Forecasting the duration of short‐term deflation episodes

• DOI: http://doi.org/10.1002/for.2514
• Published date: 01 July 2018
• Author: Yinkai Wu,Wojciech Charemza,Svetlana Makarova
• Date: 01 July 2018

RESEARCH ARTICLE

Forecasting the duration of short‐term deflation episodes

Wojciech Charemza

1

| Svetlana Makarova

1,2

| Yinkai Wu

3

1

VCAS, Vistula University, Warsaw,

Poland

2

SSES, University College London,

London, UK

3

Anhui Provincial Academy of Social

Sciences, Hefei, China

Correspondence

Svetlana Makarova, School of Slavonic and

East European Studies, University College

London, 16 Taviton Street, London WC1H

0BW, UK.

Email: s.makarova@ucl.ac.uk

Funding information

Opus 8, Grant/Award Number: 2014/15/

B/HS4/04263; ESRC/ORA, Grant/Award

Number: RES‐360‐25‐0003

Abstract

The paper proposes a simulation‐based approach to multistep probabilistic

forecasting, applied for predicting the probability and duration of negative

inflation. The essence of this approach is in counting runs simulated from a

multivariate distribution representing the probabilistic forecasts, which enters

the negative inflation regime. The marginal distributions of forecasts are

estimated using the series of past forecast errors, and the joint distribution is

obtained by a multivariate copula approach. This technique is applied for esti-

mating the probability of negative inflation in China and its expected duration,

with the marginal distributions computed by fitting weighted skew‐normal

and two‐piece normal distributions to autoregressive moving average ex post

forecast errors and using the multivariate Student tcopula.

KEYWORDS

duration forecast, inflation forecasting, multivariate copula analysis, negative inflation,probabilistic

forecasting, simulation

1|INTRODUCTION

The paper proposes a simple, albeit computationally

intensive, way of analyzing the joint distributions of

multihorizon probabilistic forecasts. The focus is on evalu-

ating the out‐of‐sample duration forecast. The concept is

illustrated by estimating the duration of negative inflation

in China.

There is already a huge body of literature on forecasting

the duration of events. In economics, the most popular

approaches are grounded within extreme value theory

(e.g., Gilli & Këllezi, 2006) and proportional hazard dura-

tion modeling. The latter is a development from survival

modeling, and is often used for analyzing the duration of

unemployment (e.g., Bover, Arellano, & Bentolila, 2002;

Lancaster, 1979). For a discussion on other approaches

see Men, Kolankiewicz, and Wirjanto (2015).

However, these methods do not seem to be fully

appropriate for forecasting the duration of short‐term

deflation episodes, defined by negative inflation. In this

paper, we regard deflation in a purely statistical sense,

as a decline in the average (weighted) level of consumers’

prices, without addressing its possible relation to aggre-

gate demand (see, e.g., Atkeson & Kehoe, 2004; Benhabib

& Spigel, 2009; for another approach to estimation of the

probability of deflation, based on data from inflation sur-

veys and inflation swap rates, see Fleckenstein, Longstaff,

& Lustig, 2017).

Firstly, historical episodes of negative inflation are

infrequent or even nonexistent for most countries, which

makes estimating from its historical appearance ineffi-

cient or actually impossible. Secondly, most of the propor-

tional hazard methods rely implicitly or explicitly on the

assumption of normality of the forecast distribution. With

inflation, this is evidently not a realistic assumption

(for the most recent evidence see Chaudhuri, Kim, &

Shin, 2016). The extreme value models are free of the

assumption of normality, but they usually rely on tight

and not easily testable assumptions (see, e.g., Kotz &

Nadarajah, 2000).

The approach we propose allows the probability and

duration of events (negative inflation in our case) to be

estimated, even if the events did not occur in the past. It

is grounded within multihorizon probability density

Received: 22 October 2016 Revised: 25 September 2017 Accepted: 2 December 2017

DOI: 10.1002/for.2514

Journal of Forecasting. 2018;37:475–488. Copyright © 2018 John Wiley & Sons, Ltd.wileyonlinelibrary.com/journal/for 475