Real-time inflation forecasting using non-linear dimension reduction techniques

Hauzenberger, Niko; Huber, Florian; Klieber, Karin

doi:10.1016/j.ijforecast.2022.03.002

Cited by 19 publications

(11 citation statements)

References 67 publications

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“…Inflation is an important aspect of consideration for the government in deciding an economic policy. Knowing the extent of inflation that occurs can give an idea in the future of how much of a country's income is suitable for consumption, storage, or investment [1]. The Consumer Price Index (CPI) is an indicator of governments such as the Badan Pusat Statistik (BPS) in calculating inflation that occurs where data comes from changes in the mean consumer goods and services used by households or consumers in a certain period [2].…”

Section: Introductionmentioning

confidence: 99%

Arima Model of Outlier Detection for Forecasting Consumer Price Index (Cpi)

Imron

Utami

Khaulasari

et al. 2022

BAREKENG: J. Math. & App.

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The Consumer Price Index (CPI) is a indicator used by Badan Pusat Statistik (BPS) which describes the average change in the prices paid by urban consumers for a market basket of consumer goods and services in a certain period. The case on Consumer Price Index (CPI) of Probolinggo City, if the Consumer Price Index (CPI) increase then describe inflation occurs and conversely. The Consumer Price Index (CPI) of Probolinggo City increase is not fixed. This study is to forecast the Consumer Price Index (CPI) that the results can be used as one of the considerations in carrying out economic development in the future. Research focused on the data of Consumer Price Index (CPI) of Probolinggo City from January 2014 to April 2022. Methodology implemented in this study is Autoregressive Integrated Moving Average (ARIMA). Result show that ARIMA without an outlier was the best model for predicting Consumer Price Index (CPI) of Probolinggo City for the next 8 months. This model shows the value of MAPE is . The value of forecasting results in each month has decreased and increased not so significantly where in May 2022 the forecasting value was 108,391 then in June 2022 the forecasting value became 108,411 and so on until December 2022 the forecasting results using ARIMA model of 107,845.

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Section: Introductionmentioning

confidence: 99%

Arima Model of Outlier Detection for Forecasting Consumer Price Index (Cpi)

Imron

Utami

Khaulasari

et al. 2022

BAREKENG: J. Math. & App.

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“…In order to meet the stationarity assumption of the time series, as presented by (Hair et al ., 2016), the first difference (prior price – current price) of this index is used.PIM-PF: Industrial physical production (IBGE). As in the ICEI variable, to meet the stationarity assumption of the time series, the first difference (prior price – current price) of this index is used.IPP: Producer price index (IBGE): For the IPP (PPI) variable, there was the need of carrying out nonlinear transformations, aiming to make the distribution more symmetric and closer to the normal distribution (Hauzenberger et al. , 2023).…”

Section: Methodological Proceduresmentioning

confidence: 99%

“…(3) IPP: Producer price index (IBGE): For the IPP (PPI) variable, there was the need of carrying out nonlinear transformations, aiming to make the distribution more symmetric and closer to the normal distribution (Hauzenberger et al, 2023). (6) IPCA: Broad national consumer price index (IBGE): the IPCA, calculated by the IBGE, has advantages of measuring the inflation of a set of national and foreign products of families whose income is between 1 and 40 minimum wages, and, thus, it guarantees a 90% coverage of the urban families in areas in which there is data gathering (Bernardino et al, 2020).…”

Section: Research Datamentioning

confidence: 99%

Exogenous shock: hierarchical effect of the industrial entrepreneur confidence in the mist of the COVID-19 pandemic

Conceição,

Lima

2023

RBF

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PurposeIn the context of investment decisions, the intricate interplay between exogenous shocks and their influence on investor confidence significantly shapes their behaviors and, consequently, their outcomes. Investment decisions are influenced by uncertainties, exogenous shocks as well as the sentiments and confidence of investors, factors typically overlooked by decision-makers. This study will meticulously examine these multifaceted influences and discern their intricate hierarchical nuances in the sentiments of industrial entrepreneurs during the COVID-19 pandemic.Design/methodology/approachEmploying the robust framework of the generalized linear latent and mixed models (GLLAMM), this research will thoroughly investigate individual and group idiosyncrasies present in diverse data compilations. Additionally, it will delve deeply into the exogeneity of disturbances across different sectors and regions.FindingsRelevant insights gleaned from this research elucidate the adverse influence of exogenous forces, including pandemics and financial crises, on the confidence of industrial entrepreneurs. Furthermore, a significant discovery emerges in the regional analysis, revealing a notable homogeneity in the propagation patterns of industrial entrepreneurs' perceptions within the sectoral and regional context. This finding suggests a mitigation of regional effects in situations of global exogenous shocks.Originality/valueWithin the realm of academic inquiry, this study offers an innovative perspective in unveiling the intricate interaction between external shocks and their significant impacts on the sentiment of industrial entrepreneurs. Furthermore, the utilization of the robust GLLAMM captures the hierarchical dimension of this relationship, enhancing the precision of analyses. This approach provides a significant impetus for data-informed strategic directions.

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“…Inflation forecasting is an important topic in macroeconomics, including for policymakers. There is recent evidence that traditional inflation benchmark forecasts can be outperformed by the use of big data in conjunction with machine-learning methods, and the outperformance is largely attributable to nonlinearities (e.g., Medeiros et al 2021;Goulet Coulombe 2022;Goulet Coulombe et al 2022;Hauzenberger, Huber, and Klieber forthcoming). In addition to nonlinearities, forecasting inflation is an interesting case study because there is evidence of structural breaks in inflation processes for numerous countries, including the United States (e.g., Watson 1996, 2003;O'Reilly and Whelan 2005;Bataa et al 2013Bataa et al , 2014.…”

Section: Forecasting Inflationmentioning

confidence: 99%

Untitled

2022

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We develop metrics based on Shapley values for interpreting time-series forecasting models, including "black-box" models from machine learning. Our metrics are model agnostic, so that they are applicable to any model (linear or nonlinear, parametric or nonparametric). Two of the metrics, iShapley-VI and oShapley-VI, measure the importance of individual predictors in fitted models for explaining the in-sample and out-of-sample predicted target values, respectively. The third metric is the performance-based Shapley value (PBSV), our main methodological contribution. PBSV measures the contributions of individual predictors in fitted models to the out-of-sample loss and thereby anatomizes out-of-sample forecasting accuracy. In an empirical application forecasting US inflation, we find important discrepancies between individual predictor relevance according to the in-sample iShapley-VI and out-ofsample PBSV. We use simulations to analyze potential sources of the discrepancies, including overfitting, structural breaks, and evolving predictor volatilities.

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Real-time inflation forecasting using non-linear dimension reduction techniques

Cited by 19 publications

References 67 publications

Arima Model of Outlier Detection for Forecasting Consumer Price Index (Cpi)

Arima Model of Outlier Detection for Forecasting Consumer Price Index (Cpi)

Exogenous shock: hierarchical effect of the industrial entrepreneur confidence in the mist of the COVID-19 pandemic

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