A reappraisal of the Meese–Rogoff puzzle

Several explanations have been put forward for the Meese–Rogoff puzzle that exchange rate models cannot outperform the random walk in out-of-sample forecasting. We suggest that a simple explanation for the puzzle is the use of the root mean square error (RMSE) to measure forecasting accuracy, presenting a rationale as to why it is difficult to beat the random walk in terms of the RMSE. By using exactly the same exchange rates, time periods and estimation methods as those of Meese and Rogoff, we find that their results cannot be overturned even if the models are estimated with time-varying coefficients. However, we also find that the random walk can be outperformed by the same models if forecasting accuracy is measured in terms of the ability to predict direction, in terms of a measure that combines magnitude and direction and in terms of profitability.     

IMPORT FORECASTS FOR INPUT-OUTPUT MODELS*

The paper presents forecasting models for (1) the share of competitive imports in the total demand for a commodity group and (2) the level of demand for competitive imports of a commodity group. The two forecasting models are used, respectively, with (1) input-output models which incorporate market share parameters as one vector of coefficients and (2) input-output models which assume imports have been determined autonomously. It is shown that these two types of input-output models can be made workable by prefixing one or other of the import forecasting models to the input-output model. Tests are made of the forecasting ability of the combined models.     

Cointegration as an explanation for the Meese–Rogoff puzzle

Some economists suggest that the failure of exchange-rate models to outperform the random walk in exchange rate forecasting out of sample can be attributed to failure to take into account cointegration when it is present. We attempt to find out if cointegration matters for forecasting accuracy by examining the relation between the stationarity and size of the forecasting error. Results based on three macroeconomic models of exchange rates do not provide strong support for the proposition that cointegration matters for forecasting accuracy. The simulation results show that while stationary errors tend to be smaller than non-stationary errors, this is not a universal rule. Irrespective of the presence or absence of cointegration, none of the three models can outperform the random walk in out-of-sample forecasting, which means that cointegration cannot solve the Meese–Rogoff puzzle.     

Box-Jenkins Methoden — Alternative Verfahren zur Prognose ökonomischer Zeitreihen

Summary In this paper we try to clarify whether the use ofBox-Jenkins methods would have improved the forecasting performance in Austria during the recession of 1975. For this purpose we estimate ARIMA models for gross national product, private consumption, investment in plant and equipment, and inventory investment. We then compare the forecasts derived from these models with the results of more convential forecasting techniques. It can not be expected that Box-Jenkins methods predict a business cycle turning point. But, as soon as the recession was under way Box-Jenkins methods were faster in adapting to the new situation than conventional forecasting techniques. We found that the accuracy of Box-Jenkins predictions depends to a large extent on the length of the forecasting horizon. Our results suggest that the forecasting horizon should not exceed one year. All in all, Box-Jenkins methods applied together with the forecasting techniques already in use could further improve the forecasting performance.     

What should the value of lambda be in the exponentially weighted moving average volatility model?

Forecasting volatility is fundamental to forecasting parametric models of value-at-risk. The exponentially weighted moving average (EWMA) volatility model is the recommended model for forecasting volatility by the Riskmetrics group. For monthly data, the lambda parameter of the EWMA model is recommended to be set to 0.97. In this study, we empirically investigate if this is the optimal value of lambda in terms of forecasting volatility. Employing monthly realized volatility as the benchmark for testing the value of lambda, it is found that a value of lambda of 0.97 is far from optimal. The tests are robust to a variety of test statistics. It is further found that the optimal value of lambda is time varying and should be based upon recent historical data. The article offers a practical method to increase the reliability and accuracy of value-at-risk forecasts that can be easily implemented within an Excel spreadsheet.     

Volatility forecasting using high frequency data: Evidence from stock markets

The paper aims to suggest the best volatility forecasting model for stock markets in Turkey. The findings of this paper support the superiority of high frequency based volatility forecasting models over traditional GARCH models. MIDAS and HAR-RV-CJ models are found to be the best among high frequency based volatility forecasting models. Moreover, MIDAS model performs better in crisis period. The findings of paper are important for financial institutions, investors and policy makers.     

A comparison of Logistic and Harvey models for electricity consumption in New Zealand

There have been a number of forecasting models based on various forms of the logistic growth curve. This paper investigates the effectiveness of two forms of Harvey models and a Logistic model for forecasting electricity consumption in New Zealand. The three growth curve models are applied to the Domestic and Non-Domestic sectors and Total electricity consumption in New Zealand. The developed models are compared using their goodness of fit to historical data and forecasting accuracy over a period of 19 years. The comparison revealed that the Harvey model is a very appropriate candidate for forecasting electricity consumption in New Zealand. The developed models are also compared with some available national forecasts.     

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.     

Time series analysis of monthly beef cattle prices with nonlinear autoregressive models

This paper analyses the dynamics underlying a time series of the monthly average beef cattle price received by producers in the State of São Paulo (Brazil). The time series under study records monthly prices since 1954. An exploratory analysis suggested that after a period of intense government intervention in the cattle and beef markets, the underlying dynamics seem to be settling to a pattern similar to the one observed prior to that period. In order to try to verify if the underlying dynamics after the interventionist phase are similar to those in former times, a forecasting procedure has been used based on nonlinear autoregressive models. This tye of models were used after the BDS test showed significant results which can be interpreted as nonlinearities in the data. The results discussed in the paper seem to suggest that after a period of intense interventions that lasted over two decades, the current underlying dynamics are close (from a forecasting point of view) to those observed more than thirty years ago.     

Forecasting realized volatility of crude oil futures with equity market uncertainty

This paper examines whether the equity market uncertainty (EMU) index contains incremental information for forecasting the realized volatility of crude oil futures. We use 5-min high-frequency transaction data for WTI crude oil futures and develop six heterogeneous autoregressive (HAR) models based on classical HAR-type models. The empirical results suggest that EMU contains more incremental information than the economic policy uncertainty (EPU) for forecasting the realized volatility of crude oil futures. More importantly, we argue that EMU is a non negligible additional predictive variable that can significantly improve the 1-day ahead predictive accuracy of all six HAR-type models, and improve the 1-week ahead forecasting performance of the HAR-RV, HAR-RV-J, HAR-RSV, HAR-RV-SJ models. These findings highlight a strong short-term and a weak mid-term predictive ability of EMU in the crude oil futures market.     

Forecasting tourism demand to Catalonia: Neural networks vs. time series models

The increasing interest aroused by more advanced forecasting techniques, together with the requirement for more accurate forecasts of tourism demand at the destination level due to the constant growth of world tourism, has lead us to evaluate the forecasting performance of neural modelling relative to that of time series methods at a regional level. Seasonality and volatility are important features of tourism data, which makes it a particularly favourable context in which to compare the forecasting performance of linear models to that of nonlinear alternative approaches. Pre-processed official statistical data of overnight stays and tourist arrivals from all the different countries of origin to Catalonia from 2001 to 2009 is used in the study. When comparing the forecasting accuracy of the different techniques for different time horizons, autoregressive integrated moving average models outperform self-exciting threshold autoregressions and artificial neural network models, especially for shorter horizons. These results suggest that the there is a trade-off between the degree of pre-processing and the accuracy of the forecasts obtained with neural networks, which are more suitable in the presence of nonlinearity in the data. In spite of the significant differences between countries, which can be explained by different patterns of consumer behaviour, we also find that forecasts of tourist arrivals are more accurate than forecasts of overnight stays.     

Seasonal decomposition and forecasting of telecommunication data: A comparative case study

In this paper, forecasting models for the monthly outgoing telephone calls in a University Campus are presented. The data have been separated in the categories of international and national calls as well as calls to mobile phones. The total number of calls has also been analyzed. Three different methods, namely the Seasonal Decomposition, Exponential Smoothing Method and SARIMA Method, have been used. Forecasts with 95% confidence intervals were calculated for each method and compared with the actual data. The outcome of this work can be used to predict future demands for the telecommunications network of the University.     

Forecasting spring wheat acreages without government controls

A number of regression models with various dependent and independent variables have been developed to forecast the acres of spring wheat under a regime of no governmental controls. A product model and an additive model were analyzed as to their accuracies in explaining historic data and their usefulness in forecasting spring wheat acreages. The additive model proved to be statistically superior to the product model, but the controlled sensitivity of the product model proved to be the better for forecasting purposes.     

Of maps and territories : The use and abuse of socioeconomic modeling in support of decision making

While the need for better data and models to support environmental decision making is generally recognized, the need for new approaches to how those data and models are used in the policy-making process has received less attention. Yet the relationship between analysis and policy is often characterized by problems of misunderstanding and mistrust between analysts and decision makers. The purpose of this paper is to examine the role of socioeconomic models in forecasting and decision making about environmental problems, and to suggest ways in which such models can be developed and used so as to increase the chance of their playing not only a scientifically but also a politically useful and desirable role.     

OWA-based ANFIS model for TAIEX forecasting

In stock market forecasting, high-order time-series models that use previous several periods of stock prices as forecast factors are more reasonable to provide a superior investment portfolio for investors than one-order time-series models using one previous period of stock prices. However, in forecasting processes, it is difficult to deal with high-order stock data, because it is hard to give a proper weight to each period of past stock price, reduce data dimensions without losing stock information, and produce a comprehensive forecasting result based on stock data with nonlinear relationships.Additionally, there are two more drawbacks to past time-series models: (1) some assumptions (Bollerslev, 1986; Engle, 1982) about stock variables are required for statistical methods, such as the autoregressive model (AR) and autoregressive moving average (ARMA); (2) numeric time-series models have been presented to deal with forecasting problems for stock markets, but they can not handle the nonlinear relationships within the stock prices.To address these shortcomings, this paper proposes a new time series model, which employs the ordered weighted averaging (OWA) operator to fuse high-order data into the aggregated values of single attributes, a fusion adaptive network-based fuzzy inference system (ANFIS) procedure, for forecasting stock price in Taiwanese stock markets.In verification, this paper employs a seven-year period of the TAIEX stock index, from 1997 to 2003, as experimental datasets and the root mean square error (RMSE) as evaluation criterion. The experimental results indicate that the proposed model is superior to the listing methods in terms of root mean squared error.     

Uncertainty and crude oil market volatility: new evidence

ABSTRACT

The main goal of this paper is to investigate the predictability of five economic uncertainty indices for oil price volatility in a changing world. We employ the standard predictive regression framework, several model combination approaches, as well as two prevailing model shrinkage methods to evaluate the performances of the uncertainty indices. The empirical results based on simple autoregression models including only one index suggest that global economic policy uncertainty (GEPU) and US equity market volatility (EMV) indices have significant predictive power for crude oil market volatility. In addition, the model combination approaches adopted in this paper can improve slightly the performances of individual autoregressive models. Lastly, the two model shrinkage methods, namely Elastin net and Lasso, outperform other individual AR-type model and combination models in most forecasting cases. Other empirical results based on alternative forecasting methods, estimation window sizes, high/low volatility and economic expansion/recession time periods further make sure the robustness of our major conclusions. The findings in this paper also have several important economic implications for oil investors.     

Forecasting and combining competing models of exchange rate determination

This article investigates the out-of-sample forecast performance of a set of competing models of exchange rate determination. We compare standard linear models with models that characterize the relationship between exchange rate and the underlying fundamentals by nonlinear dynamics. Linear models tend to outperform at short forecast horizons especially when deviations from long-term equilibrium are small. In contrast, nonlinear models with more elaborate mean-reverting components dominate at longer horizons especially when deviations from long-term equilibrium are large. The results also suggest that combining different forecasting procedures generally produces more accurate forecasts than can be attained from a single model.     

Forecasting the realized volatility: the role of jumps

This article investigates the role of jump components dependent on the ABD-LM jump test in forecasting volatility. Our out-of-sample forecasting results show that compared with the ABD-LM jump component, its decomposition forms based on signed returns can significantly improve the models’ forecasting performance and our findings have important implications for investors and policymakers.     

Forecasting and analyzing the competitive diffusion of mobile cellular broadband and fixed broadband in Taiwan with limited historical data

Taiwan experienced the rapid growth of mobile cellular broadband from 2005 by introducing 3G operations and had higher penetration than the average of the developing countries, the world, and even the developed countries. There are many forecasting models which were developed and successfully predicted the diffusion of long lifecycle product, but there are very few forecasting models which were developed for predicting new products with short lifecycle. Assumption of these models is always the growth of products follows an S-shaped curve. As for the products which were just introduced to the market, it is very difficult to identify if they follow an S-shaped curve with their limited historical data. This research aims to apply Grey system theory to predict the diffusion of mobile cellular broadband and fixed broadband in Taiwan since Grey system theory has a characteristic which requires very limited primitive data (the least 4 data) to build a differential forecasting model. We use penetration as an indicator to describe the diffusion of new products. The numerical data show that the Grey forecasting models GM(1,1) built in this paper have higher prediction accuracy than logistic models and grey Verhulst models. Moreover, we apply Lotka–Volterra model to analyze the competitive relationship between mobile cellular broadband and fixed broadband. The empirical data show that the relationship is commensalism rather than predator–prey. These results can be extended to contribute to other researches.     

Dissecting models' forecasting performance

The fact that the predictive performance of models used in forecasting stock returns, exchange rates, and macroeconomic variables is not stable and varies over time has been widely documented in the forecasting literature. Under these circumstances excessive reliance on forecast evaluation metrics that ignores this instability in forecasting accuracy, like squared errors averaged over the whole forecast evaluation sample, masks important information regarding the temporal evolution of relative forecasting performance of competing models. In this paper we suggest an approach based on the combination of the Cumulated Sum of Squared Forecast Error Differential (CSSFED) of Welch and Goyal (2008) and the Bayesian change point analysis of Barry and Hartigan (1993) that tracks the contribution of forecast errors to the aggregate measures of forecast accuracy observation by observation. In doing so, it allows one to track the evolution of the relative forecasting performance over time. We illustrate the suggested approach by using forecasts of the GDP growth rate in Switzerland.