Forecasting Inflation with a Random Walk

Pincheira, Pablo; Medel, Carlos A.

doi:10.2139/ssrn.2193936

Cited by 11 publications

(5 citation statements)

References 20 publications

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“…For the milk production form Cross Breed Cow (CBC), the best model is a random walk with drift (or an ARIMA (0,1,0) with drift), Pincheira and Medel. (2016).…”

Section: Resultsmentioning

confidence: 99%

Modelling and Forecasting of Milk Production in Chhattisgarh and India

Mishra¹,

Fatih²,

Niranjan³

et al. 2020

IJAR

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India is accounting for almost 20 percent of total milk production in the world and 70 percent of this share is coming from small, marginal farmers and landless people of the country residing in rural areas and this shows that dairy industry has an important role in social and economic development in India. Dairy is growing with a positive rate as per capita availability has reached to 375 (gms/day) in 2017-18 from 178 (gms/day) in 1990-91. In this study, time series data (2001-02 to 2015-16) on milk production and different milching species population of Chhattisgarh have been used to find out the suitable forecasting models for milk production and population of these mulching animals of Chhattisgarh. To meet the objective of study different Autoregressive Integrated Moving Average (ARIMA) models have been tried and among all ARIMA (0,2,0) model has been found more suitable for production of milk in India and Chhattisgarh both. Availability of milk is forecasted suitably by ARIMA (0,2,1) and ARIMA(0,1,1) for India and Chhattisgarh respectively. Similarly different ARIMA models have been fitted for population of different species animals. By this study milk production is expected to reach 219.73 MMT and 1.599 MMT by 2022-23 in India and Chhattisgarh respectively.

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“…For the milk production form Cross Breed Cow (CBC), the best model is a random walk with drift (or an ARIMA (0,1,0) with drift), Pincheira and Medel. (2016).…”

Section: Resultsmentioning

confidence: 99%

Modelling and Forecasting of Milk Production in Chhattisgarh and India

Mishra¹,

Fatih²,

Niranjan³

et al. 2020

IJAR

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“…These adaptative terms are also function of the unknown parameters of the model 3. For a formal derivation and generalization of this result, seePincheira and Medel (2012).…”

mentioning

confidence: 89%

Forecasting Inflation with a Simple and Accurate Benchmark: A Cross-Country Analysis

Pincheira

Medel

2012

SSRN Journal

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We explore the ability of several univariate models to predict inflation in a number of countries and at several forecasting horizons. We place special attention on forecasts coming from a family of ten seasonal models that we call the Driftless Extended Seasonal ARIMA (DESARIMA) family. Using out-of-sample Root Mean Squared Prediction Errors (RMSPE) we compare the forecasting accuracy of the DESARIMA models with that of traditional univariate time-series benchmarks available in the literature. Our results show that DESARIMA-based forecasts display lower RMSPE at short horizons for every single country, except one. We obtain mixed results at longer horizons. Roughly speaking, in half of the countries, DESARIMA-based forecasts outperform the benchmarks at long horizons. Remarkably, the forecasting accuracy of our DESARIMA models is surprisingly high in stable inflation countries, for which the RMSPE is barely higher than 100 basis points when the prediction is made 24-and even 36-months ahead. Resumen En este trabajo se explora la capacidad de varios modelos univariados para predecir la inflación de un conjunto de países a varios horizontes predictivos. Se pone especial atención en predicciones provenientes de una familia de diez modelos estacionales que es denominada Driftless Extended Seasonal ARIMA (DESARIMA). Mediante el cálculo de la raíz cuadrada del error cuadrático medio (RECM) fuera de muestra, se compara la capacidad predictiva de los modelos DESARIMA con la de modelos referenciales de series de tiempo tradicionalmente utilizados en la literatura. Los resultados indican que las predicciones basadas en modelos DESARIMA muestran una menor RECM para horizontes cortos en todos los países considerados, excepto en uno. Se obtienen resultados mixtos para horizontes mayores. Aproximadamente en la mitad de los países las predicciones basadas en modelos DESARIMA superan a los modelos referenciales en horizontes largos. Se destaca que la precisión predictiva de los modelos DESARIMA es sorprendentemente alta en países con inflación estable, para los cuales la RECM es algo mayor que 100 puntos base para predicciones realizadas a 24 y hasta 36 meses adelante. * We thank the comments of seminar participants at the Central Bank of Chile. Any errors or omissions are responsibility of the authors. The views and ideas expressed in this paper do not necessarily represent those of the Central Bank of Chile or its authorities.

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“…Hence, it is not used any available bias-correction estimation as those of Andrews (1993), Andrews and Chen (1994), Hansen (1999), Kim (2003), among others. This option is left because, as shown in Pincheira and Medel (2012) and Medel and Pincheira (2015), among the competing models to the GVAR it is included the RW, which results in a superior alternative for near-unity series. As the RW is used as a numerary model to compare the RMSFE, it results in a demanding benchmark for the GVAR-recalling the aim of this article.…”

Section: And Stock and Watson (2009)mentioning

confidence: 99%

“…In this article, it is used a driftless RW forecast, following the argument given in Pincheira and Medel (2012) and Medel and Pincheira (2015) that driftless RW-based forecast are unbiased. Iterating forward the AR(1) model we have:…”

Section: And Stock and Watson (2009)mentioning

confidence: 99%

Forecasting Inflation with the Hybrid New Keynesian Phillips Curve: A Compact-Scale Global VAR Approach

Medel¹

2018

International Economic Journal

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In this article, it is analysed the multihorizon predictive power of the Hybrid New Keynesian Phillips Curve (HNKPC) making use of a compact-scale Global VAR for the headline in ‡ation of six developed countries with di¤erent in ‡ationary experiences; covering from 2000.1 until 2014.12. The key element of this article is the use of direct measures of in ‡ation expectations-Consensus Economics-embedded in a Global VAR environment, i.e. modelling cross-country interactions. The Global VAR point forecast is evaluated using the Mean Squared Forecast Error (MSFE) statistic and statistically compared with several benchmarks. These belong to traditional statistical modelling, such as autoregressions (AR), the exponential smoothing model (ES), and the random walk model (RW). One last economics-based benchmark is the closed economy univariate HNKPC. The results indicate that the Global VAR is a valid forecasting procedure especially for the short-run. The most accurate forecasts, however, are obtained with the AR and especially with the univariate HNKPC. In the longrun, the ES model also appears as a better alternative rather than the RW. The MSPE is obviously a¤ected by the unanticipated e¤ects of the …nancial crisis started in 2008. So, when considering an evaluation sample just before the crisis, the GVAR also appears as a valid alternative in the long-run. The most robust forecasting devices across countries and horizons result in the univariate HNKPC, giving a role for economic fundamentals when forecasting in ‡ation.JEL-Codes: C22 ; C26 ; C53 ; E31 ; E37 ; E47.

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Forecasting Inflation with a Random Walk

Cited by 11 publications

References 20 publications

Modelling and Forecasting of Milk Production in Chhattisgarh and India

Modelling and Forecasting of Milk Production in Chhattisgarh and India

Forecasting Inflation with a Simple and Accurate Benchmark: A Cross-Country Analysis

Forecasting Inflation with the Hybrid New Keynesian Phillips Curve: A Compact-Scale Global VAR Approach

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