آیا آمار مالی سه ماهه دولت باید برای نظارت مالی در اروپا مورد استفاده قرار گیرد؟
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|12009||2010||14 صفحه PDF||سفارش دهید|
نسخه انگلیسی مقاله همین الان قابل دانلود است.
هزینه ترجمه مقاله بر اساس تعداد کلمات مقاله انگلیسی محاسبه می شود.
این مقاله تقریباً شامل 8252 کلمه می باشد.
هزینه ترجمه مقاله توسط مترجمان با تجربه، طبق جدول زیر محاسبه می شود:
- تولید محتوا با مقالات ISI برای سایت یا وبلاگ شما
- تولید محتوا با مقالات ISI برای کتاب شما
- تولید محتوا با مقالات ISI برای نشریه یا رسانه شما
پیشنهاد می کنیم کیفیت محتوای سایت خود را با استفاده از منابع علمی، افزایش دهید.
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : International Journal of Forecasting, Volume 26, Issue 4, October–December 2010, Pages 794–807
We use a newly available dataset of euro area quarterly national accounts fiscal data and construct multivariate state space mixed-frequencies models for the government deficit, revenue and expenditure in order to assess its information content and potential use for fiscal forecasting and monitoring purposes. The models are estimated using annual and quarterly national accounts fiscal data, but also incorporate monthly information taken from the cash accounts of the governments. The results show the usefulness of our approach for real-time fiscal policy surveillance in Europe, given the current policy framework in which the relevant official figures are expressed in annual terms.
The issue addressed in this paper is how to obtain timely estimates of annual government deficits. The operation of the fiscal policy coordination device in the European Union (EU), i.e. the Stability and Growth Pact, is directly related to an annual multilateral assessment of EU countries’ latest budgetary figures and fiscal plans, including targets and projections for subsequent years. The relevant official figures used for this assessment are expressed in annual terms, using the European System of Integrated Economic Accounts (ESA95) as a conceptual reference method. The first estimates of annual figures for year t−1t−1 are made available by the spring of year tt, in line with standard National Accounts compilation practices, while the second estimate is due by the autumn of year tt, and is sometimes subject to further revisions in subsequent years (see Bier, Mink, & Rodríguez-Vives, 2004 and Gordo & Nogueira Martins, 2007). The fact that the multilateral EU system is based solely on annual fiscal data might limit its ability to detect departures from fiscal rules early, and hinder private sector agents and the monetary authority in internalizing fiscal policy shocks in a timely fashion. Thus, a number of EU regulations have developed the mandate to compile quarterly ESA95 fiscal data.1 Following these regulations, Eurostat started to disseminate quarterly budget balance figures for the EU aggregates and for most member countries in April 2006, while the European Central Bank (ECB) has been publishing euro area aggregates since August 2004 (see ECB, 2004). Quarterly general government accounts present some shortcomings in terms of coverage of revenue and expenditure items, sample size (the period starting 1999Q1, with some exceptions), and timeliness (with at least 90 days delay). In addition, there is still some heterogeneity as regards country availability. For example, Germany and France only publish quarterly figures for the four quarters of a given year, in conjunction with the release calendar of the annual accounts of that year.2 Nevertheless, the euro area aggregate is published in a timely manner, following a regular quarterly publication calendar. Even considering all these caveats, it is fair to say that the ESA95 quarterly accounts for the general government, as currently disseminated by Eurostat, represent an important improvement in the matter of timeliness with respect to using only annual ESA95 accounts. Thus, the aim of this paper is to analyze the extent to which using this new set of information might help in improving the monitoring and forecasting of annual ESA95 figures within the current year. Fig. 1 (left panel) shows the annual ESA95 euro area government deficit path over the past 20 years, together with the four-quarter moving sum of quarterly ESA95 figures for the period 1999Q4–2007Q4 (the period for which the quarterly figures are available). The reduction in the sampling interval from 1999 onwards is evident by simple visual inspection.Fig. 1 (right panel, solid line) also displays another measure of the euro area fiscal deficit, based on monthly cash accounts of governments,3 that traces the profile of annual/quarterly ESA95 figures over the same period of time. Monthly and quarterly revenue and expenditure cash data from central government and other sub-sectors of the general government have recently been shown to contain valuable information for monitoring and forecasting euro area annual ESA95 fiscal deficits (Onorante et al., in press and Pérez, 2007). They are available with a delay of one to three months, and typically cover long periods of the recent history (i.e., it is possible to find deficit series going back to the 1980s or even the 1970s). We add this set of information to the analysis for three reasons. Firstly, to overcome the short sample problem associated with quarterly ESA95 figures (backcasting). Secondly, to assess its potential use for nowcasting quarterly ESA95 figures. Finally, to assess whether including quarterly ESA95 figures would improve the estimation of annual deficit figures within the year, compared to an approach based solely on intra-annual monthly cash data. An optimal way to use these data is to build a single model that relates data at all frequencies. In this paper we construct multivariate state space mixed-frequencies models for the euro area aggregate fiscal deficit, revenue and expenditure, based on annual and quarterly ESA95 figures, and on monthly information taken from the cash accounts of governments. Our approach is closely related to that of Harvey and Chung (2000), Moauro and Savio (2005), and Proietti and Moauro (2006).4 These papers use a temporal aggregation method that relies on the information contained in related indicators observed at the desired higher frequency. The statistical treatment of structural time series models is based on the state space form and the Kalman Filter (see Harvey, 1989). In our case, this approach allows the estimation of a monthly model using annual, quarterly and monthly observations, and permits changes over time arising from an increase in the sample size. We move beyond the relevant literature in the following respects: (i) we focus on forecasting, while the motivation of most studies is the estimation of unobserved measurements of certain variables based on measured data, with little interest in the forecasting performance5; (ii) we make extensive use of a three frequency setup: annual, quarterly and monthly data; and (iii) we always model and estimate models using non-seasonally-adjusted data. The analysis focuses on forecasting the quarterly ESA95 general government deficit, total revenue and total expenditure for the euro area aggregate, as published in the Monthly Bulletin of the European Central Bank (based on Eurostat data). The focus on the euro area aggregate stems from its availability in real time (following a quarterly calendar of releases), while in some cases individual country data is only available with a much longer delay, most noticeably in the above-mentioned cases of Germany and France. Monthly public accounts data for Belgium, Germany, Greece, Spain, France, Italy, the Netherlands, Ireland, Austria and Finland for the period 1984–2007 are used as well. The results unambiguously show the potential gains that would be derived from using quarterly ESA95 figures for fiscal surveillance in Europe. With a focus on forecasting the annual government deficit, we also evaluate the comparative behaviours of approaches that directly forecast the government deficit versus approaches that forecast government revenues and government expenditures, and then compute forecasts for the deficit as a residual variable (revenue minus expenditure). A valuable by-product of our analysis is that we provide interpolated monthly series of annual fiscal variables based only on intra-annual fiscal information. This is a relevant point for further research devoted to the integration of intra-annual fiscal variables in more general macroeconomic studies. A clear advantage of using only intra-annual fiscal data to interpolate annual fiscal variables versus an approach based on quarterly GDP and other macroeconomic variables lies in the circularity that the latter approach might induce in cases where the so-interpolated fiscal series were used with GDP or other macro variables to assess the intra-annual impact of fiscal policies (where the GDP is used to generate intra-annual dynamics in fiscal variables, and then the so-generated fiscal variables are used in turn to assess the intra-annual impact of fiscal policies). 6 An approach like the one presented in our paper, which is based solely on intra-annual fiscal information, is free from this caveat. The paper is organized as follows. Section 2 presents the data employed and the timing convention used for the main empirical exercise. Section 3 presents our methodological approach. Section 4 describes a thorough forecasting exercise for testing alternative models. Section 5 gives some additional empirical results, especially as regards the value added by the use of quarterly figures rather than an approach based solely on intra-annual monthly information. Section 6 concludes.
نتیجه گیری انگلیسی
In this paper we construct multivariate state space mixed-frequencies models for the euro area aggregate fiscal deficit, total revenue and total expenditure, based on annual and quarterly ESA95 figures and monthly cash government country data. The three frequencies structure of the models allows us to highlight the advantages of using quarterly ESA95 figures as a source of intra-year information, and also to explore their properties in conjunction with monthly cash fiscal figures. In particular, we can summarize the main points highlighted in the paper as follows: (i) the mixed-frequency models present a reasonable forecasting record compared to simple alternatives (annual random walk, quarterly random walk); (ii) the three frequency structure of the models allows us to now-cast quarterly figures using monthly fiscal figures, and in turn to use quarterly and monthly figures to now-cast annual fiscal variables; (iii) approaches that forecast the government deficit directly and approaches that forecast government revenues and expenditures and then compute forecasts for the deficit as a residual variable are not significantly different; (iv) we provide models to interpolate (back-cast) annual fiscal variables by means of only intra-annual fiscal information, with quarterly figures playing a key role; (v) quarterly ESA95 data contain valuable information that is not fully covered by the available monthly cash data; and (vi) intra-annual fiscal information might provide valuable insights for turning point detection in real-time. These results confirm the unambiguous potential gains that would be derived from using quarterly ESA95 figures for real-time fiscal surveillance in Europe.