nep-for New Economics Papers
on Forecasting
Issue of 2019‒11‒11
eight papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting Dollar Real Exchange Rates and the Role of Real Activity Factors By Sarthak Behera; Hyeongwoo Kim
  2. Does macroeconomics help in predicting stock markets volatility comovements? A nonlinear approach By Andrea Bucci; Giulio Palomba; Eduardo Rossi
  3. The Predictability of Real Estate Excess Returns: An Out-of-Sample Economic Value Analysis By Massimo Guidolin; Manuela Pedio; Milena Petrova
  4. CBO’s Economic Forecasting Record: 2019 Update By Congressional Budget Office
  5. A Segmented and Observable Yield Curve By Castro, C; Peña, J. F.; Rodríguez, C
  6. Maternal deaths in Zimbabwe: Is it a crime to be a woman in Zimbabwe? By Nyoni, Thabani
  7. Sri Lanka – the wonder of Asia: analyzing monthly tourist arrivals in the post-war era By Nyoni, Thabani
  8. The population question in Zimbabwe: reliable projections from the Box – Jenkins ARIMA approach By Nyoni, Thabani

  1. By: Sarthak Behera; Hyeongwoo Kim
    Abstract: We propose factor-based out of sample forecasting models for US dollar real exchange rates. We estimate latent common factors employing an array of data dimensionality reduction approaches that include the Principal Component Analysis, Partial Least Squares, and the LASSO for a large panel of 125 monthly frequency US macroeconomic time series data. We augment two benchmark models, a stationary autoregressive model and the random walk model, with estimated common factors to formulate out-of-sample forecasts of the real exchange rate. Empirical findings demonstrate that our factor augmented models outperform the benchmark models at longer horizons when factors are extracted from real activity variables excluding financial sector variables. Factors obtained from financial market variables overall play a limited role in forecasting. Our data-driven models tend to perform better than models with international factors that are motivated by exchange rate determination theories.
    Keywords: US Dollar Real Exchange Rate; Principal Component Analysis; Partial Least Squares; LASSO; Out-of-Sample Forecast
    JEL: C38 C53 C55 F31 G17
    Date: 2019–10
  2. By: Andrea Bucci (Dipartimento di Scienze Economiche e Sociali, Universita' Politecnica delle Marche); Giulio Palomba (Dipartimento di Scienze Economiche e Sociali, Universita' Politecnica delle Marche); Eduardo Rossi (Dipartimento di Scienze Economiche ed Aziendali, University of Pavia)
    Abstract: This paper addresses the question of the relevance of macroeconomic determinants in forecasting the evolution of stock markets volatilities and co-volatilities. Our approach combines the Cholesky decomposition of the covariance matrix with the use of the Vector Logistic Smooth Transition Autoregressive Model. The model includes predetermined variables and takes into account the asymmetries in volatility process. Structural breaks and nonlinearity tests are also implemented to determine the number of regimes and to identify the transition variables. The model is applied to realized volatility of stock indices of several countries in order to evaluate the role of economic variables in predicting the future evolution of conditional covariances. Our results show that the forecast accuracy of our model is significantly de m the accuracy of the forecasts obtained via other standard approaches.
    Keywords: Multivariate realized volatility, Non-linear models, Smooth transition, Forecast evaluation, Portfolio optimization
    JEL: C32 C58 G11 G17
    Date: 2019–10
  3. By: Massimo Guidolin; Manuela Pedio; Milena Petrova
    Abstract: We study the recursive, out-of-sample realized predictive performance of a rich set of predictor choices and models, spanning linear and Markov switching frameworks when the forecast target is represented by excess NCREIF and equity NAREIT returns. We find considerable pockets of predictive power, especially at the short- and intermediate horizons and for private real estate returns, both in absolute term and in comparison to a simple, but powerful, historical sample mean benchmark. We then test whether such forecasting accuracy may translate to positive, risk-adjusted out-of-sample performance in a recursive mean-variance portfolio allocation exercise, selecting weights of stocks, bonds, cash, and real estate (private or public). Consistently, we find that especially in the case of private real estate, significant improvements in realized Sharpe ratios and mean-variance utility scores are achieved from a range of strategies, exploiting predictability at intermediate horizons, especially when supported by Markov switching models. These results are robust the inclusion of transaction costs and extend to public real estate.
    Keywords: Public real estate, REITs, private real estate, predictability, mean-variance portfolios.
    Date: 2019
  4. By: Congressional Budget Office
    Abstract: CBO regularly evaluates the quality of its two-year and five-year economic forecasts by comparing them with the economy’s actual performance and with the Administration’s forecasts and the Blue Chip consensus, an average of about 50 private-sector forecasts. In general, forecasts produced by CBO, the Administration, and the Blue Chip consensus display similar error patterns over time. Because all forecasters faced the same challenges, periods in which CBO made large overestimates typically coincide with periods in which other forecasters made similarly large overestimates.
    JEL: C53 H20
    Date: 2019–10–31
  5. By: Castro, C; Peña, J. F.; Rodríguez, C
    Abstract: Following Almeida et al. (2018) we implement a segmented three factor Nelson-Siegel model for the yield curve using daily observable bond prices and short term inter-bank rates for Colombia. The flexible estimation for each segment (short, medium, and long) provides an improvement over the classical Nelson-Siegel approach in particular in terms of in-sample and out- of-sample forecasting performance. A segmented term structure model based on observable bond prices, provides a tool closer to the needs of practitioners in terms of reproducing the market quotes and allowing for independent local shocks in the different segments of the curve.
    Keywords: Term structure, Nelson-Siegel, Preferred habitat theory
    Date: 2019–11–01
  6. By: Nyoni, Thabani
    Abstract: Is it a crime to be a woman in Zimbabwe? Is it normal to have at least 6 women dying each day of pregnancy related complications? The time to deal with maternal health problems in Zimbabwe is now! This study uses annual time series data on maternal deaths and Maternal Mortality Ratio (MMR) in Zimbabwe from 1990 to 2015, to model and forecast both maternal deaths and MMR using the Box-Jenkins ARIMA technique. Diagnostic tests indicate that both M_t and MMR_t are I (2) variables. Based on minimum AIC statistics, the study presents the ARIMA (0, 2, 2) model and the ARIMA (2, 2, 0) model as the parsimonious models for forecasting maternal deaths and MMR respectively. The diagnostic tests further show that these models are stable and hence suitable for forecasting maternal deaths and MMR respectively. The selected optimal models prove beyond any reasonable doubt that in the next decade (2016 – 2025), maternal deaths and MMR in Zimbabwe are likely to increase. This is a serious warning signal on the need to give maternal health the attention it deserves. The study boasts of three policy prescriptions that are envisaged to reverse the predictions of the selected optimal models.
    Keywords: Maternal deaths; maternal mortality ratio
    JEL: H51 H75 I11 I12 I14 I18
    Date: 2019–10–18
  7. By: Nyoni, Thabani
    Abstract: Using the monthly time series data, ranging over the period June 2009 to December 2018, the study applied the generalized Box-Jenkins SARIMA approach in an attempt to model and forecast international tourist arrivals in Sri Lanka.The ADF tests indicate that the tourism series is I (1). The study identified the minimum MAPE value and subsequently presented the SARIMA (0, 1, 1)(0, 1, 1)12 model as the optimal model to forecast tourist arrivals in Sri Lanka. Analysis of the residuals of the SARIMA (0, 1, 1)(0, 1, 1)12 model indicate that the selected model is stable and acceptable for forecasting tourism demand in Sri Lanka. The forecasted international tourist arrivals over the period January 2019 to December 2020 show a generally upward trend.In order to accommodate the forecasted growing numbers of international tourists, there is need for the construction of more infrastructure facilities.
    Keywords: Forecasting; international tourism; SARIMA; Sri Lanka; tourism; tourist arrivals
    JEL: L83
    Date: 2019–10–15
  8. By: Nyoni, Thabani
    Abstract: Employing annual time series data on total population in Zimbabwe from 1960 to 2017, we model and forecast total population over the next 3 decades using the Box – Jenkins ARIMA technique. Diagnostic tests indicate that Zimbabwe annual total population is neither I (1) nor I (2) but for the sake of simplicity,we assume it is I (2). Based on the AIC, the study presents the ARIMA (2, 2, 2) model as the best model. The diagnostic tests further imply that the presented model is stable andacceptable. The results of the study indicate that total population in Zimbabwe will continue to increase in the next three decades. In order to enjoy the benefits of the Ahlburg (1998) and Becker et al (1999) prophecy, 2 policy prescriptions have been put forward.
    Keywords: ARIMA; forecasting; population growth; population policy; total population; Zimbabwe
    JEL: C53 Q56
    Date: 2019–09–10

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