This paper analyzes several issues regarding fiscal sustainability and fiscal adjustment in Brazil during 1990 and searches for econometric evidence of a monetary dominant regime during some subperiods. The following statistical data are also presented in detail: macroeconomic flows and balances, industrial production, consumer price index, relative public sector prices and tariffs, minimum wage statistics, financial system loans, monetary aggregates, exports by principal commodity groups, direction of trade, detailed balance of payments, total external debt, central government operations, and so on.
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Time series on economic activity in developing countries, in particular real GDP, are reported with important lags. Therefore, it is useful to construct indicators that coincide or lead the actual direction and level of economic activity. A general methodology to construct these indicators is proposed and adapted for Argentina. Three coincident indicators could be constructed, but no reliable leading indicator could be found. From an econometric standpoint, the coincident indicators produce satisfactory point estimates of real GDP. The series that enter the indicator are broadly consistent with what many economists believe is the main source of real GDP fluctuations in Argentina: shocks to the capital account of the balance of payments. This enhances the confidence in the econometric results.
Assessing the magnitude of the output gap is critical to achieving an optimal policy mix. Unfortunately, the gap is an unobservable variable, which, in practice, has been estimated in a variety of ways, depending on the preferences of the modeler. This model selection problem leads to a substantial degree of uncertainty regarding the magnitude of the output gap, which can reduce its usefulness as a policy tool. To overcome this problem, in this paper we attempt to insert some discipline into this search by providing two metrics-inflation forecasting and business cycle dating-against which different options can be evaluated using aggregated euro-area GDP data. Our results suggest that Gali, Gertler, and Lopez-Salido's (2001) inefficiency wedge performs best in inflation forecasting and production function methodology dominates in the prediction of turning points. If, however, a unique methodology must be selected, the quadratic trend delivers the best overall results.
Growth and inflation in Turkey have been volatile over the last two decades. It would, therefore, be useful to identify indicators that anticipate economic conditions and inflation. This paper investigates the predictive performance of economic indicators for inflation and real output growth in Turkey. We find that (i) the forecasting ability of individual indicators is unstable; but that (ii) a suitable combination of these unstable forecasts yields a forecast that reliably outperforms that generated by an autoregressive model. We then propose a two-stage combination forecast obtained by taking the median of the top five performing individual forecasts. This two-stage forecast reliably improves on autoregressive benchmarks and outperforms the combination forecast based on all the individual forecasts.
External headwinds, together with domestic vulnerabilities, have loomed over the prospects of
emerging markets in recent years. We propose an empirical toolbox to quantify the impact of external
macro-financial shocks on domestic economies in parsimonious way. Our model is a Bayesian VAR
consisting of two blocks representing home and foreign factors, which is particularly useful for small
open economies. By exploiting the mixed-frequency nature of the model, we show how the toolbox
can be used for “nowcasting” the output growth. The conditional forecast results illustrate that regular
updates of external information, as well as domestic leading indicators, would significantly enhance
the accuracy of forecasts. Moreover, the analysis of variance decompositions shows that external
shocks are important drivers of the domestic business cycle.
Macroeconomic analysis in Lebanon presents a distinct challenge. For example, long
delays in the publication of GDP data mean that our analysis often relies on proxy
variables, and resembles an extended version of the “nowcasting” challenge familiar to
many central banks. Addressing this problem—and mindful of the pitfalls of extracting
information from a large number of correlated proxies—we explore some recent
techniques from the machine learning literature. We focus on two popular techniques
(Elastic Net regression and Random Forests) and provide an estimation procedure that is
intuitively familiar and well suited to the challenging features of Lebanon’s data.