Inflation forecasting using the New Keynesian Phillips Curve with a time-varying trend

Does theory aid inflation forecasting? To address this question, we develop a novel forecasting procedure based upon a New Keynesian Phillips Curve that incorporates time-varying trend inflation, to capture shifts in central bank preferences and monetary policy frameworks. We generate theory-implied predictions for both the trend and cyclical components of inflation, and recombine them to obtain an overall inflation forecast. Using quarterly data for the Euro Area and the United States that cover almost half a century, we compare our inflation forecasting procedure against the most popular time series models. We find that our theory-based forecasts outperform these benchmarks that previous studies found difficult to beat. Our results are shown to be robust to structural breaks, geographic areas, and variants of the econometric specification. Our findings suggest that the scepticism concerning the use of theory in forecasting is unwarranted, and theory should continue to play an important role in policymaking.     

Long memory,economic policy uncertainty and forecasting US inflation: a Bayesian VARFIMA approach

We compare inflation forecasts of a vector autoregressive fractionally integrated moving average (VARFIMA) model against standard forecasting models. U.S. inflation forecasts improve when controlling for persistence and economic policy uncertainty (EPU). Importantly, the VARFIMA model, comprising of inflation and EPU, outperforms commonly used inflation forecast models.     

通货膨胀实时预测及菲利普斯曲线的适用性

本文从实时分析的视角,基于多种退势方法的产出缺口最终估计、准最终估计和实时估计序列,分别构建了四类预测模型对我国通货膨胀率进行预测,分析了产出缺口修正效应和滞后阶数变化效应对通胀预测的影响,并进一步考察了产出缺口在通胀预测中的作用及菲利普斯曲线在通胀预测中的适用性。研究结论表明,通胀率的实时预测效果要明显比基于最终数据的差,其中滞后阶数变化效应对实时预测精度的影响大于产出缺口修正效应;尤为重要的是,尽管在最终数据的预测分析中,产出缺口的引入能够提高通胀率的预测精度,但是在实时预测中,产出缺口没有提供有价值的信息,因此"产出—通胀"型菲利普斯曲线在我国通胀实时预测中并不适用。     

Forecasting South African inflation using non-linearmodels: a weighted loss-based evaluation

The conduct of inflation targeting is heavily dependent on accurate inflation forecasts. Non-linear models have increasingly featured, along with linear counterparts, in the forecasting literature. In this study, we focus on forecasting South African inflation by means of non-linear models and using a long historical dataset of seasonally adjusted monthly inflation rates spanning from 1921:02 to 2013:01. For an emerging market economy such as South Africa, non-linearities can be a salient feature of such long data, hence the relevance of evaluating non-linear models’ forecast performance. In the same vein, given the fact that 1969:10 marks the beginning of a protracted rising trend in South African inflation data, we estimate the models for an in-sample period of 1921:02–1966:09 and evaluate 1, 4, 12, and 24 step-ahead forecasts over an out-of-sample period of 1966:10–2013:01. In addition, using a weighted loss function specification, we evaluate the forecast performance of different non-linear models across various extreme economic environments and forecast horizons. In general, we find that no competing model consistently and significantly beats the LoLiMoT’s performance in forecasting South African inflation.     

Forecasting UK consumer price inflation using inflation forecasts

The inflation rate is a key economic indicator for which forecasters are constantly seeking to improve the accuracy of predictions, so as to enable better macroeconomic decision making. Presented in this paper is a novel approach which seeks to exploit auxiliary information contained within inflation forecasts for developing a new and improved forecast for inflation by modeling with Multivariate Singular Spectrum Analysis (MSSA). Unlike other forecast combination techniques, the key feature of the proposed approach is its use of forecasts, i.e. data into the future, within the modeling process and extracting auxiliary information for generating a new and improved forecast. We consider real data on consumer price inflation in UK, obtained via the Office for National Statistics. A variety of parametric and nonparametric models are then used to generate univariate forecasts of inflation. Thereafter, the best univariate forecast is considered as auxiliary information within the MSSA model alongside historical data for UK consumer price inflation, and a new multivariate forecast is generated. We find compelling evidence which shows the benefits of the proposed approach at generating more accurate medium to long term inflation forecasts for UK in relation to the competing models. Finally, through the discussion, we also consider Google Trends forecasts for inflation within the proposed framework.     

UK macroeconomic forecasting with many predictors: Which models forecast best and when do they do so?

Block factor methods offer an attractive approach to forecasting with many predictors. These extract the information in these predictors into factors reflecting different blocks of variables (e.g. a price block, a housing block, a financial block, etc.). However, a forecasting model which simply includes all blocks as predictors risks being over-parameterized. Thus, it is desirable to use a methodology which allows for different parsimonious forecasting models to hold at different points in time. In this paper, we use dynamic model averaging and dynamic model selection to achieve this goal. These methods automatically alter the weights attached to different forecasting models as evidence comes in about which has forecast well in the recent past. In an empirical study involving forecasting output growth and inflation using 139 UK monthly time series variables, we find that the set of predictors changes substantially over time. Furthermore, our results show that dynamic model averaging and model selection can greatly improve forecast performance relative to traditional forecasting methods.     

Combination forecasts of tourism demand with machine learning models

The main objective of this study is to analyse whether the combination of regional predictions generated with machine learning (ML) models leads to improved forecast accuracy. With this aim, we construct one set of forecasts by estimating models on the aggregate series, another set by using the same models to forecast the individual series prior to aggregation, and then we compare the accuracy of both approaches. We use three ML techniques: support vector regression, Gaussian process regression and neural network models. We use an autoregressive moving average model as a benchmark. We find that ML methods improve their forecasting performance with respect to the benchmark as forecast horizons increase, suggesting the suitability of these techniques for mid- and long-term forecasting. In spite of the fact that the disaggregated approach yields more accurate predictions, the improvement over the benchmark occurs for shorter forecast horizons with the direct approach.     

FORECASTING INFLATION USING DYNAMIC MODEL AVERAGING*

We forecast quarterly US inflation based on the generalized Phillips curve using econometric methods that incorporate dynamic model averaging. These methods not only allow for coefficients to change over time, but also allow for the entire forecasting model to change over time. We find that dynamic model averaging leads to substantial forecasting improvements over simple benchmark regressions and more sophisticated approaches such as those using time varying coefficient models. We also provide evidence on which sets of predictors are relevant for forecasting in each period.     

An Evaluation of Alternative Methods of Forecasting Australian Inflation

The purpose of this paper is to evaluate the forecast of Australian inflation based on four alternative procedures: a univariate time series model, an interest rate model, an error correction model and a public survey of inflation forecasts. We derive estimates of expected and unexpected inflation from each of the methods and compare the out-of-sample forecasting results. Based on a range of evaluation criteria, the time series model dominates the other models, with the interest rate model, the error correction model and the survey forecasts following in that order.     

Do we need a global VAR model to forecast inflation and output in South Africa?

This study determines whether the global vector autoregressive (GVAR) approach provides better forecasts of key South African variables than a vector error correction model (VECM) and a Bayesian vector autoregressive (BVAR) model augmented with foreign variables. The article considers both a small GVAR model and a large GVAR model in determining the most appropriate model for forecasting South African variables. We compare the recursive out-of-sample forecasts for South African GDP and inflation from six types of models: a general 33 country (large) GVAR, a customized small GVAR for South Africa, a VECM for South Africa with weakly exogenous foreign variables, a BVAR model, autoregressive (AR) models and random walk models. The results show that the forecast performance of the large GVAR is generally superior to the performance of the customized small GVAR for South Africa. The forecasts of both the GVAR models tend to be better than the forecasts of the augmented VECM, especially at longer forecast horizons. Importantly, however, on average, the BVAR model performs the best when it comes to forecasting output, while the AR(1) model outperforms all the other models in predicting inflation. We also conduct ex ante forecasts from the BVAR and AR(1) models over 2010:Q1–2013:Q4 to highlight their ability to track turning points in output and inflation, respectively.     

FORECASTING US INFLATION USING DYNAMIC GENERAL‐TO‐SPECIFIC MODEL SELECTION

We forecast US inflation using a standard set of macroeconomic predictors and a dynamic model selection and averaging methodology that allows the forecasting model to change over time. Pseudo out‐of‐sample forecasts are generated from models identified from a multipath general‐to‐specific algorithm that is applied dynamically using rolling regressions. Our results indicate that the inflation forecasts that we obtain employing a short rolling window substantially outperform those from a well‐established univariate benchmark, and contrary to previous evidence, are considerably robust to alternative forecast periods.     

Forecasting Brazilian inflation by its aggregate and disaggregated data: a test of predictive power by forecast horizon

This work aims to compare the forecast efficiency of different types of methodologies applied to Brazilian consumer inflation (Índice de Preços ao Consumidor Amplo; IPCA). We will compare forecasting models using disaggregated and aggregated data from IPCA over 12 months ahead. We used IPCA in a monthly basis, over the period between January 1996 and March 2012. Out-of-sample analysis will be made through the period of January 2008 to March 2012. The disaggregated models were estimated by Seasonal Autoregressive Integrated Moving Average (SARIMA) and will have different levels of disaggregation from IPCA as groups and items, as well as disaggregation with more economic sense used by Brazilian Central Bank as: (1) services, monitored prices, food and industrials and (2) durables, non-durables, semi-durables, services and monitored prices. Aggregated models will be estimated by time series techniques as SARIMA, state-space structural models and Markov-switching. The forecasting accuracy among models will be made by the selection model procedure known as Model Confidence Set developed by Peter Hansen, Asger Lunde and James Nason. We were able to find evidence of forecast accuracy gains in models using more disaggregated rather than aggregate data.     

Forecasting inflation through a bottom-up approach: How bottom is bottom?

The aim of this paper is to assess inflation forecast accuracy over the short-term horizon, using Consumer Price Index (CPI) disaggregated data, through a bottom-up approach. That is, aggregating forecasts is compared with aggregate forecasting. A new dimension to the question of to bottom-up or not is introduced by considering different levels of data disaggregation, namely a higher disaggregation level than the one considered up to now. This raises modelling issues that one has to cope with. In particular, it is suggested the use of a new strand of models, the Factor-Augmented SARIMA models. Considering as case-study the Portuguese one, we find an inverse relationship between the forecast horizon and the amount of information underlying the forecast, when minimizing the RMSFE.     

Measures of underlying inflation in the euro area: assessment and role for informing monetary policy

The paper evaluates the 24-month-ahead inflation forecasting performance of various indicators of underlying inflation and structural models. Measures derived using the generalized dynamic factor model (GDFM) overperform other measures over the monetary policy horizon and are leading indicators of headline inflation. Trimmed means, although weaker than GDFM indicators, have good forecasting performance, while indicators by permanent exclusion underperform but provide useful information about short-term dynamics. The forecasting performance of theoretically-founded models that relate monetary aggregates, the output gap, and inflation improves with the time horizon but generally falls short of that of the GDFM. A composite measure of underlying inflation, derived by averaging the statistical indicators and the model-based estimates, improves forecast accuracy by eliminating bias and offers valuable insight about the distribution of risks.     

Cross-Country Evidence on the Ability of the Nominal Interest Rate to Predict Inflation

This paper examines the viability of using short-term interest rates to forecast inflation as implied by the Fisher hypothesis. A major problem with this approach lies in the implicit assumptions that the real interest rate is constant and that the relationship between inflation and interest rate does not change over time. We demonstrate, using quarterly data for four OECD countries, that by relaxing these assumptions and allowing for seasonality in the inflation rate it is possible to obtain a model with a high degree of forecasting accuracy and efficiency.
JEL Classification Numbers: C22, C52, E31.     

金融状况、通胀不确定性与通胀预测——基于Markov机制转换模型的分析

一国的金融状况一般通过信贷传导机制、利率传导机制、汇率传导机制和资产价格传导机制来影响通胀水平并决定通胀趋势,但高通胀水平伴随着较大的通胀不确定性。FCI能有效预测我国的通胀趋势,高通胀状态下FCI对通胀趋势的预测能力强于低通胀状态;低通胀状态下FCI对通胀趋势的短期预测效果优于中长期。我国应尽快指定相关部门制定并定期公布FCI,充分发挥FCI的通胀预测功能,并以此帮助国家实施宏观经济监测、完善调控政策、提高通胀治理效率。要提高央行政策制定的透明度,应避免频繁的政策方向变动,政策调控应尽量平滑操作,从而维持货币政策的稳定性和连贯性、降低通胀不确定性。要充分利用低通胀环境赋予的有利时机推进价格等各项体制的改革。     

Forecasting Inflation Using Constant Gain Least Squares

This paper assesses the usefulness of constant gain least squares when forecasting inflation. An out‐of‐sample forecast exercise is conducted, in which univariate autoregressive models for inflation in Australia, Sweden, the United Kingdom and the United States are used. The results suggest that it is possible to improve the forecast accuracy by employing constant gain least squares instead of ordinary least squares. In particular, when using a gain of 0.05, constant gain least squares generally outperforms the corresponding autoregressive model estimated with ordinary least squares. In fact, at longer forecast horizons, the root mean square forecast error is reliably lowered for all four countries and for all lag lengths considered in the study.     

Inflation Volatility and Forecast Accuracy

We examine the statistical properties of inflation in a sample of inflation‐targeting (IT) and non‐IT countries. It is hard to distinguish in which monetary regime inflation is less volatile. Inflation became easier to forecast in both groups of countries after the introduction of IT. The improvement was greater for IT countries, but forecast errors remain smaller for non‐IT countries. Our analysis is based on a stochastic volatility model proposed by Stock and Watson and its novel modification. Forecasts from the modified model are generally superior to both simple benchmarks and the original Stock and Watson model.     

Improving GARCH volatility forecasts with regime-switching GARCH

Many researchers use GARCH models to generate volatility forecasts. Using data on three major U.S. dollar exchange rates we show that such forecasts are too high in volatile periods. We argue that this is due to the high persistence of shocks in GARCH forecasts. To obtain more flexibility regarding volatility persistence, this paper generalizes the GARCH model by distinguishing two regimes with different volatility levels; GARCH effects are allowed within each regime. The resulting Markov regime-switching GARCH model improves on existing variants, for instance by making multi-period-ahead volatility forecasting a convenient recursive procedure. The empirical analysis demonstrates that the model resolves the problem with the high single-regime GARCH forecasts and that it yields significantly better out-of-sample volatility forecasts. First Version Received: November 2000/Final Version Received: August 2001