nep-for New Economics Papers
on Forecasting
Issue of 2018‒11‒19
seven papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting house prices in Italy By Simone Emiliozzi; Elisa Guglielminetti; Michele Loberto
  2. On the Comparison of Interval Forecasts By Ross Askanazi; Francis X. Diebold; Frank Schorfheide; Minchul Shin
  3. Forecasting Financial Stress Indices in Korea: A Factor Model Approach By Kim, Hyeongwoo; Shi, Wen; Kim, Hyun Hak
  4. Uncertain Kingdom: nowcasting GDP and its revisions By Anesti, Nikoleta; Galvão, Ana; Miranda-Agrippino, Silvia
  5. Forecasting Financial Vulnerability in the US: A Factor Model Approach By Kim, Hyeongwoo; Shi, Wen
  6. Machine Learning for Regularized Survey Forecast Combination: Partially Egalitarian Lasso and its Derivatives By Francis X. Diebold; Minchul Shin
  7. Inflation Decomposition Model: Application to Macedonian inflation By Danica Unevska-Andonova

  1. By: Simone Emiliozzi (Bank of Italy); Elisa Guglielminetti (Bank of Italy); Michele Loberto (Bank of Italy)
    Abstract: Forecasting house prices is a difficult task given the strong relationship between real estate markets, economic activity and financial stability, but it is an important one. This paper evaluates the out-of-sample forecasting performance of various models of house prices in a quasi-real time setting. Focusing on Italy, we consider two structural models (using simultaneous equations) and a Bayesian VAR and compute both conditional and unconditional forecasts. We find that the models perform better than a simple autoregressive benchmark; however, the relative forecast accuracy depends on the forecast horizon and also changes over time. For the full sample period the simultaneous equation model, which takes into account credit supply restrictions and real estate taxation, shows the best performance measured in terms of root mean squared forecasting error (RMSFE). In the first part of the sample (2005-2010), medium-term forecasts of house prices greatly benefit from conditioning on the evolution of households’ disposable income, whereas from 2010 onwards the path of the stock of mortgages becomes important.
    Keywords: house prices, forecasting, structural model, BVAR
    JEL: C32 C53 E37 R39
    Date: 2018–10
  2. By: Ross Askanazi (Cornerstone Research); Francis X. Diebold (Department of Economics, University of Pennsylvania); Frank Schorfheide (Department of Economics, University of Pennsylvania); Minchul Shin (Department of Economics, University of Illinois)
    Abstract: We explore interval forecast comparison when the nominal conï¬ dence level is speciï¬ ed, but the quantiles on which intervals are based are not speciï¬ ed. It turns out that the problem is difficult, and perhaps unsolvable. We ï¬ rst consider a situation where intervals meet the Christoffersen conditions (in particular, where they are correctly calibrated), in which case the common prescription, which we rationalize and explore, is to prefer the interval of shortest length. We then allow for mis-calibrated intervals, in which case there is a calibration-length tradeoff. We propose two natural conditions that interval forecast loss functions should meet in such environments, and we show that a variety of popular approaches to interval forecast comparison fail them. Our negative results strengthen the case for abandoning interval forecasts in favor of density forecasts: Density forecasts not only provide richer information, but also can be readily compared using known proper scoring rules like the log predictive score, whereas interval forecasts cannot.
    Keywords: Forecast accuracy, forecast evaluation, prediction
    JEL: C53
    Date: 2018–08–02
  3. By: Kim, Hyeongwoo; Shi, Wen; Kim, Hyun Hak
    Abstract: We propose factor-based out-of-sample forecast models for Korea's financial stress index and its 4 sub-indices that are developed by the Bank of Korea. We extract latent common factors by employing the method of the principal components for a panel of 198 monthly frequency macroeconomic data after differencing them. We augment an autoregressive-type model of the financial stress index with estimated common factors to formulate out-of-sample forecasts of the index. Our models overall outperform both the stationary and the nonstationary benchmark models in forecasting the financial stress indices for up to 12-month forecast horizons. The first common factor that represents not only financial market but also real activity variables seems to play a dominantly important role in predicting the vulnerability in the financial markets in Korea.
    Keywords: Financial Stress Index; Principal Component Analysis; PANIC; In-Sample Fit; Out-of-Sample Forecast; Diebold-Mariano-West Statistic
    JEL: E44 E47 G01 G17
    Date: 2018–10
  4. By: Anesti, Nikoleta (Bank of England); Galvão, Ana (Warwick Business School); Miranda-Agrippino, Silvia (Bank of England)
    Abstract: We design a new econometric framework to nowcast macroeconomic data subject to revisions, and use it to predict UK GDP growth in real-time. To this end, we assemble a novel dataset of monthly and quarterly indicators featuring over ten years of real-time data vintages. In the Release-Augmented DFM (or RA-DFM) successive monthly estimates of GDP growth for the same quarter are treated as correlated observables in a Dynamic Factor Model (DFM) that also includes a large number of mixed-frequency predictors. The framework allows for a simple characterisation of the stochastic process for the revisions as a function of the observables, and permits a detailed assessment of the contribution of the data flow in informing (i) forecasts of quarterly GDP growth; (ii) the evolution of forecast uncertainty; and (iii) forecasts of revisions to early released GDP data. We find that the RA-DFM predictions have information about the latest GDP releases above and beyond that contained in the statistical office earlier estimates; predictive intervals are well-calibrated; and that real-time estimates of UK GDP growth are commensurate with those of professional forecasters. Data on production and labour markets, subject to large publication delays, account for most of the forecastability of the revisions.
    Keywords: Nowcasting; data revisions; dynamic factor model
    JEL: C51 C53
    Date: 2018–11–02
  5. By: Kim, Hyeongwoo; Shi, Wen
    Abstract: This paper presents a factor-based forecasting model for the financial market vulnerability, measured by changes in the Cleveland Financial Stress Index (CFSI). We estimate latent common factors via the method of the principal components from 170 monthly frequency macroeconomic data in order to out-of-sample forecast the CFSI. Our factor models outperform both the random walk and the autoregressive benchmark models in out-of-sample predictability at least for the short-term forecast horizons, which is a desirable feature since financial crises often come to a surprise realization. Interestingly, the first common factor, which plays a key role in predicting the financial vulnerability index, seems to be more closely related with real activity variables rather than nominal variables. We also present a binary choice version factor model that estimates the probability of the high stress regime successfully.
    Keywords: Financial Stress Index; Method of the Principal Component; Out-of-Sample Forecast; Ratio of Root Mean Square Prediction Error; Diebold-Mariano-West Statistic; Ordered Probit Model
    JEL: E44 E47 G01 G17
    Date: 2018–10
  6. By: Francis X. Diebold (Department of Economics, University of Pennsylvania); Minchul Shin (Department of Economics, University of Illinois)
    Abstract: Despite the clear success of forecast combination in many economic environments, several important issues remain incompletely resolved. The issues relate to selection of the set of forecasts to combine, and whether some form of additional regularization (e.g., shrinkage) is desirable. Against this background, and also considering the frequently-found good performance of simple-average combinations, we propose a LASSO-based procedure that sets some combining weights to zero and shrinks the survivors toward equality (“partially-egalitarian LASSO†). Ex-post analysis reveals that the optimal solution has a very simple form: The vast majority of forecasters should be discarded, and the remainder should be averaged. We therefore propose and explore direct subset-averaging procedures motivated by the structure of partially-egalitarian LASSO and the lessons learned, which, unlike LASSO, do not require choice of a tuning parameter. Intriguingly, in an application to the European Central Bank Survey of Professional Forecasters, our procedures outperform simple average and median forecasts – indeed they perform approximately as well as the ex-post best forecaster.
    Keywords: Forecast combination, forecast surveys, shrinkage, model selection, LASSO, regularization
    JEL: C53
    Date: 2018–08–17
  7. By: Danica Unevska-Andonova (National Bank of Republic of Macedonia)
    Abstract: The purpose of the paper is to introduce the framework for decomposing the forecast of headline inflation, obtained by macroeconomic model of NBRM for monetary policy analysis and medium term projections (MAKPAM), into its components: food, energy and core inflation. The model for inflation decomposition is a small structural model, set up in state space framework. Kalman filter procedure is applied to filter the future paths of CPI components, given projected headline inflation obtained by MAKPAM model and exogenous determinants, such as output gap, world commodity prices, and foreign effective inflation. The results of the model’s forecasting performance suggest that this model can be a useful analytical tool in the process of inflation forecast, with relatively good fit of equations for food and domestic oil prices. This model serves as satellite model to MAKPAM and enriches the set of tools for forecasting and monetary policy analysis in NBRM. In this paper we highlight its most important equations, results and model performances.
    Keywords: Inflation, forecasting, Macedonia
    JEL: C53 E31 E37
    Date: 2018

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