Advances in forecasting: An introduction in light of the debate on inflation forecasting

Banerjee, Anindya; Hall, Stephen G.; Κουρέτας, Γεώργιος Π.; Tavlas, George S.

doi:10.1002/for.2949

Cited by 6 publications

(1 citation statement)

References 8 publications

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“…A recent example is the COVID-19 pandemic, which affected crime rates in complex ways (Lopez & Rosenfeld, 2021). Another is the abrupt increase in inflation beginning in 2021 (Banerjee et al, 2023). Forecasts will also be incorrect when the conditions known to influence the crime rate change in unexpected ways.…”

Section: Discussionmentioning

confidence: 99%

Forecasting Future Crime Rates

Rosenfeld,

Berg

2023

Journal of Contemporary Criminal Justice

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Despite its importance for testing criminological theories and informing crime control policy, forecasting crime rates has all but disappeared from criminology. We argue for a resurgence of crime forecasting in the study of crime trends. As an example of the value, as well as the challenges, of forecasting, we engage in a forecasting exercise based on data from New York City. We discuss each of the steps taken to forecast New York’s violent and property crime rates to 2024, from preparing the data for reliable analysis, specifying the forecasting model, selecting the forecasting method, and validating the results. The results of autoregressive integrated moving average (ARIMA) forecast models show a rise in New York’s violent and property crime rates in 2022 and 2023 before flattening in 2024. Renewed attention to forecasting can help to secure the future of the study of crime trends.

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Section: Discussionmentioning

confidence: 99%

Forecasting Future Crime Rates

Rosenfeld,

Berg

2023

Journal of Contemporary Criminal Justice

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Inflation forecasting with rolling windows: An appraisal

Hall,

Tavlas,

Wang

et al. 2024

Journal of Forecasting

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We examine the performance of rolling windows procedures in forecasting inflation. We implement rolling windows augmented Dickey–Fuller (ADF) tests and then conduct a set of Monte Carlo experiments under stylized forms of structural breaks. We find that as long as the nature of inflation is either stationary or non‐stationary, popular varying‐length window techniques provide little advantage in forecasting over a conventional fixed‐length window approach. However, we also find that varying‐length window techniques tend to outperform the fixed‐length window method under conditions involving a change in the inflation process from stationary to non‐stationary, and vice versa. Finally, we investigate methods that can provide early warnings of structural breaks, a situation for which the available rolling windows procedures are not well suited.

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On the Detection of Structural Breaks: The Case of the Covid Shock

Hall,

Tavlas,

Trapani

et al. 2024

Journal of Forecasting

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Both the Federal Reserve (Fed) and the European Central Bank (ECB) have been criticized for not having perceived that the outbreak of Covid at the beginning of 2020 would lead to a structural change in inflation in the early 2020s. Both central banks viewed the initial inflation surge in 2021 as temporary and delayed monetary tightening until 2022. We argue that the existing literature on structural breaks could not have been useful to policymakers because it identifies the breaks in an arbitrary way. The tests used to identify breaks do not incorporate prior knowledge that a break may have occurred so that the tests have very little power to detect a break that occurs at the end of the sample. We show that, in the event of a major shock, such as Covid, using knowledge that a break may have occurred and testing for a break in a recursive way as new data become available could have alerted policymakers to the break in inflation. We conduct Monte Carlo simulations suggesting that our method would have identified that a break had occurred in inflation by the end of 2020, well before policymakers had perceived the break.

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Advances in forecasting: An introduction in light of the debate on inflation forecasting

Cited by 6 publications

References 8 publications

Forecasting Future Crime Rates

Forecasting Future Crime Rates

Inflation forecasting with rolling windows: An appraisal

On the Detection of Structural Breaks: The Case of the Covid Shock

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