Box-Jenkins Methods: An Alternative to Econometric Models

Naylor, Thomas H.; Seaks, Terry G.; Wichern, Dean W.

doi:10.2307/1402755

Cited by 160 publications

(43 citation statements)

References 13 publications

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“…The coding was done independently (i.e., the coders did not meet each other) and it was done blindly (i.e., the coders were not aware of the hypotheses in this study). In each of the four comparisons from these studies (Vand6me 1963; Markland Naylor et al 1972) there was perfect agreement among the author and the two raters. In addition, five of the ex post prediction studies were coded (Kosobud 1970;Fair 1971;Cooper 1972;Elliott 1973;Granger and Newbold 1974).…”

Section: Empirical Evidencementioning

confidence: 74%

“…Efforts were made to test for statistical significance where this had not been done in the published study. In general, serious difficulties were encountered; most of these studies did not provide sufficient data (e.g., Naylor, Seaks, and Wichern 1972), others failed to use comparable time periods, and still others sufferedfrom small sample sizes. The most striking result was that not one study was found where the econometric method was signijicantly more accurate.…”

Section: Empirical Evidencementioning

confidence: 99%

See 1 more Smart Citation

Forecasting with Econometric Methods: Folklore Versus Fact

Armstrong¹

1978

J BUS

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Evidence from social psychology suggests that econometricians will avoid evidence that disconfirms their beliefs. Two beliefs of econometricians were examined: (1) Econometric methods provide more accurate short-term forecasts than do other methods; and (2) more complex econometric methods yield more accurate forecasts. A survey of 21 experts in econometrics found that 95% agreed with the first statement and 72% agreed with the second. A review of the published empirical evidence yielded little support for either of the two statements in the 41 studies. The method of multiple hypotheses was suggested as a research strategy that will lead to more effective use of disconfirming evidence. Although this strategy was suggested in 1890, it has only recently been used by econometricians. CommentsPostprint version.

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Section: Empirical Evidencementioning

confidence: 74%

Section: Empirical Evidencementioning

confidence: 99%

Forecasting with Econometric Methods: Folklore Versus Fact

Armstrong¹

1978

J BUS

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show abstract

“…The main reason behind using Box and Jenkins technique is that it has been shown to give relatively accurate forecasts. The results from comparative studies conducted by Naylor et al (1972) and Nelson (1973) show that the Box and Jenkins model, although simpler, was more effective than other such contemporary econometric models.…”

Section: Methods Adoptedmentioning

confidence: 98%

Modeling Growth Trend and Forecasting Techniques for Vehicular Population in India

Jha¹,

Sinha²,

Arkatkar³

et al. 2013

IJTTE

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Forecasting and estimation of growth in vehicular population is a sine qua non of any major transportation engineering development, requires capturing the past trend and using it to predict the future trend based on qualified assumptions, simulations and models created using explanatory variables. This work attempts to review the in vogue approaches and investigate a more contemporary approach, the Time Series (TS) Analysis. Three fundamentally different methods were explored and results from each of these analyses were collated to check for respective levels of accuracy in predicting vehicular population for the same target year. Within the scope of this study and estimation, results obtained from TS Analysis were found to be considerably more accurate than those from Trend Line Analysis and observably better than those from Econometric Analysis. To reinforce these observations and inferences drawn, a second set of analysis was done on more recent input by using AADT data from PeMS, California. Inter alia this was carried out to contrast any statistical improvement observed when doing TS analysis with rich and accurate data. With all the data sets used and locations analyzed for forecasting, the Time Series analysis technique was invariably found to be a potent tool for forecasting.

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“…ARIMA model is a time-series forecasting method which was proposed by Box and Jenkins in the early 1970s [7].ARIMA model contains three factors, p, d and q. AR is autoregressive (p), MA is moving average (q) and d is the level of differencing to stationarize the time series. The formula of the ARIMA model is:…”

Section: Autoregressive Integrated Moving Average (Arima) Modelsmentioning

confidence: 99%

A Short-Term Electricity Price Forecasting Scheme for Power Market

Gao¹,

Lo²,

Lu³

et al. 2016

WJET

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Electricity price forecasting has become an important aspect of promoting competition and safeguarding the interests of participants in electricity market. As market participants, both producers and consumers intent to contribute more efforts on developing appropriate price forecasting scheme to maximize their profits. This paper introduces a time series method developed by Box-Jenkins that applies autoregressive integrated moving average (ARIMA) model to address a best-fitted time-domain model based on a time series of historical price data. Using the model's parameters determined from the stationarized time series of prices, the price forecasts in UK electricity market for 1 step ahead are estimated in the next day and the next week. The most suitable models are selected for them separately after comparing their prediction outcomes. The data of historical prices are obtained from UK three-month Reference Price Data from April 1 st to July7 th 2010.

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Box-Jenkins Methods: An Alternative to Econometric Models

Cited by 160 publications

References 13 publications

Forecasting with Econometric Methods: Folklore Versus Fact

Forecasting with Econometric Methods: Folklore Versus Fact

Modeling Growth Trend and Forecasting Techniques for Vehicular Population in India

A Short-Term Electricity Price Forecasting Scheme for Power Market

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