nep-ets New Economics Papers
on Econometric Time Series
Issue of 2018‒09‒24
ten papers chosen by
Jaqueson K. Galimberti
KOF Swiss Economic Institute

  1. Cointegrated Dynamics for A Generalized Long Memory Process By Asai, M.; Peiris, S.; McAleer, M.J.; Allen, D.E.
  2. Dynamic Spatial Autoregressive Models with Time-varying Spatial Weighting Matrices By Anna Gloria Billé; Leopoldo Catania
  3. Machine Learning for Regularized Survey Forecast Combination: Partially-Egalitarian Lasso and its Derivatives By Francis X. Diebold; Minchul Shin
  4. Comparative Study of Three Time Series Methods in Forecasting Dengue Hemorrhagic Fever Incidence in Thailand By Somsri Banditvilai; Siriluck Anansatitzin
  5. Estimating dynamic stochastic decision models: explore the generalized maximum entropy alternative By Zheng, Y.; Gohin, A.
  6. Big Data Econometrics: Now Casting and Early Estimates By Dario Buono; George Kapetanios; Massimiliano Marcellino; Gianluigi Mazzi; Fotis Papailias
  7. Modeling and Forecasting Naira / USD Exchange Rate In Nigeria: a Box - Jenkins ARIMA approach By Nyoni, Thabani
  8. Volatility Co-movement and the Great Moderation. An Empirical Analysis By Haroon Mumtaz; Konstantinos Theodoridis
  9. International Propagation of Shocks: A Dynamic Factor Model Using Survey Forecasts By Kajal Lahiri; Yongchen Zhao
  10. Global trends in interest rates By Del Negro, Marco; Giannone, Domenico; Giannoni, Marc; Tambalotti, Andrea

  1. By: Asai, M.; Peiris, S.; McAleer, M.J.; Allen, D.E.
    Abstract: Recent developments in econometric methods enable estimation and testing of general long memory process, which include the general Gegenbauer process. This paper considers the error correction model for a vector general long memory process, which encompasses the vector autoregressive fractionally-integrated moving average and general Gegenbauer process. We modify the tests for unit roots and cointegration, based on the concept of heterogeneous autoregression. The Monte Carlo simulations show that the finite sample properties of the modified tests are satisfactory, while the conventional tests suffer from size distortion. Empirical results for interest rates series for the U.S.A. and Australia indicate that: (1) the modified unit root test detected unit roots for all series, (2) after differencing, all series favour the general Gegenbauer process, (3) the modified test for cointegration found only two cointegrating vectors, and (4) the zero interest rate policy in the U.S.A. has no effect on the cointegrating vector for the two countries
    Keywords: Long Memory Processes, Gegenbauer Process, Dickey-Fuller Tests, Cointegration, Differencing, Interest Rates
    JEL: C22 C32 C51
    Date: 2018–08–01
  2. By: Anna Gloria Billé (Free University of Bolzano‐Bozen, Faculty of Economics, Italy); Leopoldo Catania (Aarhus University, Department of Economics and Business Economics and CREATES, Denmark)
    Abstract: We propose a new spatio-temporal model with time-varying spatial weighting matrices. We allow for a general parameterization of the spatial matrix, such as: (i) a function of the inverse distances among pairs of units to the power of an unknown time-varying distance decay parameter, and (ii) a negative exponential function of the time-varying parameter as in (i). The filtering procedure of the time-varying parameters is performed using the information in the score of the conditional distribution of the observables. An extensive Monte Carlo simulation study to investigate the finite sample properties of the ML estimator is reported. We analyze the association between eight European countries' perceived risk, suggesting that the economically strong countries have their perceived risk increased due to their spatial connection with the economically weaker countries, and we investigates the evolution of the spatial connection between the house prices in different areas of the UK, identifying periods when the usually adopted sparse weighting matrix is not sufficient to describe the underlying spatial process.
    Keywords: Dynamic spatial autoregressive models, Time-varying weighting matrices, Distance decay functions
    JEL: C33 C61 C58
    Date: 2018–09
  3. By: Francis X. Diebold; Minchul Shin
    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.
    JEL: C53
    Date: 2018–08
  4. By: Somsri Banditvilai (King Mongkut's Institute of Technology Ladkrabang); Siriluck Anansatitzin (King Mongkut's Institute of Technology Ladkrabang)
    Abstract: Accurate incidence forecasting of infectious disease such as dengue hemorrhagic fever is critical for early prevention and detection of outbreaks. This research presents a comparative study of three different forecasting methods based on the monthly incidence of dengue hemorrhagic fever. Holt and Winters method, Box-Jenkins method and Artificial Neural Networks were compared. The data were taken from the Bureau of Epidemiology, Department of Disease Control, Ministry of Public Health starting from January, 2003 to December, 2016. The data were divided into 2 sets. The first set from January, 2003 to December, 2015 were used for constructing and selection the forecasting models. The second set from January, 2016 to December, 2016 were used for computing the accuracy of the forecasting model. The forecasting models were chosen by considering the smallest root mean square error (RMSE) and mean absolute percentage error (MAPE) were used to measure the accuracy of the model. The results showed that Artificial Neural Networks obtained the smallest RMSE in the modeling process and the MAPE in the forecasting process was 14.05%
    Keywords: Dengue hemorrhagic fever, Time Series Forecasting, Holt-Winters method, Box-Jenkins method, Artificial Neural Networks
    JEL: C22 C45
    Date: 2018–06
  5. By: Zheng, Y.; Gohin, A.
    Abstract: This paper proposes a generalized maximum entropy (GME) approach to estimate nonlinear dynamic stochastic decision models. For these models, the state variables are latent and a solution process is required to obtain the state space representation. To our knowledge, this method has not been used to estimate dynamic stochastic general equilibrium (DSGE) or DSGE-like models. Based on the Monte Carlo experiments with simulated data, we show that the GME approach yields precise estimation for the unknown structural parameters and the structural shocks. In particular, the preference parameter which captures the risk preference and the intertemporal preference is also relatively precisely estimated. Compare to the more widely used filtering methods, the GME approach provides a similar accuracy level but much higher computational efficiency for nonlinear models. Moreover, the proposed approach shows favorable properties for small sample size data.
    Keywords: Agricultural and Food Policy
    Date: 2018–07
  6. By: Dario Buono; George Kapetanios; Massimiliano Marcellino; Gianluigi Mazzi; Fotis Papailias
    Abstract: This paper aims at providing a primer on the use of big data in macroeconomic nowcasting and early estimation. We discuss: (i) a typology of big data characteristics relevant for macroeconomic nowcasting and early estimates, (ii) methods for features extraction from unstructured big data to usable time series, (iii) econometric methods that could be used for nowcasting with big data, (iv) some empirical nowcasting results for key target variables for four EU countries, and (v) ways to evaluate nowcasts and ash estimates. We conclude by providing a set of recommendations to assess the pros and cons of the use of big data in a specific empirical nowcasting context.
    Keywords: Big Data, Nowcasting, Early Estimates, Econometric Methods
    JEL: C32 C53
    Date: 2018
  7. By: Nyoni, Thabani
    Abstract: In the financial as well as managerial decision making process, forecasting is a crucial element (Majhi et al, 2009). Most research have been made on forecasting of financial and economic variables through the help of researchers in the last decades using series of fundamental and technical approaches yielding different results (Musa et al, 2014). The theory of forecasting exchange rate has been in existence for many centuries where different models yield different forecasting results either in the sample or out of sample (Onasanya & Adeniji, 2013). A country’s exchange rate is one of the most closely monitored indicators, as fluctuations in exchange rates can have far reaching economic consequences (Ribeiro, 2016). The recent financial turmoil all over the world demonstrates the urgency of perfect information of the exchange rates (Shim, 2000). Understanding the forecasting of exchange rate behaviour is important to monetary policy (Simwaka, 2007). One of the important variables that have considerable influence on other socio – economic variables in Nigeria is the Nigerian naira / dollar exchange rate (Ismail, 2009). Owing to the critical role played by exchange rate dynamics in international trade and overall economic performance of all countries in general, the need for a good forecasting tool cannot be ruled out. In this study, we model and forecast the Naira / USD exchange rates over the period 1960 – 2017. Our diagnostic tests such as the ADF test indicate that EXC time series data is I (1). Based on the minimum AIC value, the study presents the ARIMA (1, 1, 1) model as the optimal model. The ADF test further indicates that the residuals of the ARIMA (1, 1, 1) model are stationary and thus bear the characteristics of a white noise process. It is also important to note that our forecast evaluation statistics, namely ME, RMSE, MAE, MPE, MAPE and Theil’s U absolutely show that our forecast accuracy is quite good. Our forecast actually indicates that the Naira will continue to depreciate. The main policy implication from this study is that the Central Bank of Nigeria (CBN), should devalue the Naira in order to not only restore exchange rate stability but also encourage local manufacturing and promote foreign capital inflows.
    Keywords: ARIMA, Exchange rate, Forecasting, Nigeria
    JEL: C53 E37 E47 F31 F37 O24
    Date: 2018–08–22
  8. By: Haroon Mumtaz (Queen Mary University of London); Konstantinos Theodoridis (Bank of England)
    Abstract: We propose an extended time-varying parameter Vector Autoregression that allows for an evolving relationship between the variances of the shocks. Using this model, we show that the relationship between the conditional variance of GDP growth and the long-term interest rate has become weaker over time in the US. Similarly, the co-movement between the variance of the long=term interest rate across the US and the UK declined over the 'Great Moderation' period. In contrast, the volatility of US and UK GDP growth appears to have become increasingly correlated in the recent past.
    Keywords: Vector-Autoregressions, Time-varying parameters, Stochastic volatility
    JEL: C15 C32 E32
    Date: 2016–11–02
  9. By: Kajal Lahiri (Department of Economics, University at Albany, State University of New York); Yongchen Zhao (Department of Economics, Towson University)
    Abstract: This paper studies the pathways for the propagation of shocks across G7 and major Asia-Pacific countries using multi-horizon forecasts of real GDP growth from 1995 to 2017. We show that if the forecasts are efficient in the long run, results obtained using the forecasts are comparable to those obtained from the actual outturns. We measure global business cycle connectedness and study the impact of country- specific shocks as well as common international shocks using a panel factor structural VAR model. Our results suggest strong convergence of business cycles within the group of industrialized countries and the group of developing economies during non-recessionary periods. In particular, we find increased decoupling between the industrialized and developing economies after the 2008 recession. However, the direction of shock spillovers during recessions and other crisis periods are varied, depending on the nature and origin of the episode.
    Keywords: GDP growth, business cycle connectedness, transmission of shocks, common international shocks, panel VAR model, Blue Chip Surveys.
    JEL: F41 F42 E32 C33
    Date: 2018–09
  10. By: Del Negro, Marco (Federal Reserve Bank of New York); Giannone, Domenico (Federal Reserve Bank of New York); Giannoni, Marc (Federal Reserve Bank of Dallas); Tambalotti, Andrea (Federal Reserve Bank of New York)
    Abstract: The trend in the world real interest rate for safe and liquid assets fluctuated close to 2 percent for more than a century, but has dropped significantly over the past three decades. This decline has been common among advanced economies, as trends in real interest rates across countries have converged over this period. It was driven by an increase in the convenience yield for safety and liquidity and by lower global economic growth.
    Keywords: world interest rate; convenience yield; interest rate parity; VAR with common trends
    JEL: E43 E44 F31 G12
    Date: 2018–09–01

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