Forecasting Inflation with Online Prices

Aparicio, Diego; Bertolotto, Manuel I.

doi:10.2139/ssrn.2740600

Cited by 6 publications

(5 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Price changes are weighted using Argentina's National Statistics Office (INDEC) official weights by CPI category. (See Cavallo, 2013, andBertolotto, 2020, for evidence that online price indices closely track and forecast official CPIs. )…”

Section: Prices and Inflationmentioning

confidence: 99%

Targeted Price Controls on Supermarket Products

Aparicio

Cavallo

2021

Review of Economics and Statistics

Self Cite

View full text Add to dashboard Cite

We study the impact of targeted price controls on supermarket products in Argentina. Using daily prices for controlled and non-controlled goods we examine the effects on inflation, product availability, entry and exit, and price dispersion. First, price controls have only a small and temporary effect on inflation that reverses soon after the controls are lifted. Second, contrary to common beliefs, controlled goods are consistently available for sale. Third, firms respond by introducing new varieties at higher prices. Overall, our results show that targeted price controls are just as ineffective as more traditional policies of price controls in reducing aggregate inflation.

show abstract

Section: Prices and Inflationmentioning

confidence: 99%

Targeted Price Controls on Supermarket Products

Aparicio

Cavallo

2021

Review of Economics and Statistics

Self Cite

View full text Add to dashboard Cite

show abstract

“…The Billion Prices Project's academic output, which is nicely summarized in Cavallo and Rigobon (2016), informs many of our modelling decisions and inferences. Aparicio and Bertolotto (2016) have presented CPI forecasts informed by web-scraped prices, as computed with a low order auto-regressive moving average model with exogenous inputs. The exogenous variable here is an aggregated CPI computed from web-scraped prices.…”

Section: Background To Index Modellingmentioning

confidence: 99%

Section: Remarks On Inferred Consumer Price Index Valuesmentioning

confidence: 99%

“…These figures ought to be compared with the findings of Aparicio and Bertolotto (2016) who considered forecasts of the aggregated UK log-CPI covering all consumer products monthly. Specifically, we concentrate on Table 1 of Aparicio and Bertolotto (2016). Their calculations suggest that regressing on aggregates of web-scraped log-prices can lead to reductions in RMSE from 0:00191 (for their basic uninformed AR(1) model) to 0:00118 (for their best performing model informed by on-line prices including fuel prices).…”

Section: Remarks On Inferred Consumer Price Index Valuesmentioning

confidence: 99%

See 1 more Smart Citation

Tracking and Modelling Prices Using Web-Scraped Price Microdata: Towards Automated Daily Consumer Price Index Forecasting

Powell

Nason

Elliott

et al. 2017

Journal of the Royal Statistical Society Series A: Statistics in Society

View full text Add to dashboard Cite

Summary.With the increasing relevance and availability of on-line prices that we see today, it is natural to ask whether the prediction of the consumer price index (CPI), or related statistics, may usefully be computed more frequently than existing monthly schedules allow for.The simple answer is 'yes', but there are challenges to be overcome first. A key challenge, addressed by our work, is that web-scraped price data are extremely messy and it is not obvious, a priori , how to reconcile them with standard CPI statistics. Our research focuses on average prices and disaggregated CPI at the level of product categories (lager, potatoes, etc.) and develops a new model that describes the joint time evolution of latent daily log-inflation rates driving prices seen on the Internet and prices recorded in official surveys, with the model adapting to various product categories. Our model reveals the differing levels of dynamic behaviour across product category and, correspondingly, differing levels of predictability. Our methodology enables good prediction of product-category-specific CPI immediately before their release. In due course, with increasingly complete web-scraped data, combined with the best survey data, the prospect of more frequent intermonth aggregated CPI prediction is an achievable goal.

show abstract

“…Hendry and Hubrich (2011) show that adding disaggregated sector-level information into forecast models improves forecast accuracy for aggregate US inflation. Aparicio and Bertolotto (2020) use combinations of high-frequency online price item series to forecast CPI one to three months ahead in ten advanced economies; their forecasts outperform benchmark models as well as surveys of forecasters by anticipating changes in official inflation rates. Closest related to our approach, Ibarra ( 2012) uses a factor model based on 243 CPI item series and 54 macroeconomic series to forecast aggregate CPI in Mexico, reaching a forecasting performance comparable to forecasts from expert surveys.…”

Section: Introductionmentioning

confidence: 99%