSV is appropriate for modeling and forecasting output growth and inflation rates.…”

Section: Unobserved Component Model With Stochastic Volatilitymentioning

confidence: 99%

“…Another variant of the univariate model is the unobserved component model with stochastic volatility (UCSV). Studies by Stock and Watson (2007) and Barnett et al (2014) show that the UCSV is appropriate for modeling and forecasting output growth and inflation rates. As in Stock and Watson (2007), the UCSV can be expressed as…”

Section: Unobserved Component Model With Stochastic Volatilitymentioning

confidence: 99%

“…Such figures reveal information about the current state of the economy and play a key role in formulating appropriate monetary and fiscal policies. In addition, Herbst and Schorfheide (2012), Barnett, Mumtaz, and Theodoridis (2014), and Rossi and Sekhposyan (2014) argue that it is becoming more and more important to assess the uncertainty associated with the forecasts of models. Hence the need for accurately forecasting the growth rate of the economy cannot be overstated.…”

Section: Introductionmentioning

confidence: 99%

“…Hence we look at both linear and nonlinear models. In addition, Herbst and Schorfheide (2012), Barnett, Mumtaz, and Theodoridis (2014), and Rossi and Sekhposyan (2014) argue that it is becoming more and more important to assess the uncertainty associated with the forecasts of models. This is specifically why central bankers wish to evaluate how well models perform in forecasting a range (uncertainty) of future values of relevant macroeconomic variables, rather than just the point forecasts of these variables.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting US GNP growth: The role of uncertainty

Segnon

Gupta

Bekiros

et al. 2018

Journal of Forecasting

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A large number of models have been developed in the literature to analyze and forecast changes in output dynamics. The objective of this paper was to compare the predictive ability of univariate and bivariate models, in terms of forecasting US gross national product (GNP) growth at different forecasting horizons, with the bivariate models containing information on a measure of economic uncertainty. Based on point and density forecast accuracy measures, as well as on equal predictive ability (EPA) and superior predictive ability (SPA) tests, we evaluate the relative forecasting performance of different model specifications over the quarterly period of 1919:Q2 until 2014:Q4. We find that the economic policy uncertainty (EPU) index should improve the accuracy of US GNP growth forecasts in bivariate models. We also find that the EPU exhibits similar forecasting ability to the term spread and outperforms other uncertainty measures such as the volatility index and geopolitical risk in predicting US recessions. While the Markov switching time‐varying parameter vector autoregressive model yields the lowest values for the root mean squared error in most cases, we observe relatively low values for the log predictive density score, when using the Bayesian vector regression model with stochastic volatility. More importantly, our results highlight the importance of uncertainty in forecasting US GNP growth rates.

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Section: Unobserved Component Model With Stochastic Volatilitymentioning

confidence: 99%

Section: Unobserved Component Model With Stochastic Volatilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Forecasting US GNP growth: The role of uncertainty

Segnon

Gupta

Bekiros

et al. 2018

Journal of Forecasting

View full text Add to dashboard Cite

show abstract

“…In this last case, Barnett et al (2014) evidence how the VAR coe¢ cients may change faster during the recent crisis, while the changes are small in tranquil periods, hence the TVP-VAR is a better forecasting tool during turmoil events. We estimate the model using a Gibbs sampling algorithm and the posterior simulated is computed as proposed by Carter and Kohn (2004).…”

Section: Tvp-var Modelmentioning

confidence: 89%

Oil price forecastability and economic uncertainty

2015

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Information on economic policy uncertainty (EPU) does matter in predicting oil returns especially when accounting for omitted nonlinearities in the relationship between these two variables via a timevarying coe¢ cient approach. In this work, we compare the forecastability of standard, Bayesian and TVP-VAR models against the random-walk and benchmark AR models. Our results indicate that over the period 1900:1-2014:2 the time-varying VAR model with stochastic volatility outranks all alternative models.JEL Classi…cation: C22, C32, C53, E60, Q41

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Forecast evaluation of DSGE models: Linear and nonlinear likelihood

Chin

2022

Journal of Forecasting

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We apply a Bayesian approach to estimate a small‐scale New Keynesian Dynamic Stochastic General Equilibrium (DSGE) model, solved by linear and nonlinear solution techniques. In particular, the likelihood function for the linearized and nonlinearized models is approximated via the linear and two types of nonlinear filters, respectively. We evaluate the performance of the linearized/nonlinearized model in terms of the log marginal data density and out‐of‐sample forecasting exercise over the transition periods of “Great Inflation” to “Great Moderation” (GI‐GM) and “Great Moderation” to “Great Recession” (GM‐GR). Several results are found as follows. First, it is not clear whether the nonlinearized DSGE model has a better fit than a linearized one to the full sample, and the result is also applied over the transition period of GI‐GM when we compute the time‐varying marginal data density. Second, under the point forecast evaluation, we do not find any statistical evidence that the forecasting performance of DSGE models, either the linearized or nonlinearized one, is better than the VAR models on predicting GDP growth rate, inflation, and interest rates over two transition periods. Moreover, the forecast errors for three data series calculated from either the individual model or the case of “Aggregation across models” in “ Normal” period are significantly lower than those in “ Crisis” period. Third, under the density forecast evaluation across DSGE models, we do not find any significant difference on the sum of log predictive likelihood across models at all forecast horizons for three individual data series. In particular, the above results are also applied to two transition periods and real‐time data sets.

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Forecasting UK GDP growth and inflation under structural change. A comparison of models with time-varying parameters

Cited by 51 publications

References 32 publications

Forecasting US GNP growth: The role of uncertainty

Forecasting US GNP growth: The role of uncertainty

Oil price forecastability and economic uncertainty

Forecast evaluation of DSGE models: Linear and nonlinear likelihood

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