Simple Identification and Specification of Cointegrated Varma Models

Kascha, Christian; Trenkler, Carsten

doi:10.1002/jae.2393

Cited by 5 publications

(10 citation statements)

References 42 publications

(80 reference statements)

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“…In a recent paper Kascha and Trenkler () show that a cointegrated VARMA model generates superior forecasts for US interest rates. Our application differs from theirs in several aspects.…”

Section: Term Structure Of Interest Ratesmentioning

confidence: 99%

“…Kascha and Trenkler () generalize the final moving average (FMA) representation proposed by Dufour and Pelletier () to cointegrated VARMA models, and use an information criterion to choose the AR and MA orders for the cointegrated VARMA model in levels. They find promising results relative to a multivariate random walk and a standard VECM for predicting US interest rates.…”

Section: Introductionmentioning

confidence: 99%

“…The first is the determination of the cointegrating rank. With the exception of Poskitt () the existing papers either assume this away by taking the cointegrating rank as known (Kascha and Trenkler, ), or use the Johansen method (Johansen, ) to determine the cointegrating rank (Lütkepohl and Claessen, ). The Johansen method is based on likelihood ratio tests for cointegration under the assumption that the data‐generating process (DGP) is a finite‐order VAR.…”

Section: Introductionmentioning

confidence: 99%

“…The second aspect that we address is the identification and specification of the EC‐VARMA model. The existing papers either rely on the identification of VARMA models in their Echelon form (Lütkepohl and Claessen, ), or opt for the FMA representation that is less general but easier to identify (Kascha and Trenkler, ). The rules for the identification of Echelon‐form VARMA models have often been found to be too unintuitive to understand.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Determination of Long‐run and Short‐run Dynamics in EC‐VARMA Models via Canonical Correlations

Athanasopoulos

Poskitt

Vahid

et al. 2015

J of Applied Econometrics

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SUMMARYThis article studies a simple, coherent approach for identifying and estimating error-correcting vector autoregressive moving average (EC-VARMA) models. Canonical correlation analysis is implemented for both determining the cointegrating rank, using a strongly consistent method, and identifying the short-run VARMA dynamics, using the scalar component methodology. Finite-sample performance is evaluated via Monte Carlo simulations and the approach is applied to modelling and forecasting US interest rates. The results reveal that EC-VARMA models generate significantly more accurate out-of-sample forecasts than vector error correction models (VECMs), especially for short horizons.

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“…In a recent paper Kascha and Trenkler () show that a cointegrated VARMA model generates superior forecasts for US interest rates. Our application differs from theirs in several aspects.…”

Section: Term Structure Of Interest Ratesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Determination of Long‐run and Short‐run Dynamics in EC‐VARMA Models via Canonical Correlations

Athanasopoulos

Poskitt

Vahid

et al. 2015

J of Applied Econometrics

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“…Over the past two decades, a number of authors (e.g., Athanasopoulos, Poskitt, & Vahid, 2012;Athanasopoulos & Vahid, 2008;Dufour & Pelletier, 2014;Dufour & Stevanović, 2013;Kascha & Trenkler, 2015;Lütkepohl & Claessen, 1997;Lütkepohl & Poskitt, 1996;Poskitt, 2016) have pointed out this unfortunate phenomenon and various approaches have been proposed aimed at making VARMAs accessible to applied macroeconomists. Nevertheless, VARs continue to dominate in this field.…”

Section: Introductionmentioning

confidence: 99%

Efficient estimation of Bayesian VARMAs with time‐varying coefficients

Chan

Eisenstat

2017

J of Applied Econometrics

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Summary Empirical work in macroeconometrics has been mostly restricted to using vector autoregressions (VARs), even though there are strong theoretical reasons to consider general vector autoregressive moving averages (VARMAs). A number of articles in the last two decades have conjectured that this is because estimation of VARMAs is perceived to be challenging and proposed various ways to simplify it. Nevertheless, VARMAs continue to be largely dominated by VARs, particularly in terms of developing useful extensions. We address these computational challenges with a Bayesian approach. Specifically, we develop a Gibbs sampler for the basic VARMA, and demonstrate how it can be extended to models with time‐varying vector moving average (VMA) coefficients and stochastic volatility. We illustrate the methodology through a macroeconomic forecasting exercise. We show that in a class of models with stochastic volatility, VARMAs produce better density forecasts than VARs, particularly for short forecast horizons.

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Rainfall and Temperature Predictions: Implications for Rice Production in the Lower River Region of The Gambia

Bagbohouna¹,

Ragatoa²,

Simon³

et al. 2020

ujar

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Climate change impacts have been the major subject of discussion for scientists from different fields of study, including the agriculture sector. This study investigates the effects and implications of future climate change on rice production in the Lower River Region of The Gambia. The study seeks the following specific objectives: i) Analyse temperature and rainfall trends over Lower River Region; ii) Determine the relationship between temperature, rainfalls and rice production in the study area; and iii) Simulate temperature, rainfalls and rice production as well as the existing relationship among those parameters in the future using seasonality. The trend was examined after an exploratory data analysis, a unit root test and a correlation analysis. The study revealed an increase in maximum temperature (Tmax) and a variation in minimum temperature (Tmin) where the increase is not constant over 1981-2015. Also, the harvested area, production and rainfall increased while yield decreased. The data was extrapolated to 2035 using a VARMA statistical forecast method. Ordinary Least Squares and robust linear regression models were applied to find out the future implications (2035 and subsequent near years) of the climate parameters on rice production using 1981 to 2015-year series. The model shows that by 2035, yields will negatively be affected by the increase in Tmax and positively by the very little variation in Tmin. But the risk is that the ratio is not balanced, the damages of Tmax will be greater than the good productions of Tmin. The Tmin will also decrease as a general trend occasioning severe conditions for rice production in the region. This reveals the effects of climate change on rice production even though the relationship between climatic and rice variables remains low, because of the numerous parameters in rice production. This calls for an urgent need to improve rice varieties that will thrive well in the anticipated new climatic conditions (high yielding, heat tolerant, saline tolerant and early maturing) and promotion of good cultural practices that save water to cope with future climate. This study suggests that more studies should include other parameters of rice production for improved predictions.

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Simple Identification and Specification of Cointegrated Varma Models

Cited by 5 publications

References 42 publications

Determination of Long‐run and Short‐run Dynamics in EC‐VARMA Models via Canonical Correlations

Determination of Long‐run and Short‐run Dynamics in EC‐VARMA Models via Canonical Correlations

Efficient estimation of Bayesian VARMAs with time‐varying coefficients

Rainfall and Temperature Predictions: Implications for Rice Production in the Lower River Region of The Gambia

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