Browsing by Subject "forecasting"

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  • Niu, Linlin; Xu, Xiu; Chen, Ying (2015)
    BOFIT Discussion Papers 12/2015
    We propose the use of a local autoregressive (LAR) model for adaptive estimation and forecasting of three of China’s key macroeconomic variables: GDP growth, inflation and the 7-day interbank lending rate. The approach takes into account possible structural changes in the data-generating process to select a local homogeneous interval for model estimation, and is particularly well-suited to a transition economy experiencing ongoing shifts in policy and structural adjustment. Our results indicate that the proposed method outperforms alternative models and forecast methods, especially for forecast horizons of 3 to 12 months. Our 1-quarter ahead adaptive forecasts even match the performance of the well-known CMRC Langrun survey forecast. The selected homogeneous intervals indicate gradual changes in growth of industrial production driven by constant evolution of the real economy in China, as well as abrupt changes in interestrate and inflation dynamics that capture monetary policy shifts.
  • Barker, Jamie; Herrala, Risto (2021)
    BOFIT Policy Brief 8/2021
    Since embarking on economic reform in 1991, India has experienced three decades of rapid economic development. Recently, however, there has been significant uncertainty about the growth outlook of the Indian economy in the mid-term perspective. In this paper we use standard regression techniques to project the path of the Indian economy over the next 4 years. The analysis, which abstracts from the pandemic period, mainly serves as support to forecasting the global economy. After the pandemic, GDP growth is projected to rebound this year and then slide to-wards 6 ‒ 7% in the medium term. The analysis broadly agrees with the recent projections of India’s mid-term growth rate by other institutions.
  • Amstad, Marlene; Ye, Huan; Ma, Guonan (2018)
    BOFIT Discussion Papers 11/2018
    Inflation in emerging markets is often driven by large, persistent changes in food and energy prices. Core inflation measures that neglect or under-weight volatile CPI subcomponents such as food and energy risk excluding information helpful in assessing current and future inflation trends. This paper develops an underlying inflation gauge (UIG) for China, extracting the persistent part of the common component in a broad dataset of price and non-price variables. Our proposed UIG for China avoids the excess volatility reduction that plagues traditional Chinese core inflation measures. When forecasting headline CPI, the proposed UIG outperforms traditional core inflation measures over a variety of samples.
  • Mikosch, Heiner; Solanko, Laura (2019)
    Russian Journal of Money and Finance 1
    This paper presents a pseudo real‐time out‐of‐sample forecast exercise for short‐term forecasting and nowcasting quarterly Russian GDP growth with mixed‐frequency data. We employ a large set of indicators and study their predictive power for different subperiods within the forecast evaluation period 2008–2016. Four indicators consistently figure in the list of top-performing indicators: the Rosstat key sector economic output index, the OECD composite leading indicator for Russia, household banking deposits, and money supply M2. Aside from these indicators, the top indicators in the 2008–2011 evaluation period are traditional real‐sector variables, while those in the 2012–2016 evaluation period largely comprise monetary, banking sector and financial market variables. We also compare the forecast accuracy of three different mixed‐frequency forecasting model classes (bridge equations, MIDAS models, and U-MIDAS models). Differences between the performance of model classes are generally small, but for the 2008–2011 period MIDAS models and U-MIDAS models outperform bridge equation models.
  • Fernald, John; Hsu, Eric; Spiegel, Mark M. (2015)
    BOFIT Discussion Papers 29/2015
    How reliable are China’s GDP and other data? We address this question by using trading partner exports to China as an independent measure of its economic activity from 2000–2014. We find that the information content of Chinese GDP improves markedly after 2008.We also consider a number of plausible, non-GDP indicators of economic activity that have been identified as alternative Chinese output measures. We find that activity factors based on the first principal component of sets of indicators are substantially more informative than GDP alone. The index that best matches activity in-sample uses four indicators: electricity, rail freight, an index of raw materials supply, and retail sales. Adding GDP to this group only modestly improves in-sample performance. Moreover, out of sample, a single activity factor without GDP proves the most reliable measure of economic activity.
  • Mikosch, Heiner; Solanko, Laura (2017)
    BOFIT Discussion Papers 19/2017
    Published in Russian Journal of Money and Finance, Volume 78, Number 1, March 2019
    GDP forecasters face tough choices over which leading indicators to follow and which forecasting models to use. To help resolve these issues, we examine a range of monthly indicators to forecast quarterly GDP growth in a major emerging economy, Russia. Numerous useful indicators are identified and forecast pooling of three model classes (bridge models, MIDAS models and unrestricted mixed-frequency models) are shown to outperform simple benchmark models. We further separately examine forecast accuracy of each of the three model classes. Our results show that differences in performance of model classes are generally small, but for the period covering the Great Recession unrestricted mixed-frequency models and MIDAS models clearly outperform bridge models. Notably, the sets of top-performing indicators differ for our two subsample observation periods (2008Q1–2011Q4 and 2012Q1–2016Q4). The best indicators in the first period are traditional real-sector variables, while those in the second period consist largely of monetary, banking sector and financial market variables. This finding supports the notion that highly volatile periods of recession and subsequent recovery are driven by forces other than those that prevail in more normal times. The results further suggest that the driving forces of the Russian economy have changed since the global financial crisis.
  • Funke, Michael; Loermann, Julius; Tsang, Andrew (2017)
    BOFIT Discussion Papers 15/2017
    In line with the deepening of the derivative foreign-exchange market in Hong Kong, we recover risk-neutral probability densities for future US dollar/offshore renminbi exchange rates as implied by exchange rate option prices. The risk-neutral densities (RND) approach is shown to be useful in analyzing market sentiment and risk aversion in the renminbi market. We include a forecasting exercise that confirms market participants were able to forecast the shape of the actual densities correctly for short horizons, even if their exact location could not be determined.