Global stability of dynamic models

A necessary and sufficient condition for global stability of dynamic models is summability to one of the long-run elasticities and cointegration. The short-run coefficients automatically satisfy the homogeneity condition. A relevant restriction has to be imposed in the parameter estimation process; otherwise, the ratios of variables appearing in the model will tend to either infinity or zero, which is economic nonsense in most cases. This conclusion is particularly important for the ECM or VEC (SVEC) models that decompose behavior into long and short-run parts.     

Uniqueness of Markov equilibrium in stochastic OLG models with nonclassical production

A large class of stochastic OLG economies with nonclassical production is shown to possess a unique Markov Equilibrium (ME) which is also the unique sequential equilibrium. Additional properties such as monotonicity, continuity, and smoothness of the ME are also discussed.     

Exchange rate regime verification: An alternative method of testing for regime changes

This work proposes a change in persistence test for identifying de facto exchange rate regime changes. The results from 25 African countries show that this approach is able to identify some regime changes not captured by existing methods.     

Nonlinearity and structural stability in the Phillips curve: Evidence from Turkey

In this paper, we investigate possible nonlinearities in the inflation–output relationship in Turkey for the 1980–2008 period. We first estimate a linear bivariate model for the inflation rate and output gap, and test for linearity of the estimated model against nonlinear alternatives. Linearity test results suggest that the relationship between the inflation rate and output gap is highly nonlinear. We estimate a bivariate time-varying smooth transition regression model, and compute dynamic effects of one variable on the other by generalized impulse response functions. Computed impulse response functions indicate that inflation–output relationship in Turkey during the analyzed period was regime dependent and varied considerably across time.     

MEAN‐PLUS‐NOISE FACTOR MODELS: AN EMPIRICAL EXPLORATION

In this paper I propose an alternative factor model whose common factor follows a mean‐plus‐noise process. While the existing methods for factor models should be valid for this model, their performance may be misleading in small samples. Because the likelihood function of this model is not tractable, I estimate this model by Bayesian methods. When applied to US macroeconomic time series, we find that the estimated mean‐plus‐noise factor is useful for predicting real personal income, industrial production and unemployment at long prediction horizons.     

Nonlinear adjustment in US bond yields: An empirical model with conditional heteroskedasticity

Starting from the work by Campbell and Shiller (Campbell, J.Y. and Shiller, R.J. (1987). Cointegration and tests of present value models. Journal of Political Economy, 95(5):1062–1088.), empirical analysis of interest rates has been conducted in the framework of cointegration. However, parts of this approach have been questioned recently, as the adjustment mechanism may not follow a simple linear rule; another line of criticism points out that stationarity of the spreads is difficult to maintain empirically.     

Macroeconomic forecasting in the Euro area: Country specific versus area-wide information

This paper compares several time series methods for short-run forecasting of Euro-wide inflation and real activity using data from 1982 to 1997. Forecasts are constructed from univariate autoregressions, vector autoregressions, single equation models that include Euro-wide and US aggregates, and large-model methods in which forecasts are based on estimates of common dynamic factors. Aggregate Euro-wide forecasts are constructed from models that utilize only aggregate Euro-wide variables and by aggregating country-specific models. The results suggest that forecasts constructed by aggregating the country-specific models are more accurate than forecasts constructed using the aggregate data.     

The vanishing role of money in the macro-economy: An empirical investigation for India

The recent de-emphasizing of the role of “money” in both theoretical macroeconomics as well as in the practical conduct of monetary policy sits uneasily with the idea that inflation is a monetary phenomenon. Empirical evidence has, however, been accumulating pointing to an important leading indicator role for money and credit aggregates with respect to long term inflationary trends. Such a role could arise from monetary aggregates furnishing a nominal anchor for inflationary expectations, from their influence on the term structure of interest rates and from their affecting transaction costs in markets. Our paper attempts to assess the informational content role of money in the Indian economy by a separation of these effects across time scales and frequency bands, using the techniques of wavelet analysis and band spectral analysis respectively. Our results indicate variability of causal relations across frequency ranges and time scales, as also occasional causal reversals.     

Long- versus medium-run identification in fractionally integrated VAR models

We state that long-run restrictions that identify structural shocks in VAR models with unit roots lose their original interpretation if the fractional integration order of the affected variable is below one. For such fractionally integrated models we consider a medium-run approach that employs restrictions on variance contributions over finite horizons. We show for alternative identification schemes that letting the horizon tend to infinity is equivalent to imposing the restriction of Blanchard and Quah (1989) introduced for the unit-root case.     

On winning forecasting competitions in economics

To explain which methods might win forecasting competitions on economic time series, we consider forecasting in an evolving economy subject to structural breaks, using mis-specified, data-based models. ‘Causal’ models need not win when facing deterministic shifts, a primary factor underlying systematic forecast failure. We derive conditional forecast biases and unconditional (asymptotic) variances to show that when the forecast evaluation sample includes sub-periods following breaks, non-causal models will outperform at short horizons. This suggests using techniques which avoid systematic forecasting errors, including improved intercept corrections. An application to a small monetary model of the UK illustrates the theory.     

Real-time forecasts of economic activity for Latin American economies

Macroeconomic policy decisions in real-time are based on the assessment of current and future economic conditions. Crucially, these assessments are made difficult by the presence of incomplete and noisy data. The problem is more acute for emerging market economies, where most economic data are released infrequently with a (sometimes substantial) lag. This paper evaluates nowcasts and forecasts of real GDP growth using five models for ten Latin American countries. The results indicate the flow of monthly data helps to improve forecast accuracy, and the dynamic factor model consistently produces more accurate nowcasts and forecasts relative to other model specifications, across most of the countries we consider.     

Nowcasting causality in mixed frequency vector autoregressive models

This paper introduces nowcasting causality as the mixed-frequency version of instantaneous causality. We analyze the relationship between nowcasting and Granger causality in a mixed-frequency VAR and illustrate its impact on the significance of high-frequency variables in mixed-frequency conditional models.     

Outline of forecast theory using generalized cost functions

The cost functions used to form forecasts in practice may be quite different than the squared costs that is often assumed in forecast theory. The impact on evaluation procedures is determined and simple properties for the derivate of the cost function of the errors are found to provide simple tests of optimality. For a very limited class of situations are forecasts based on conditional means optimal, generally, the econometricians needs to provide the whole conditional predicted distribution. Implications for multi-step forecasts and the combination of forecasts are briefly considered.     

Structural models, information and inherited restrictions

The derived structural estimates of the system βY = γZ + δU impose identifying restrictions on the reduced form estimates ex post. Some or all of the derived structural estimates are presented as evidence of the model's efficacy. In fact, the reduced form inherits a great deal of information from the structure's restrictions and hypothesized sign patterns, limiting the allowable signs for the reduced form. A method for measuring a structural model's statistical information content is proposed. Further, the paper develops a method for enumerating the allowable reduced form outcomes which can be used to falsify the proposed model independently of significant coefficients found for the structural relations.     

Inflation targeting in Latin America: Empirical analysis using GARCH models

This paper studies inflation dynamics in eight Latin American countries, some of which have adopted formal inflation targets (IT) as their monetary policy frameworks. We analyze the possible benefits associated with IT, not only in terms of inflation level and volatility, but also regarding other nonlinear characteristics of these series, such as volatility persistence or the fulfillment of the Friedman hypothesis. To describe inflation dynamics we use an unobserved components model, where each component can follow a GARCH type process. Once we estimate the model, the main findings of the empirical exercise confirm the favorable performance of IT.     

Forecasting the volatility of S&P depositary receipts using GARCH-type models under intraday range-based and return-based proxy measures

We employ four various GARCH-type models, incorporating the skewed generalized t (SGT) errors into those returns innovations exhibiting fat-tails, leptokurtosis and skewness to forecast both volatility and value-at-risk (VaR) for Standard & Poor's Depositary Receipts (SPDRs) from 2002 to 2008. Empirical results indicate that the asymmetric EGARCH model is the most preferable according to purely statistical loss functions. However, the mean mixed error criterion suggests that the EGARCH model facilitates option buyers for improving their trading position performance, while option sellers tend to favor the IGARCH/EGARCH model at shorter/longer trading horizon. For VaR calculations, although these GARCH-type models are likely to over-predict SPDRs' volatility, they are, nevertheless, capable of providing adequate VaR forecasts. Thus, a GARCH genre of model with SGT errors remains a useful technique for measuring and managing potential losses on SPDRs under a turbulent market scenario.