Midpoint method and accuracy of variability forecasting

This study develops an alternative variability forecasting method, the midpoint method. This method, along with the interval computing and OLS lower and upper bound methods in the literature, is applied to predict variability in the stock market and mortgage rates. Results suggest that both the midpoint and interval computing methods can generate significantly higher accuracy in variability forecasts than the OLS lower and upper bound method. Nonetheless, the midpoint method requires less asymmetric distribution of input data than the interval computing.     

Forecasting the barbados money supply

The purpose of this paper is to provide an adequate forecasting method for the money supply in the Barbadian economy. This would assist the Central Bank in making decisions on monetary intervention. The performance of ARIMA and vector autoregressive forecasting models are investigated along with combinations of these models. The results of this study suggest that there are reasonable options available for obtaining reliable forecasts of the Barbados money supply. Our findings indicate that seasonal factors and interest rate effects should be comprehended within the forecasting model. We accomplished this through a combination forecasting procedure in which seasonal effects are captured by an ARIMA model and interest rates are introduced through a vector autoregressive forecasting model as exogenous variables.     

Measuring uncertainty and assessing its predictive power in the euro area

Expectations and uncertainty play a key role in economic behavior. This paper deals with both, expectations and uncertainty derived from the European Central Bank Survey of Professional Forecasters. Given the strong turbulences that the euro area macroeconomic indicators observe since 2007, the aim of the paper is to check whether there is any room for improvement of the consensus forecast accuracy for GDP growth and inflation when accounting for uncertainty. We propose a new measure of uncertainty, alternative to the ad hoc equal weights commonly used, based on principal components. We test the role of uncertainty in forecasting macroeconomic performance in the euro area between 2005 and 2015. We also check the role of surprises in the considered forecasting sample.     

Forecasting ECB monetary policy: Accuracy is a matter of geography

Monetary policy in the euro area is conducted within a multi-country, multi-cultural, and multi-lingual context. How does this heterogeneity affect the ability of economic agents to understand and to anticipate monetary policy by the European Central Bank (ECB)? Using a database of surveys of professional ECB policy forecasters in 24 countries, we find remarkable differences in forecast accuracy, and show that these have important repercussions on market behaviour. Explaining the differences in forecast accuracy, we provide evidence that they are partly related to geography and clustering around informational hubs, as well as to country-specific economic conditions. In large part this heterogeneity can be traced to differences in forecasting models.     

Does the vector error correction model perform better than others in forecasting stock price? An application of residual income valuation theory

This paper employs a multi-equation model approach to consider three statistic problems (heteroskedasticity, endogeneity and persistency), which are sources of bias and inefficiency in the predictive regression models. This paper applied the residual income valuation model (RIM) proposed by Ohlson (1995) to forecast stock prices for Taiwan three sectors. We compare relative forecasting accuracy of vector error correction model (VECM) with the vector autoregressive model (VAR) as well as OLS and RW models used in the prior studies. We conduct out-of-sample forecasting and employ two instruments to assess forecasting performance. Our empirical results suggest that the VECM statistically outperforms other three models in forecasting stock prices. When forecasting horizons extend longer, VECM produces smaller forecasting errors and performs substantially better than VAR, suggesting that the ability of VECM to improve VAR forecast accuracy is stronger with longer horizons. These findings imply that an error correction term (ECT) of the VECM contributes to improving forecast accuracy of stock prices. Our economic significance analyses and robustness tests for different data frequency are in favor of the superiority of VECM estimator.     

How do oil price forecast errors impact inflation forecast errors? An empirical analysis from US,French and UK inflation forecasts

This paper proposes an empirical investigation of the impact of oil price forecast errors on inflation forecast errors for three different sets of recent forecast data: the median of SPF inflation forecasts for the United States and the Central Bank inflation forecasts for France and the United Kingdom. Mainly two salient points emerge from our results. First, there is a significant contribution of oil price forecast errors to the explanation of inflation forecast errors, whatever the country or the period considered. Second, the pass-through of oil price forecast errors to inflation forecast errors is typically multiplied by around 2 when the oil price volatility is large.     

Comparative accuracy of forecasts of inflation: a Canadian study

An inaccurate forecast of inflation is costlier to economic agents when the inflation rate is high and volatile. In this situation, the use of more sophisticated and information-oriented forecasting models become economically efficient. We test this hypothesis by analysing the forecasting accuracy of vector auto-regressive (VAR), auto-regressive integrated moving average (ARIMA) and static expectation models. We use Canadian data and divide the post-sample forecasting period into four sub-periods, based on high/low and volatile/stable inflation. Prediction errors are compared for both short-term and long-term forecasts. Finally, the paper proposes a portfolio approach for obtaining a more accurate forecast of inflation.     

The out-of-sample performance of an exact median-unbiased estimator for the near-unity AR(1) model

We analyse the forecasting performance of several strategies when estimating the near-unity AR(1) model. We focus on the Andrews’ (1993) exact median-unbiased estimator (BC), the OLS estimator and the driftless random walk (RW). We also explore two pairwise combinations between these strategies. We do this to investigate whether BC helps in reducing forecast errors. Via simulations, we find that BC forecasts typically outperform OLS forecasts. When BC is compared to the RW we obtain mixed results, favouring the latter while the persistence of the true process increases. Interestingly, we find that the combination of BC-RW performs well in a near-unity scheme.     

Forecasting the Government Bond Term Structure in Australia

In this paper, we evaluate the performance of the dynamic Nielsen and Siegel interest rate model in forecasting Australian government bond yields. We compare a two‐stage OLS estimation procedure to a more powerful and robust state‐space framework estimated via a Kalman filter. We show that the one‐step approach generates smaller forecast errors than the two‐step procedure or a benchmark random walk model when forecasting the Australian government term structure across various horizons.     

Out-of-sample forecasting of the Canadian unemployment rates using univariate models

This article investigates the out-of-sample forecasting performance of some linear and nonlinear univariate time series models on the monthly seasonally adjusted Canadian unemployment rates during the 1980–2013 period. The findings reveal that nonlinear time series models better capture the asymmetry present in the unemployment rate series at short and long forecast horizons.     

Applying a microfounded-forecasting approach to predict Brazilian inflation

We investigate whether combining forecasts from surveys of expectations is a helpful empirical strategy for forecasting inflation in Brazil. We employ the FGV–IBRE Economic Tendency Survey, which consists of monthly qualitative information from approximately 2000 consumers since 2006, and also the Focus Survey of the Central Bank of Brazil, with daily forecasts since 1999 from roughly 250 professional forecasters. Natural candidates to win a forecast competition in the literature of surveys of expectations are the (consensus) cross-sectional average forecasts (AF). We first show that these forecasts are a bias-ridden version of the conditional expectation of inflation using the no-bias tests proposed in Issler and Lima (J Econom 152(2):153–164, 2009) and Gaglianone and Issler (Microfounded forecasting, 2015). The results reveal interesting data features: Consumers systematically overestimate inflation (by 2.01 p.p., on average), whereas market agents underestimate it (by 0.68 p.p. over the same sample). Next, we employ a pseudo out-of-sample analysis to evaluate different forecasting methods: the AR(1) model, the Granger and Ramanathan (J Forecast 3:197–204, 1984) forecast combination (GR) technique, a Phillips-curve based method, the Capistrán and Timmermann (J Bus Econ Stat 27:428–440, 2009) combination method, the consensus forecast (AF), the bias-corrected average forecast (BCAF), and the extended BCAF. Results reveal that: (i) the MSE of the AR(1) model is higher compared to the GR (and usually lower compared to the AF); and (ii) the extended BCAF is more accurate than the BCAF, which, in turn, dominates the AF. This validates the view that the bias corrections are a useful device for forecasting using surveys. The Phillips-curve based method has a median performance in terms of MSE, and the Capistrán and Timmermann (2009) combination method fares slightly worse.     

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.     

Interest rate assumptions and predictive accuracy of central bank forecasts

The interest rate assumptions for macroeconomic forecasts differ among central banks. Common approaches are given by the assumptions that interest rates remain constant over the forecast horizon, follow a path as expected by market participants or follow a path as expected by the central bank itself. Theoretical papers such as Svensson (The instrument-rate projection under inflation targeting: the Norwegian example. Centre for European Policy Studies Working Paper (127), 2006) and Galí (J Monet Econ 58:537–550, 2011) suggest an accuracy ranking for these forecasts, from employing central bank expectations yielding the highest forecast accuracy to conditioning on constant interest rates yielding the lowest. Yet, when investigating the predictive accuracy of the Bank of England’s and the Banco Central do Brasil’s forecasts for interest rates, inflation and output growth, we hardly find any significant differences between forecasts based on the different interest rate paths. Our results suggest that the choice of the interest rate assumption appears to be of minor relevance empirically.     

Market‐Based Measures of Monetary Policy Expectations and Their Evolution Since the Introduction of the Euro

The paper considers the relation between monetary policy expectations and financial markets in the case of Europe. A number of money market instruments are compared, with the result that the 1‐month forward interest rates extracted from the Libor yield curve has the best prediction power of the future monetary policy path. These forward rates have been used to study the evolution of market expectations regarding the monetary policy of the European Central Bank (ECB). The sharp increases and the following decreases in interest rates during 2000–2001 have reduced the predictive power of money market instruments, but smoother management of interest rates and better communication from the ECB has helped to improve the forecasting power of money market instruments.     

Inflation Targeting: What Can the ECB Learn From the Recent Experience of the Bank of England?

Establishment of the European Central Bank presents a rare opportunity to define the operations of a central bank without a prior track record. This paper asks what might be learnt from the recent experience of inflation targeting at the Bank of England before the ECB specifies an, as yet undefined, operational target. We consider whether there should be single or multiple targets and which inflation measure should be used, if at all. If inflation is targeted then a forecast of its value becomes the intermediate variable. This raises an issue of transparency and the compensating supply of information necessary to fill the gap, but too much 'openness' can also be problematic. The ECB must be accountable and the contracting approach may be useful although being seen to 'say' and 'do' the same thing is ultimately most important.     

Improving forecast accuracy of financial vulnerability: PLS factor model approach

We present a factor augmented forecasting model for assessing the financial vulnerability in Korea. Dynamic factor models often extract latent common factors from a large panel of time series data via the method of the principal components (PC). Instead, we employ the partial least squares (PLS) method that estimates target specific common factors, utilizing covariances between predictors and the target variable. Applying PLS to 198 monthly frequency macroeconomic time series variables and the Bank of Korea's Financial Stress Index (KFSTI), our PLS factor augmented forecasting models consistently outperformed the random walk benchmark model in out-of-sample prediction exercises in all forecast horizons we considered. Our models also outperformed the autoregressive benchmark model in short-term forecast horizons. We expect our models would provide useful early warning signs of the emergence of systemic risks in Korea's financial markets.     

A new multiscale decomposition ensemble approach for forecasting exchange rates

Due to the high complexity and strong nonlinearity nature of foreign exchange rates, how to forecast foreign exchange rate accurately is regarded as a challenging research topic. Therefore, developing highly accurate forecasting method is of great significance to investors and policy makers. A new multiscale decomposition ensemble approach to forecast foreign exchange rates is proposed in this paper. In the approach, the variational mode decomposition (VMD) method is utilized to divide foreign exchange rates into a finite number of subcomponents; the support vector neural network (SVNN) technique is used to model and forecast each subcomponent respectively; another SVNN technique is utilized to integrate the forecasting results of each subcomponent to generate the final forecast results. To verify the superiority of the proposed approach, four major exchange rates were chosen for model comparison and evaluation. The experimental results indicate that our proposed VMD-SVNN-SVNN multiscale decomposition ensemble approach outperforms some other benchmarks in terms of forecasting accuracy and statistical tests. This demonstrates that our proposed VMD-SVNN-SVNN multiscale decomposition ensemble approach is promising for forecasting foreign exchange rates.     

Dynamic forecasting of monetary exchange rate models: Evidence from cointegration

The Frenkel-Bilson and Dornbusch-Frankel monetary exchange rate models are used to estimate the out-of-sample forecasting performance for the U.S. dollar/Canadian dollar exchange rate. By using Johansen's multivariate cointegration, up to three cointegrating vectors were found between the exchange rate and macroeconomic fundamentals. This means that there is a long-run relationship between the exchange rate and economic fundamentals. Based on error correction models, two monetary models outperform the random walk model at the three-, six-, and 12-month forecasting horizons. Therefore, monetary exchange rate models are still useful in forecasting exchange rates.     

Asymmetric behavior of Australia's Big-4 banks in the mortgage market

This paper presents an alternative framework for modeling the behavior of banks in setting lending and/or saving rates. In a short-run dynamic model, we correct for deviations from the long-run path using three feedback coefficients capturing different disequilibria. This enables us to test for both amount and adjustment asymmetries by considering the size and direction of any deviations. We use this model to examine the relationship between the official cash rate (set by the Reserve Bank of Australia as a monetary policy tool) and the standard variable mortgage rates of Australian Big-4 banks using weekly data from 2001 to 2012. The evidence indicates both types of asymmetries along with synchronized rate-setting behavior. Overall, the banks immediately pass on 120% of any rate rise, but only 85% of any rate cut. Further, when mortgage rates are substantially above the equilibrium path, we find no significant attempt to lower rates, but faster adjustment when rates are below equilibrium values. This finding has important implications for the RBA's monetary policy transmission mechanism and the effectiveness of the expansionary versus contractionary policy.     

The administered Bank rate and the Canada-U.S. spot exchange rate: 1975–1979

This paper analyzes, for the period 1975–1979, the announcement effect of the unanticipated Canadian administered Bank rate changes (the Bank rate is analogous to the discount rate in the U.S.) on the Canada-U.S. dollar spot exchange rate. Leading and/or lagged announcement effects are also investigated. By examining the behaviour of the residuals, on and surrounding the announced Bank rate changes, derived from an autoregressive equation of the daily changes (4:30 p.m. to noon the following day) in the logarithm of the spot $ Canadian/$ U.S. rate the evidence seems to indicate, on the average, a significant adjustment (appreciation of the Canadian dollar for Bank rate increases and depreciation for Bank rate decreases) of the exchange rate on the day of the effective unanticipated administered Bank rate changes regardless of the assumptions made with respect to the probability distribution of the residuals — whether it be normal, symmetric stable with a characteristic exponent of 1.8, or nonparametric.