ARE LONG‐TERM INFLATION EXPECTATIONS WELL‐ANCHORED? EVIDENCE FROM THE EURO AREA AND THE UNITED STATES

This paper analyses the stability of long‐term inflation expectations and uncertainty, based on their sensitivity to innovations to observed inflation, short‐ and medium‐term forecast news. News is defined in a subjective sense and derived from revisions to shorter‐term fixed‐target forecasts. The assessment tests for presence of non‐linear effects, including regime changes during disinflation in the USA in the 1990s and the recent financial crisis. Stability is also investigated in terms of level evolution, based on a structural non‐linear and non‐Gaussian learning model to uncover the presence of a common trend underlying the long‐term dynamics of inflation, individual expectations, and uncertainty.     

MONTHLY GDP FORECASTING USING BRIDGE MODELS: APPLICATION FOR THE FRENCH ECONOMY

This paper presents a model to predict French gross domestic product (GDP) quarterly growth rate. The model is designed to be used on a monthly basis by integrating monthly economic information through bridge models, for both supply and demand sides, allowing thus economic interpretations. For each GDP component, bridge equations are specified by using a general‐to‐specific approach implemented in an automated way by Hoover and Perez and improved by Krolzig and Hendry. This approach allows to select explanatory variables among a large data set of hard and soft data. A rolling forecast study is carried out to assess the forecasting performance in the prediction of aggregated GDP, by taking publication lags into account in order to run pseudo real‐time forecasts. It turns out that the model outperforms benchmark models. The results show that changing the set of equations over the quarter is superior to keeping the same equations over time. In addition, GDP growth seems to be more precisely predicted from a supply‐side approach rather than a demand‐side approach.     

The evolution of inflation expectations in Canada and the US

We model inflation forecasts as monotonically diverging from an estimated long‐run anchor point towards actual inflation as the forecast horizon shortens. Fitting the model with forecaster‐level data for Canada and the US, we identify three key differences between the two countries. First, the average estimated anchor of US inflation forecasts has tended to decline gradually over time in rolling samples, from 3.4% for 1989–1998 to 2.2% for 2004–2013. By contrast, it has remained close to 2% since the mid‐1990 for Canadian forecasts. Second, the variance of estimates of the long‐run anchor is considerably lower for the panel of Canadian forecasters than US ones following Canada's adoption of inflation targets. And third, forecasters in Canada look much more alike than those in the US in terms of the weight that they place on the anchor. One explanation for these results is that an explicit inflation‐targeting regime (Canada) provides for less uncertainty about future monetary policy actions than a monetary policy regime where there was no explicit numerical inflation target (the US before 2012) to anchor expectations.     

Multi-horizon inflation forecasts using disaggregated data

In this paper we use multi-horizon evaluation techniques to produce monthly inflation forecasts for up to twelve months ahead. The forecasts are based on individual seasonal time series models that consider both, deterministic and stochastic seasonality, and on disaggregated Consumer Price Index (CPI) data. After selecting the best forecasting model for each index, we compare the individual forecasts to forecasts produced using two methods that aggregate hierarchical time series, the bottom-up method and an optimal combination approach. Applying these techniques to 16 indices of the Mexican CPI, we find that the best forecasts for headline inflation are able to compete with those taken from surveys of experts.     

An analysis of the informational content of New Zealand data releases: The importance of business opinion surveys

We examine the informational content of New Zealand data releases using a parametric dynamic factor model estimated with unbalanced real-time panels of quarterly data. The data are categorised into 21 different release blocks, allowing us to make 21 different factor model forecasts each quarter. We compare three of these factor model forecasts for real GDP growth, CPI inflation, non-tradable CPI inflation, and tradable CPI inflation with three different real-time forecasts made by the Reserve Bank of New Zealand each quarter. We find that, at some horizons, the factor model produces forecasts of similar accuracy to the Reserve Bank's forecasts. Analysing the marginal value of each of the data releases reveals the importance of the business opinion survey data—the Quarterly Survey of Business Opinion and the National Bank's Business Outlook survey—in determining how factor model predictions, and the uncertainty around those predictions, evolve through each quarter.     

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.     

Has US inflation really become harder to forecast?

Since the mid-1980s, Phillips curve forecasts of US inflation have been inferior to those of a conventional causal autoregression. However, little change in forecast accuracy is detected against the benchmark of a noncausal autoregression, more accurately characterizing US inflation dynamics.     

MONEY AND INFLATION IN INDIA: EVIDENCE FROM P‐STAR MODEL

This study uses P‐star model to examine the role of money in explaining inflation in India. In particular, we compare the performance of traditional Phillips curve approach against P‐star model in forecasting inflation. Moreover, the study estimates P‐star model using the alternative measures of money such as simple sum and Divisia M3, to examine the relevance of aggregation theoretic monetary aggregates in explaining inflation. The empirical results indicate that P‐star model with real money gap has an edge over traditional Phillips curve approach in forecasting inflation. More importantly, we found that the P‐star model estimated with Divisia real money gap performs better than its simple sum counterpart. These empirical findings suggest that the changes in real money gap play a crucial role in explaining inflation in India.     

A Bayesian method of combining judgmental and model-based density forecasts

This paper introduces a formal method of combining expert and model density forecasts when the sample of past forecasts is unavailable. It works directly with the expert forecast density and endogenously delivers weights for forecast combination, relying on probability rules only. The empirical part of the paper illustrates how the framework can be applied in forecasting US inflation by mixing density forecasts from an autoregressive model and the Survey of Professional Forecasters.     

Semi‐Parametric Estimates of Taylor Rules for a Small,Open Economy – Evidence from Switzerland

This article estimates the policy reaction function of the Swiss National Bank (SNB) using real‐time internal inflation forecasts and output gap estimates from 2000 to 2012. We analyze potential nonlinearities of policy rate responses to economic fundamentals using a novel semiparametric approach. We find a linear response of the SNB's policy rate to inflation forecasts but a strong nonlinear response of the policy rate to the output gap and exchange rate changes. This finding suggests that the SNB reacts to extreme movements of these variables if they become a concern for price stability and economic activity.     

Low‐inflation Targeting and Long‐run Unemployment*

The model of Akerlof, Dickens and Perry (2000) (ADP) predicts that low inflation may cause unemployment to persist at high levels. When applied to U.S. data, their results strongly rejected the conventional NAIRU model. We apply the ADP model to Swedish data. The fact that our Swedish data also reject the NAIRU model has a number of interesting implications for the Swedish economy and, potentially, for other European countries as well. The results indicate that raising the Swedish inflation target from 2 to 4 percent would bring long‐run unemployment down by several percentage points. The possibility of ADP‐type long‐run Phillips curves also across the euro countries may raise some concern about the EMU project. While detailed studies on other countries are needed, there is nothing to suggest that these non‐vertical Phillips curves would not differ considerably across the euro countries. Any single inflation level targeted by the ECB would then generate excess unemployment in individual member states.     

The Effects of Monetary Policy Shocks in the USA: A Forecast‐Augmented VAR Approach

This paper formulates a forward‐looking monetary policy function for the USA in a structural vector autoregression (VAR) model, by using forecasts of key macroeconomic variables, in addition to the ex post realised variables used in a standard VAR. Since this forecast‐augmented VAR (FOAVAR) uses both forecasted and realised variables, and the standard VAR uses only realised variables, the standard VAR is nested in the FOAVAR. I find that the Fed responds to forecasted macroeconomic variables more significantly than realised variables. I also find that the monetary policy shock in the FOAVAR generates impulse responses of variables that are consistent with the predictions of economic theories, while the policy shock in the standard VAR causes a price puzzle: an increase in the price level due to a contractionary policy shock. These results suggest that a monetary policy function identified in a standard VAR, by using only realised macroeconomic variables, may incorrectly represent the Fed's policy function.     

Asymmetric and Time‐Varying Causality between Inflation and Inflation Uncertainty in G‐7 Countries

We use Granger causality tests within a conditional Gaussian Markov switching vector autoregressive (MS‐VAR) model using monthly data for G‐7 countries covering the period 1959:12–2008:10 to examine the relationship between inflation and inflation‐uncertainty. The MS‐VAR model allows us to model parameter time‐variation so as to reflect changes in Granger causality, assuming that these changes are stochastic and governed by an unobservable Markov chain. Inflation uncertainty is measured as the conditional variance generated by a Fractionally Integrated Smooth Transition Autoregressive Moving Average‐Asymmetric Power ARCH (FISTARMA‐APARCH) model. The distinguishing feature of our approach from the previous studies is the determination of the sign of the Granger causality relationship between inflation and its uncertainty over time. First, using a rolling VAR model, we show that the relationship between inflation and inflation uncertainty is time varying with frequent breaks. Second, using the MS‐VAR model, we obtain strong evidence in favour of the Holland's ‘stabilizing Fed hypothesis’ for Canada, France, Germany, Japan, United Kingdom, and the United States. We also find evidence in favour of the Friedman hypothesis for Canada and the United States.     

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.     

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.     

Inflation Dynamics in a Small Open Economy

We evaluate the empirical performance of forward‐looking models for inflation dynamics in a small open economy. Using likelihood‐based testing procedures, we find that the exact formulation is at odds with Norwegian data. Moreover, some of the parameters in the model are not well identified. We also find that the inexact formulation is not rejected statistically using a test based on a minimum distance method. However, confidence regions also reveal an identification problem with this model. Instead, we find a well‐specified backward‐looking model with imperfect competition underlying the price setting, which is a model that outperforms an alternative forward‐looking model in‐sample. The backward‐looking model also forecasts somewhat better than the alternative forward‐looking model, during and after the recent financial crisis.     

MONETARY EXPANSION AND PRODUCTIVE PUBLIC EXPENDITURE IN A CASH‐IN‐ADVANCE ECONOMY*

Using a dynamic optimization model that incorporates a cash‐in‐advance constraint on both consumption and investment and productive public capital financed by a lump‐sum tax and seigniorage, this paper analyses the steady‐state effects of an increase in the inflation rate (the money growth rate) on output, private capital and welfare. The effects are negative at high inflation rates. However, at low inflation rates, the effects depend on the amount of lump‐sum tax revenue collected and therefore are either positive or negative.     

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.     

Re‐vitalizing money demand in the Euro area. Still valid at the zero‐lower bound

The analysis of monetary developments has always been a cornerstone of the ECB's monetary analysis and, thus, of its overall monetary policy strategy. In this respect, money demand models provide a framework for explaining monetary developments and assessing price stability over the medium term. It is a well‐documented fact in the literature that, when interest rates are at the zero‐lower bound, the analysis of money stocks become even more important for monetary policy. Therefore, this paper re‐investigates the stability properties of M3 demand in the euro area in the light of the recent economic crisis. A cointegration analysis is performed over the sample period 1983 Q1 and 2015 Q1 and leads to a well‐identified model comprising real money balances, income, the long‐term interest rate and the own rate of M3 holdings. The specification appears to be robust against the Lucas critique of a policy dependent parameter regime, in the sense that no signs of breaks can be found when interest rates reach the zero‐lower bound. Furthermore, deviations of M3 from its equilibrium level do not point to substantial inflation pressure at the end of the sample. Excess liquidity models turn out to outperform the autoregressive benchmark, as they deliver more accurate CPI inflation forecasts, especially at the longer horizons. The inclusion of unconventional monetary policy measures does not contradict these findings.     

FORECASTING GLOBAL EQUITY INDICES USING LARGE BAYESIAN VARS

This paper proposes a large Bayesian Vector Autoregressive (BVAR) model with common stochastic volatility to forecast global equity indices. Using a monthly dataset on global stock indices, the BVAR model controls for co‐movement commonly observed in global stock markets. Moreover, the time‐varying specification of the covariance structure accounts for sudden shifts in the level of volatility. In an out‐of‐sample forecasting application we show that the BVAR model with stochastic volatility significantly outperforms the random walk both in terms of point as well as density predictions. The BVAR model without stochastic volatility, on the other hand, shows some merits relative to the random walk for forecast horizons greater than six months ahead. In a portfolio allocation exercise we moreover provide evidence that it is possible to use the forecasts obtained from our model with common stochastic volatility to set up simple investment strategies. Our results indicate that these simple investment schemes outperform a naive buy‐and‐hold strategy.