nep-ets New Economics Papers
on Econometric Time Series
Issue of 2020‒01‒20
seven papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Fast Forecast Reconciliation Using Linear Models By Mahsa Ashouri; Rob J Hyndman; Galit Shmueli
  2. Information Transmission Among Equity Markets: A Comparison Between ARDL and GARCH Model By Ghouse, Ghulam; Khan, Saud Ahmed; Habeeb, Kashif
  3. A dynamic conditional score model for the log correlation matrix By HAFNER Christian M.,; WANG Linqi,
  4. Business cycle dynamics after the Great Recession: An extended Markov-Switching Dynamic Factor Model By Catherine Doz; Laurent Ferrara; Pierre-Alain Pionnier
  5. An empirical study of neural networks for trend detection in time series By Alexandre Miot; Gilles Drigout
  6. Drawing Conclusions from Structural Vector Autoregressions Identified on the Basis of Sign Restrictions By Christiane Baumeister; James D. Hamilton
  7. Oil price Volatility and Exchange rate Dynamics in Nigeria: A Markov Switching Approach By Ilu, Ahmad Ibraheem

  1. By: Mahsa Ashouri; Rob J Hyndman; Galit Shmueli
    Abstract: Forecasting hierarchical or grouped time series usually involves two steps: computing base forecasts and reconciling the forecasts. Base forecasts can be computed by popular time series forecasting methods such as Exponential Smoothing (ETS) and Autoregressive Integrated Moving Average (ARIMA) models. The reconciliation step is a linear process that adjusts the base forecasts to ensure they are coherent. However using ETS or ARIMA for base forecasts can be computationally challenging when there are a large number of series to forecast, as each model must be numerically optimized for each series. We propose a linear model that avoids this computational problem and handles the forecasting and reconciliation in a single step. The proposed method is very flexible in incorporating external data, handling missing values and model selection. We illustrate our approach using two datasets: monthly Australian domestic tourism and daily Wikipedia pageviews. We compare our approach to reconciliation using ETS and ARIMA, and show that our approach is much faster while providing similar levels of forecast accuracy.
    Keywords: hierarchical forecasting, grouped forecasting, reconciling forecast, linear regression.
    JEL: C10 C14 C22
    Date: 2019
  2. By: Ghouse, Ghulam; Khan, Saud Ahmed; Habeeb, Kashif
    Abstract: This study compares the performance of autoregressive conditional heteroscedastic (ARCH) model and autoregressive distributed lag (ARDL) model in term of relationship detection. The daily, weekly, and monthly data are used from 2005 to 2019 to explore the dynamic linkages among KSE 100, S&P 500, Nasdaq 100, Dowjones 30, and DFMG indices. The results indicate that the ARDL and ARCH model have same power to detect the relationship among financial series. The results show that due high volatility in daily and weekly data the ARDL model is failed to capture ARCH effect. In case of monthly data, the performance of ARDL model is as good as GARCH model. It concluded that on monthly basis or less frequency data the ARDL model can be used as an alternative method to GARCH model for financial time series modeling.
    Keywords: Volatility, Spillover effect, GARCH, ARDL
    JEL: G10 G15 G3 G32
    Date: 2019–01–01
  3. By: HAFNER Christian M., (Université catholique de Louvain, Belgium); WANG Linqi, (Université catholique de Louvain, Belgium)
    Abstract: This paper proposes a new model for the dynamics of correlation matrices, where the dynamics are driven by the likelihood score with respect to the matrix logarithm of the correlation matrix. In analogy to the exponential GARCH model for volatility, this transformation ensures that the correlation matrices remain positive defi nite, even in high dimensions. For the conditional distribution of returns we assume a student-t copula to explain the dependence structure and univariate student-t for the marginals with potentially diff erent degrees of freedom. The separation into volatility and correlation parts allows two-step estimation, which facilitates estimation in high dimensions. We derive estimation theory for one-step and two-step estimation. In an application to a set of six asset indices including nancial and alternative assets we show that the model performs well in terms of various diagnostics and speci cation tests.
    Keywords: score, correlation, matrix logarithm, identification
    JEL: C14 C43 Z11
    Date: 2019–12–17
  4. By: Catherine Doz (Paris School of Economics); Laurent Ferrara (SKEMA Business School); Pierre-Alain Pionnier (OECD)
    Abstract: The Great Recession and the subsequent period of subdued GDP growth in most advanced economies have highlighted the need for macroeconomic forecasters to account for sudden and deep recessions, periods of higher macroeconomic volatility, and fluctuations in trend GDP growth. In this paper, we put forward an extension of the standard Markov-Switching Dynamic Factor Model (MS-DFM) by incorporating two new features: switches in volatility and time-variation in trend GDP growth. First, we show that volatility switches largely improve the detection of business cycle turning points in the low-volatility environment prevailing since the mid-1980s. It is an important result for the detection of future recessions since, according to our model, the US economy is now back to a low-volatility environment after an interruption during the Great Recession. Second, our model also captures a continuous decline in the US trend GDP growth that started a few years before the Great Recession and continued thereafter. These two extensions of the standard MS-DFM framework are supported by information criteria, marginal likelihood comparisons and improved real-time GDP forecasting performance.
    Keywords: Great Moderation, Great Recession, Macroeconomic Forecasting, Markov-Switching Dynamic Factor Model (MS-DFM), Turning-Point Detection
    JEL: C22 C51 E32 E37
    Date: 2020–01–16
  5. By: Alexandre Miot; Gilles Drigout
    Abstract: Detecting structure in noisy time series is a difficult task. One intuitive feature is the notion of trend. From theoretical hints and using simulated time series, we empirically investigate the efficiency of standard recurrent neural networks (RNNs) to detect trends. We show the overall superiority and versatility of certain standard RNNs structures over various other estimators. These RNNs could be used as basic blocks to build more complex time series trend estimators.
    Date: 2019–12
  6. By: Christiane Baumeister; James D. Hamilton
    Abstract: This paper discusses the problems associated with using information about the signs of certain magnitudes as a basis for drawing structural conclusions in vector autoregressions. We also review available tools to solve these problems. For illustration we use Dahlhaus and Vasishtha's (2019) study of the effects of a U.S. monetary contraction on capital flows to emerging markets. We explain why sign restrictions alone are not enough to allow us to answer the question and suggest alternative approaches that could be used.
    JEL: C30 E5 F2
    Date: 2020–01
  7. By: Ilu, Ahmad Ibraheem
    Abstract: The paper examines the spillover effect of crude oil price shocks to exchange rate movement of appreciation and depreciation in Nigeria using monthly data ranging from January 2004- June 2019. The study employed a two stage heteroskedastic Markov switching model. Results from preliminary investigations reveal that both Crude oil prices and exchange rate series are characterized with non- normal distribution, presence of unit root and ARCH effects. Also the BDS, Bai-Perron and Cusum Q tests are conducted to figure out the nonlinearities and structural breaks in the data. The result obtained from the estimated model indicated a positive relationship between oil prices and exchange rate in regime 1(depreciation regime) and negative relationship in regime 2 (appreciation regime). Further analysis reveals that low volatility appreciation regime is more persistent than high volatility deprecation regime with transitional probabilities 0.97 and 0.39 respectively. Consistently the expected duration of stay reveals that duration of stay in the appreciation regime is higher than in the depreciation regime at 35.92 months and 1.6 months respectively. Further, analysis of volatility spillover between oil prices and exchange rate reveals that a rise in oil prices leads to appreciation of the Naira and conversely negative shock in oil prices cause a consequent deprecation of the local currency. Certainly the findings are shall be of utmost relevance to monetary authorities.
    Keywords: Markov switching model, exchange rate, appreciation, deprecation, volatility
    JEL: F31 Q41 Q43
    Date: 2019–12–17

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