nep-for New Economics Papers
on Forecasting
Issue of 2021‒02‒08
twelve papers chosen by
Rob J Hyndman
Monash University

  1. Predictability of Bull and Bear Markets: A New Look at Forecasting Stock Market Regimes (and Returns) in the US By Felix Haase; Matthias Neuenkirch
  2. Forecasting Commodity Prices Using Long Short-Term Memory Neural Networks By Racine Ly; Fousseini Traore; Khadim Dia
  3. "Go wild for a while!": A new asymptotically Normal test for forecast evaluation in nested models By Pincheira, Pablo; Hardy, Nicolás; Muñoz, Felipe
  4. The Mean Squared Prediction Error Paradox: A summary By Pincheira, Pablo; Hardy, Nicolas
  5. Summary of the Paper Entitled: Forecasting Fuel Prices with the Chilean Exchange Rate By Pincheira, Pablo; Jarsun, Nabil
  6. How to Improve Inflation Forecasting in Canada By Troy D Matheson
  7. Forecasting unemployment rate in the time of COVID-19 pandemic using Google trends data (case of Indonesia) By Fajar, Muhammad; Prasetyo, Octavia Rizky; Nonalisa, Septiarida; Wahyudi, Wahyudi
  8. The Road to Recovery from COVID-19 for Australian Tourism By George Athanasopoulos; Rob J Hyndman; Mitchell O'Hara-Wild
  9. An application of hybrid forecasting singular spectrum analysis – extreme learning machine method in foreign tourists forecasting By Fajar, Muhammad
  10. Comparison of ARIMA, SSA, and ARIMA – SSA hybrid model performance in Indonesian economic growth forecasting By Fajar, Muhammad; Hartini, Sri
  11. Principles and Algorithms for Forecasting Groups of Time Series: Locality and Globality By Pablo Montero-Manso; Rob J Hyndman
  12. GDP Forecasting using Payments Transaction Data By Arunav Das

  1. By: Felix Haase; Matthias Neuenkirch
    Abstract: The empirical literature of stock market predictability mainly suffers from model uncertainty and parameter instability. To meet this challenge, we propose a novel approach that combines the documented merits of diffusion indices, regime-switching models, and forecast combination to predict the dynamics in the S&P 500. First, we aggregate the weekly information of 115 popular macroeconomic and financial variables through an interaction of principal component analysis and shrinkage methods. Second, we estimate one-step Markov-switching models with time-varying transition probabilities using the diffusion indices as predictors. Third, we pool the forecasts in clusters to hedge against model risk and to evaluate the usefulness of different specifications. Our results show that we can adequately predict regime dynamics. Our forecasts provide a statistical improvement over several benchmarks and generate economic value by boosting returns, improving the certainty equivalent return, and reducing tail risk. Using the same approach for return forecasts, however, does not lead to a consistent outperformance of the historical average.
    Keywords: forecast combination, Markov-Switching Models, shrinkage methods, stock market regimes, time-varying transition probabilities
    JEL: C53 G11 G17
    Date: 2021
  2. By: Racine Ly; Fousseini Traore; Khadim Dia
    Abstract: This paper applies a recurrent neural network (RNN) method to forecast cotton and oil prices. We show how these new tools from machine learning, particularly Long-Short Term Memory (LSTM) models, complement traditional methods. Our results show that machine learning methods fit reasonably well the data but do not outperform systematically classical methods such as Autoregressive Integrated Moving Average (ARIMA) models in terms of out of sample forecasts. However, averaging the forecasts from the two type of models provide better results compared to either method. Compared to the ARIMA and the LSTM, the Root Mean Squared Error (RMSE) of the average forecast was 0.21 and 21.49 percent lower respectively for cotton. For oil, the forecast averaging does not provide improvements in terms of RMSE. We suggest using a forecast averaging method and extending our analysis to a wide range of commodity prices.
    Date: 2021–01
  3. By: Pincheira, Pablo; Hardy, Nicolás; Muñoz, Felipe
    Abstract: In this paper we present a new asymptotically normal test for out-of-sample evaluation in nested models. Our approach is a simple modification of a traditional encompassing test that is commonly known as Clark and West test (CW). The key point of our strategy is to introduce an independent random variable that prevents the traditional CW test from becoming degenerate under the null hypothesis of equal predictive ability. Using the approach developed by West (1996), we show that in our test the impact of parameter estimation uncertainty vanishes asymptotically. Using a variety of Monte Carlo simulations in iterated multi-step-ahead forecasts we evaluate our test and CW in terms of size and power. These simulations reveal that our approach is reasonably well-sized even at long horizons when CW may present severe size distortions. In terms of power, results are mixed but CW has an edge over our approach. Finally, we illustrate the use of our test with an empirical application in the context of the commodity currencies literature.
    Keywords: forecasting; random walk; out-of-sample; prediction; mean square prediction error
    JEL: C01 C1 C12 G17
    Date: 2021–01
  4. By: Pincheira, Pablo; Hardy, Nicolas
    Abstract: This is a summary of the paper entitled : “The Mean Squared Prediction Error Paradox”. In that paper, we show that traditional comparisons of Mean Squared Prediction Error (MSPE) between two competing forecasts may be highly controversial. This is so because when some specific conditions of efficiency are not met, the forecast displaying the lowest MSPE will also display the lowest correlation with the target variable. Given that violations of efficiency are usual in the forecasting literature, this opposite behavior in terms of accuracy and correlation with the target variable may be a fairly common empirical finding that we label here as "the MSPE Paradox." We characterize "Paradox zones" in terms of differences in correlation with the target variable and conduct some simple simulations to show that these zones may be non-empty sets. Finally, we illustrate the relevance of the Paradox with two empirical applications.
    Keywords: Mean Squared Prediction Error, Correlation, Forecasting, Time Series, Random Walk.
    JEL: C0 C00 C01 C02 C2 C21 C22 C4 C41 C44 C5 C51 C52 C53 C54 C58 E0 E3 E37 E5 E58 E6 F3 F31 F37 F4 F41 F44 F47 G00 G1 G12 G15 G17
    Date: 2020–12–29
  5. By: Pincheira, Pablo; Jarsun, Nabil
    Abstract: This draft is a summary of the paper entitled: Forecasting Fuel Prices with the Chilean Exchange Rate. In that paper we show that the Chilean exchange rate has the ability to predict the returns of oil prices and of three additional oil-related products: gasoline, propane and heating oil. The theoretical underpinnings of our empirical findings rely on the present-value theory for exchange rate determination and on the strong co-movement displayed by some commodity prices. The Chilean economy is heavily influenced by one particular commodity: copper, which represents nearly 50% of total national exports and attracts a similar share in terms of Foreign Direct Investment. As a consequence, the floating Chilean exchange rate is importantly affected by fluctuations in the copper price. As oil-related products display an important co-movement with base metal prices, it is reasonable to expect evidence of Granger causality from the Chilean peso to these oil-related products. We find substantial evidence of predictability both in-sample and out-of-sample. Our paper is part of a growing literature that in the recent years has explored the linkages between commodity prices and commodity currencies.
    Keywords: Exchange rates, energy, oil, gasoline, commodity prices, predictability, time-series
    JEL: C01 C02 C1 C12 C13 C2 C22 C3 C32 C4 C5 C51 C52 C53 C58 E31 E37 E58 F3 F31 F37 F4 F47 G12 G15 G17
    Date: 2020–12–30
  6. By: Troy D Matheson
    Abstract: Against the backdrop of an ongoing review of the inflation-targeting framework, this paper examines the real-time inflation forecasts of the Bank of Canada with the aim of identifying potential areas for improvement. Not surprisingly, the results show that errors in forecasting non-core inflation (commodity prices etc.) are found to be the largest contributors to overall inflation forecast errors. Perhaps more importantly, relatively small core inflation forecast errors appear to mask large and offsetting errors related to the output gap and the policy interest rate, partly reflecting a tendency to overestimate the neutral nominal policy rate in real time. Faced with these uncertainties, the Governing Council’s gradual approach to changing its policy settings appears to have served it well.
    Keywords: Inflation;Central bank policy rate;Economic forecasting;Output gap;Inflation targeting;WP,core inflation,inflation expectation
    Date: 2019–09–13
  7. By: Fajar, Muhammad; Prasetyo, Octavia Rizky; Nonalisa, Septiarida; Wahyudi, Wahyudi
    Abstract: The outbreak of COVID-19 is having a significant impact on the contraction of Indonesia`s economy, which is accompanied by an increase in unemployment. This study aims to predict the unemployment rate during the COVID-19 pandemic by making use of Google Trends data query share for the keyword “phk” (work termination) and former series from official labor force survey conducted by Badan Pusat Statistik (Statistics Indonesia). The method used is ARIMAX. The results of this study show that the ARIMAX model has good forecasting capabilities. This is indicated by the MAPE value of 13.46%. The forecast results show that during the COVID-19 pandemic period (March to June 2020) the open unemployment rate is expected to increase, with a range of 5.46% to 5.70%. The results of forecasting the open unemployment rate using ARIMAX during the COVID-19 period produce forecast values are consistent and close to reality, as an implication of using the Google Trends index query as an exogenous variable can capture the current conditions of a phenomenon that is happening. This implies that the time series model which is built based on the causal relationship between variables reflects current phenomenon if the required data is available and real-time, not only past historical data.
    Keywords: Unemployment, Google Trends, PHK, ARIMAX
    JEL: C22 C53 E24 E37 E39 J6 J64
    Date: 2020–11–30
  8. By: George Athanasopoulos; Rob J Hyndman; Mitchell O'Hara-Wild
    Abstract: COVID-19 has had a devastating effect on many industries around the world including tourism, and policy makers are interested in mapping out what the recovery path will look like. In this paper we focus on Australian tourism, analysing international arrivals and domestic flows. Both sectors have been severely affected by travel restrictions in the form of international and interstate border closures and regional lockdowns. We use statistical models of historical data to generate COVID-free counterfactual forecasts pretending that the pandemic never occurred. We also use survey responses from 443 tourism experts to generate scenario-based probabilistic forecasts for pessimistic, most-likely and optimistic paths to recovery. Using both sets of forecasts, we estimate the expected effect of the pandemic on the Australian tourism industry.
    Keywords: forecasting, judgemental, probabilistic, scenarios, survey
    Date: 2021
  9. By: Fajar, Muhammad
    Abstract: International tourism is one indicator of measuring tourism development. Tourism development is important for the national economy since tourism could boost foreign exchange, create business opportunities, and provide employment opportunities. The prediction of foreign tourist numbers in the future obtained from forecasting is used as an input parameter for strategy and tourism programs planning. In this paper, the Hybrid Singular Spectrum Analysis – Extreme Learning Machine (SSA-ELM) is used to forecast the number of foreign tourists. Data used is the number of foreign tourists January 1980 - December 2017 taken from Badan Pusat Statistik (Statistics Indonesia). The result of this research concludes that Hybrid SSA-ELM performance is very good at forecasting the number of foreign tourists. It is shown by the MAPE value of 4.91 percent with eight observations out a sample.
    Keywords: foreign tourist, singular spectrum analysis, extreme learning machine
    JEL: C22 C45 C51 E17
    Date: 2019–10–31
  10. By: Fajar, Muhammad; Hartini, Sri
    Abstract: The aim of this research is to compare among the performance of ARIMA, Singular Spectrum Analysis (SSA), and ARIMA-SSA hybrid model which is applied to Indonesian economic growth forecasting. Data used in this research is economic growth (quarter to quarter, q to q) 1983 Q2 – 2018Q2 taken from Badan Pusat Statistik (BPS). The result of this research concludes that ARIMA-SSA hybrid method shows a better performance in economic growth forecasting compared to ARIMA and SSA based on the RMSE results.
    Keywords: hybrid model, ARIMA-SSA, forecasting, growth
    JEL: C22 C45 E17
    Date: 2020–06–16
  11. By: Pablo Montero-Manso; Rob J Hyndman
    Abstract: Forecasting of groups of time series (e.g. demand for multiple products offered by a retailer, server loads within a data center or the number of completed ride shares in zones within a city) can be approached locally, by considering each time series as a separate regression task and fitting a function to each, or globally, by fitting a single function to all time series in the set. While global methods can outperform local for groups composed of similar time series, recent empirical evidence shows surprisingly good performance on heterogeneous groups. This suggests a more general applicability of global methods, potentially leading to more accurate tools and new scenarios to study. However, the evidence has been of empirical nature and a more fundamental study is required. Formalizing the setting of forecasting a set of time series with local and global methods, we provide the following contributions: • We show that global methods are not more restrictive than local methods for time series forecasting, a result which does not apply to sets of regression problems in general. Global and local methods can produce the same forecasts without any assumptions about similarity of the series in the set, therefore global models can succeed in a wider range of problems than previously thought. • We derive basic generalization bounds for local and global algorithms, linking global models to pre-existing results in multi-task learning: We find that the complexity of local methods grows with the size of the set while it remains constant for global methods. Global algorithms can afford to be quite complex and still benefit from better generalization error than local methods for large datasets. These bounds serve to clarify and support recent experimental results in the area of time series forecasting, and guide the design of new algorithms. For the specific class of limited-memory autoregressive models, this bound leads to the design of global models with much larger memory than what is effective for local methods. • The findings are supported by an extensive empirical study. We show that purposely naïve algorithms derived from these principles, such as global linear models fit by least squares, deep networks or even high order polynomials, result in superior accuracy in benchmark datasets. In particular, global linear models show an unreasonable effectiveness, providing competitive forecasting accuracy with far fewer parameters than the simplest of local methods. Empirical evidence points towards global models being able to automatically learn long memory patterns and related effects that are only available to local models if introduced manually.
    Keywords: time series, forecasting, generalization, global, local, cross-learning, pooled regression
    Date: 2020
  12. By: Arunav Das
    Abstract: UK GDP data is published with a lag time of more than a month and it is often adjusted for prior periods. This paper contemplates breaking away from the historic GDP measure to a more dynamic method using Bank Account, Cheque and Credit Card payment transactions as possible predictors for faster and real time measure of GDP value. Historic timeseries data available from various public domain for various payment types, values, volume and nominal UK GDP was used for this analysis. Low Value Payments was selected for simple Ordinary Least Square Simple Linear Regression with mixed results around explanatory power of the model and reliability measured through residuals distribution and variance. Future research could potentially expand this work using datasets split by period of economic shocks to further test the OLS method or explore one of General Least Square method or an autoregression on GDP timeseries itself.
    Date: 2021–01

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