Causal relations between inflation and inflation uncertainty—Cross sectional evidence in favour of the Friedman-Ball hypothesis

We test for causality between inflation and its associated uncertainty by means of both in-sample and out-of-sample modelling. Our findings indicate that the impact of inflation on inflation uncertainty is more pronounced than the reverse causal effect.     

Evaluating the effect of monetary policy on unemployment with alternative inflation forecasts

This paper explores the role that inflation forecasts play in the uncertainty surrounding the estimated effects of alternative monetary rules on unemployment dynamics in the euro area and the US. We use the inflation forecasts of 8 competing models in a standard Bayesian VAR to analyse the size and the timing of these effects, as well as to quantify the uncertainty relative to the different inflation models under two rules. The results suggest that model uncertainty can be a serious issue and strengthen the case for a policy strategy that takes into account several sources of information. We find that combining inflation forecasts from many models not only yields more accurate forecasts than those of any specific model, but also reduces the uncertainty associated with the real effects of policy decisions. These results are in line with the model-combination approach that central banks already follow when conceiving their strategy.     

Cost-based Phillips Curve forecasts of inflation

It is a well-established idea that prices are a function of marginal cost, yet estimating a reliable measure of marginal cost is difficult to do. Stock and Watson (1999) use the Phillips Curve to forecast inflation for a variety of existing activity variables that researchers commonly use to proxy for marginal cost. This paper uses a similar type of approach to examine the performance of a new candidate for the activity variable, which is marginal cost measured following the theoretical methodology of Bils (1987), which we find to be simple yet powerful when implemented empirically. We then use the Phillips Curve to conduct pseudo out-of-sample inflation forecasts for the US using: output, unemployment, hours, the labor share, the capacity utilization rate, and the new measure of marginal cost. For almost all cases, forecast errors are lowest in the regressions with the new marginal cost variable, indicating that this new measure is an improvement over previous attempts to proxy for marginal cost.     

Real-time data revisions and the PCE measure of inflation

This paper tracks data revisions in the Personal Consumption Expenditure using the exclusions-from-core inflation persistence model. Keeping the number of observations the same, the regression parameters of earlier vintages of real-time data, beginning with vintage 1996:Q1, are tested for coincidence against the regression parameters of the last vintage of real-time data, used in this paper, which is vintage 2008:Q2 in a parametric and two nonparametric frameworks. The effects of data revisions are not detectable in the vast majority of cases in the parametric model, but the flexibility of the two nonparametric models is able to utilize the data revisions.     

Time scale evaluation of economic forecasts

A maximal overlap discrete wavelet transform is used to obtain time scale decompositions of economic forecasts and their errors. The generated time scale components can be used in loss measures and tests for comparing forecast accuracy to evaluate whether the forecasts accurately capture the cyclical features of the data.     

The use of consumer and business surveys in forecasting

Surveys improve forecasting performance by adding explanatory power to a model which is based on only past values of manufacturing growth. The issue addressed in this paper is whether surveys of production expectations, when added to equations that contain lagged values of a headline index pertaining to the real economy, improve forecasting performance. If so, it may be better for researchers to use not just the headline index, but production expectations or the Economic Sentiment Indicator if they wish to better predict manufacturing growth.     

German business cycle forecasts, asymmetric loss and financial variables

We examine the efficiency of German forecasts for output growth and inflation allowing for an asymmetric loss function of the forecasters. We find the loss of output growth forecasts to be approximately symmetric while there is an asymmetry in the loss of the inflation forecasts. The information of financial variables seems to be adequately incorporated into the output forecasts but to a lesser extent into the inflation forecasts.     

Do disaggregated CPI data improve the accuracy of inflation forecasts?

In this paper, we evaluate the role of using consumer price index (CPI) disaggregated data to improve the accuracy of inflation forecasts. Our forecasting approach is based on extracting the factors from the subcomponents of the CPI at the highest degree of disaggregation. The data set contains 54 macroeconomic series and 243 CPI subcomponents from 1992 to 2009 for Mexico. We find that the factor models that include disaggregated data outperform the benchmark autoregressive model and the factor models containing alternative groups of macroeconomic variables. We provide evidence that using disaggregated price data improves forecasting performance. The forecasts of the factor models that extract the information from the CPI disaggregated data are as accurate as the forecasts from the survey of experts.     

On the dynamics of international inflation

We investigate the dynamic properties of inflation in 20 OECD countries with a novel approach based on the autocorrelation function. We find evidence in favor of long memory and nonlinearity. Linear autoregressive models are shown to be misspecified.     

Time variation in inflation persistence: New evidence from modelling US inflation

This article explores how inflation persistence relates to the conduct and goals of monetary policy by presenting a new approach to modelling US inflation persistence and the Fed's dual mandate. Our framework fills a gap in pre-existing models by more flexibly accounting for diverse dynamic properties and shocks. Estimating a Phillips Curve model augmented with inflation volatilities and expectations, we find that the degree of monthly inflation persistence is time variant since World War II. Variations in persistence continue to be observed regardless of the absolute level of inflation and the extent of the trade-off between inflation and unemployment. We demonstrate that inflation persistence varies in line with expectations formed by memories of past inflation. This supports the case for more flexible monetary policy at times, as in the 1980s or especially the present decade, when inflation is more persistent.     

Thai inflation dynamics: A view from disaggregated price data

This paper utilizes disaggregated prices at the micro level to examine the patterns of price adjustment in Thailand. Among the key stylized facts, we found that the frequency of price changes are generally low, prices decreases are common, the size of price changes are large relative to the inflation rate, and there is significant dispersion in price levels as well as in the synchronicity of price changes across regions. To better understand the underlying sources of heterogeneous price dynamics, we conduct dynamic factor analyses and highlight the importance of relative price changes in driving the bulk of overall CPI movements. This suggests an important role of non-monetary factors in driving inflation. A significant Phillips curve relation is detected once idiosyncratic price changes are filtered out, reinforcing the importance of disaggregated analysis of price trends in the conduct of monetary policy.     

Inflation targeting in Latin America: Empirical analysis using GARCH models

This paper studies inflation dynamics in eight Latin American countries, some of which have adopted formal inflation targets (IT) as their monetary policy frameworks. We analyze the possible benefits associated with IT, not only in terms of inflation level and volatility, but also regarding other nonlinear characteristics of these series, such as volatility persistence or the fulfillment of the Friedman hypothesis. To describe inflation dynamics we use an unobserved components model, where each component can follow a GARCH type process. Once we estimate the model, the main findings of the empirical exercise confirm the favorable performance of IT.     

House price dynamics,conditional higher-order moments,and density forecasts

Most studies on housing price dynamics are only concerned with the conditional mean and variance, but overlook other higher-order conditional moments and the structural change characteristics inherent in housing prices. In order to take into account these two important issues, this study utilizes the generalized Markov switching GARCH model to explore house price dynamics and conditional distribution for US market over 1975Q1–2007Q4. The housing return follows two distinct dynamics: the bust regime and the boom regime. The volatility pattern is different in the bust and boom regimes. In addition, the conditional densities derived by the regime-switching model change dramatically over time and are significantly different from normal distribution. More importantly, the regime-switching model can detect in advance a weak US housing market such as the one that occurred in the middle of 2007. The in-sample fitting ability of regime-switching model, which incorporates higher-order moments, has significant improvements compared to the single-regime AR and AR-GARCH models. For the out-of-sample Value-at-Risk forecasting performance, the ability of regime-switching AR-GARCH model to forecast one-step-ahead density is better compared to the single-regime AR-GARCH model.     

Modeling the behaviour of inflation deviations from the target

This study tests for and models non-linearities in inflation deviations from the target in five OECD countries that adopted inflation targeting over the 1990s. Our tests reject the linearity hypothesis and we show that the exponential smooth transition autoregressive (ESTAR) model is capable of capturing the non-linear behavior of inflation misalignments. The extent of inflation deviations from the target varies across the OECD countries, with countries that consistently undershoot the target having a rapid adjustment process, whereas countries that overshoot the target have a slower revision back to equilibrium. Out-of-sample forecasts from the ESTAR model outperform the Markov regime-switching model.     

News and monetary shocks at a high frequency: A simple approach

We develop a simple approach to identify economic news and monetary shocks at a high frequency. The approach is used to examine financial market developments in the United States following the Federal Reserve’s May 22, 2013 taper talk suggesting that it would begin winding down its quantitative easing program. Our findings show that the sharp rise in 10-year Treasury bond yields immediately after the taper talk was largely due to monetary shocks, with positive economic news becoming increasingly important in subsequent months.     

Interpreting euro area inflation at high and low frequencies

Several authors have recently interpreted the European Central Bank's (ECB's) two-pillar framework as separate approaches to forecast and analyse inflation at different time horizons or frequency bands. The ECB has publicly supported this understanding of the framework. This paper presents further evidence on the behaviour of euro area inflation using band spectrum regressions, which allow for a natural definition of the short and long run in terms of specific frequency bands, and causality tests in the frequency domain. The main finding is that variations in inflation are well explained by low-frequency movements of money and real output growth and high-frequency fluctuations of the output gap.     

South African stock return predictability in the context data mining: The role of financial variables and international stock returns

In this paper, we examine the predictive ability, both in-sample and the out-of-sample, for South African stock returns using a number of financial variables, based on monthly data with an in-sample period covering 1990:01 to 1996:12 and the out-of-sample period of 1997:01 to 2010:04. We use the t-statistic corresponding to the slope coefficient in a predictive regression model for in-sample predictions, while for the out-of-sample, the MSE-F and the ENC-NEW tests statistics with good power properties were utilised. To guard against data mining, a bootstrap procedure was employed for calculating the critical values of both the in-sample and out-of-sample test statistics. Furthermore, we use a procedure that combines in-sample general-to-specific model selection with out-of-sample tests of predictive ability to further analyse the predictive power of each financial variable. Our results show that, for the in-sample test statistic, only the stock returns for our major trading partners have predictive power at certain short and long run horizons. For the out-of-sample tests, the Treasury bill rate and the term spread together with the stock returns for our major trading partners show predictive power both at short and long run horizons. When accounting for data mining, the maximal out-of-sample test statistics become insignificant from 6-months onward suggesting that the evidence of the out-of-sample predictability at longer horizons is due to data mining. The general-to-specific model shows that valuation ratios contain very useful information that explains the behaviour of stock returns, despite their inability to predict stock return at any horizon. The model also highlights the role of multiple variables in predicting stock returns at medium- to long run horizons.     

Real-time forecasts of economic activity for Latin American economies

Macroeconomic policy decisions in real-time are based on the assessment of current and future economic conditions. Crucially, these assessments are made difficult by the presence of incomplete and noisy data. The problem is more acute for emerging market economies, where most economic data are released infrequently with a (sometimes substantial) lag. This paper evaluates nowcasts and forecasts of real GDP growth using five models for ten Latin American countries. The results indicate the flow of monthly data helps to improve forecast accuracy, and the dynamic factor model consistently produces more accurate nowcasts and forecasts relative to other model specifications, across most of the countries we consider.     

Policy reform and optimal inflation rate for Japan in computable OLG economy

We build a computable OLG model with monetary growth to calculate the optimal level of inflation rate for Japan, and to study policy reforms make any quantitative impacts on it. Four main results were obtained: (i) the optimal inflation rate for Japan is calculated around 1.0%; (ii) the calculated underlying inflation rate is about 9% under the present Japanese economic and fiscal situation; (iii) to prevent high inflation, fiscal reconstruction needs to be implemented; and (iv) if fundamental fiscal reform is conducted, the optimal inflation rate might be achieved. These results are very robust to calibration.     

Conditional forecasts on SVAR models using the Kalman filter

This note shows how conditional forecasts from identified VAR models can be computed using Kalman filtering techniques. These techniques are nowadays routine for applied macroeconomists, and hence the computation of conditional forecasts using these methods are simple to implement.