nep-for New Economics Papers
on Forecasting
Issue of 2023‒01‒23
four papers chosen by
Rob J Hyndman
Monash University

  1. Estimation and forecasting using mixed-frequency DSGE models By Meyer-Gohde, Alexander; Shabalina, Ekaterina
  2. A Comparative Study On Forecasting Consumer Price Index Of India Amongst XGBoost, Theta, ARIMA, Prophet And LSTM Algorithms. By Asati, Akshita
  3. Multi-step-ahead Stock Price Prediction Using Recurrent Fuzzy Neural Network and Variational Mode Decomposition By Hamid Nasiri; Mohammad Mehdi Ebadzadeh
  4. On LASSO for High Dimensional Predictive Regression By Ziwei Mei; Zhentao Shi

  1. By: Meyer-Gohde, Alexander; Shabalina, Ekaterina
    Abstract: In this paper, we propose a new method to forecast macroeconomic variables that combines two existing approaches to mixed-frequency data in DSGE models. The first existing approach estimates the DSGE model in a quarterly frequency and uses higher frequency auxiliary data only for forecasting (see Giannone, Monti and Reichlin (2016)). The second method transforms a quarterly state space into a monthly frequency and applies, e.g., the Kalman filter when faced missing observations (see Foroni and Marcellino (2014)). Our algorithm combines the advantages of these two existing approaches, using the information from monthly auxiliary variables to inform in-between quarter DSGE estimates and forecasts. We compare our new method with the existing methods using simulated data from the textbook 3-equation New Keynesian model (see, e.g., Galí (2008)) and real-world data with the Smets and Wouters (2007) model. With the simulated data, our new method outperforms all other methods, including forecasts from the standard quarterly model. With real world data, incorporating auxiliary variables as in our method substantially decreases forecasting errors for recessions, but casting the model in a monthly frequency delivers better forecasts in normal times.
    Keywords: Mixed-frequency data, DSGE models, Forecasting, Estimation, Temporal aggregation
    JEL: E12 E17 E37 E44 C61 C68
    Date: 2022
  2. By: Asati, Akshita
    Abstract: CPI often referred to as the Consumer Price Index is a crucial and thorough method employed to estimate price changes over a fixed time interval within a country which is representative of consumption expenditure in a country‘s economy. CPI being an economic indicator engenders therefore the popular metric called inflation of the country. Thus, if we can accurately forecast the CPI, the country‘s economy can be controlled well in time and appropriate decision-making can be enabled. Hence, for a decade CPI index forecasting, especially in a developing country like India, has been always a matter of interest and research topic for economists and policy of the government. To forecast CPI, humans (decision makers) required vast domain knowledge and experience. Moreover, traditional CPI forecasting involved a multitude of human interventions and discussions for the same. However, with the recent advancements in the domain of time series forecasting techniques encompassing dependable modern machine learning, statistical as well as deep learning models there exists a potential scope in leveraging modern technology to forecast CPI of India which can technically aid towards this important decision-making step in a diverse country like India. In this paper, a comparative study is carried out exploring MAD, RMSE, and MAPE as comparison criteria amongst Machine Learning (XGBoost), Statistical Learning (Theta, ARIMA, Prophet) and Deep Learning (LSTM) algorithms. Furthermore, from this comparative univariate time series forecasting study, it can be demonstrated that technological solutions in the domain of forecasting show promising results with reasonable forecast accuracy.
    Date: 2022–12–21
  3. By: Hamid Nasiri; Mohammad Mehdi Ebadzadeh
    Abstract: Financial time series prediction, a growing research topic, has attracted considerable interest from scholars, and several approaches have been developed. Among them, decomposition-based methods have achieved promising results. Most decomposition-based methods approximate a single function, which is insufficient for obtaining accurate results. Moreover, most existing researches have concentrated on one-step-ahead forecasting that prevents stock market investors from arriving at the best decisions for the future. This study proposes two novel methods for multi-step-ahead stock price prediction based on the issues outlined. DCT-MFRFNN, a method based on discrete cosine transform (DCT) and multi-functional recurrent fuzzy neural network (MFRFNN), uses DCT to reduce fluctuations in the time series and simplify its structure and MFRFNN to predict the stock price. VMD-MFRFNN, an approach based on variational mode decomposition (VMD) and MFRFNN, brings together their advantages. VMD-MFRFNN consists of two phases. The input signal is decomposed to several IMFs using VMD in the decomposition phase. In the prediction and reconstruction phase, each of the IMFs is given to a separate MFRFNN for prediction, and predicted signals are summed to reconstruct the output. Three financial time series, including Hang Seng Index (HSI), Shanghai Stock Exchange (SSE), and Standard & Poor's 500 Index (SPX), are used for the evaluation of the proposed methods. Experimental results indicate that VMD-MFRFNN surpasses other state-of-the-art methods. VMD-MFRFNN, on average, shows 35.93%, 24.88%, and 34.59% decreases in RMSE from the second-best model for HSI, SSE, and SPX, respectively. Also, DCT-MFRFNN outperforms MFRFNN in all experiments.
    Date: 2022–12
  4. By: Ziwei Mei; Zhentao Shi
    Abstract: In a high dimensional linear predictive regression where the number of potential predictors can be larger than the sample size, we consider using LASSO, a popular L1-penalized regression method, to estimate the sparse coefficients when many unit root regressors are present. Consistency of LASSO relies on two building blocks: the deviation bound of the cross product of the regressors and the error term, and the restricted eigenvalue of the Gram matrix of the regressors. In our setting where unit root regressors are driven by temporal dependent non-Gaussian innovations, we establish original probabilistic bounds for these two building blocks. The bounds imply that the rates of convergence of LASSO are different from those in the familiar cross sectional case. In practical applications given a mixture of stationary and nonstationary predictors, asymptotic guarantee of LASSO is preserved if all predictors are scale-standardized. In an empirical example of forecasting the unemployment rate with many macroeconomic time series, strong performance is delivered by LASSO when the initial specification is guided by macroeconomic domain expertise.
    Date: 2022–12

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