On Aggregation of Linear Dynamic Models

This paper provides a general framework for aggregating linear dynamic models by deriving the aggregate model as an optimal prediction of the aggregate variable of interest with respect to an aggregate information set generated by current and past values of available aggregate observations. The paper shows how the results in the literature can be readily obtained using the proposed forecasting approach, and provides a number of important extensions and generalisations. In particular, it does not require the assumption of independence of the micro distributed lag coefficients, and establishes that in general the long-run coefficients obtained from the optimal aggregate relation are equal to the averages of the long-run coefficients from the micro relations. Finally, the approach advocated in the paper is applied to aggregation of life-cycle decision rules under habit formation, and the implications of the heterogeneity in habit formation coefficients across individuals for the analysis of aggregate consumption are investigated.Aggregation, Heterogeneous dynamic models, Long memory, Life cycle models under habit formation

Macroeconometric Modelling with a Global Perspective

This paper provides a synthesis and further development of a global modelling approach introduced in Pesaran, Schuermann and Weiner (2004), where country specific models in the form of VARX* structures are estimated relating a vector of domestic variables to their foreign counterparts and then consistently combined to form a Global VAR (GVAR). It is shown that VARX* models can be derived as the solution to a dynamic stochastic general equilibrium (DSGE) model where over-identifying long-run theoretical relations can be tested and imposed if acceptable. Similarly, short-run over-identifying theoretical restrictions can be tested and imposed if accepted. The assumption of the weak exogeneity of the foreign variables for the long-run parameters can be tested, where foreign variables can be interpreted as proxies for global factors. Rather than using deviations from ad hoc statistical trends, the equilibrium values of the variables reflecting the long-run theory embodied in the model can be calculated

Oil Prices and the Global Economy: Is It Different This Time Around?

The recent plunge in oil prices has brought into question the generally accepted view that lower oil prices are good for the US and the global economy. In this paper, using a quarterly multi-country econometric model, we first show that a fall in oil prices tends relatively quickly to lower interest rates and inflation in most countries, and increase global real equity prices. The effects on real output are positive, although they take longer to materialize (around 4 quarters after the shock). We then re-examine the effects of low oil prices on the US economy over different sub-periods using monthly observations on real oil prices, real equity prices and real dividends. We confirm the perverse positive relationship between oil and equity prices over the period since the 2008 financial crisis highlighted in the recent literature, but show that this relationship has been unstable when considered over the longer time period of 1946.2016. In contrast, we find a stable negative relationship between oil prices and real dividends which we argue is a better proxy for economic activity (as compared to equity prices). On the supply side, the effects of lower oil prices differ widely across the different oil producers, and could be perverse initially, as some of the major oil producers try to compensate their loss of revenues by raising production. Taking demand and supply adjustments to oil price changes as a whole, we conclude that oil markets equilibrate but rather slowly, with large episodic swings between low and high oil prices.global oil market

Theory and Practice of GVAR Modeling

The Global Vector Autoregressive (GVAR) approach has proven to be a very useful approach to analyze interactions in the global macroeconomy and other data networks where both the cross-section and the time dimensions are large. This paper surveys the latest developments in the GVAR modeling, examining both the theoretical foundations of the approach and its numerous empirical applications. We provide a synthesis of existing literature and highlight areas for future research

Identifying the Effects of Sanctions on the Iranian Economy using Newspaper Coverage

This paper considers how sanctions affected the Iranian economy using a novel measure of sanctions intensity based on daily newspaper coverage. It finds sanctions to have significant effects on exchange rates, inflation, and output growth, with the Iranian rial over-reacting to sanctions, followed up with a rise in inflation and a fall in output. In absence of sanctions, Iran’s average annual growth could have been around 4-5 per cent, as compared to the 3 per cent realized. Sanctions are also found to have adverse effects on employment, labor force participation, secondary and high-school education, with such effects amplified for females

Non-nested Hypothesis Testing: An Overview

In econometric analysis, non-nested models arise naturally when rival economic theories are used to explain the same phenomenon, such as unemployment, inflation or output growth. The authors examine the problem of hypothesis testing when the models under consideration are ‘non-nested’ or belong to ‘separate’ families of distributions in the sense that none of the individual models may be obtained form the remaining, either by imposition of parameter restrictions or through a limiting process. Although the primary focus is on non-nested hypothesis testing, the authors briefly discuss the problem of model selection and the differences and similarities between the two approaches. By using the linear regression model as a convenient framework, the authors examine three broad approaches to non-nested hypothesis testing: the modified (centred) long-likelihood ratio procedure, the comprehensive models approach, and the encompassing procedure. Finally, they consider a number of practical problems which arise in the application of non-nested tests to non-linear models such as the probit and logit qualitative response models.Non-nested hypotheses, Model selection, Cox’s test, Encompassing, Stochastic simulation, Kullback-Leibler divergence measure

Pairwise tests of purchasing power parity

Given nominal exchange rates and price data on N + 1 countries indexed by i = 0,1,2,…, N, the standard procedure for testing purchasing power parity (PPP) is to apply unit root or stationarity tests to N real exchange rates all measured relative to a base country, 0, often taken to be the U.S. Such a procedure is sensitive to the choice of base country, ignores the information in all the other cross-rates and is subject to a high degree of cross-section dependence which has adverse effects on estimation and inference. In this article, we conduct a variety of unit root tests on all possible N(N + 1)/2 real rates between pairs of the N + 1 countries and estimate the proportion of the pairs that are stationary. This proportion can be consistently estimated even in the presence of cross-section dependence. We estimate this proportion using quarterly data on the real exchange rate for 50 countries over the period 1957-2001. The main substantive conclusion is that to reject the null of no adjustment to PPP requires sufficiently large disequilibria to move the real rate out of the band of inaction set by trade costs. In such cases, one can reject the null of no adjustment to PPP up to 90% of the time as compared to around 40% in the whole sample using a linear alternative and almost 60% using a nonlinear alternative

Economic and Statistical Measures of Forecast Accuracy

This paper argues in favour of a closer link between decision and forecast evaluation problems. Although the idea of using decision theory for forecast evaluation appears early in the dynamic stochastic programming literature, and has continued to be used in meteorological forecasts, it is hardly mentioned in standard academic textbooks on economic forecasting. Some of the main issues involved are illustrated in the context of a two-state, two-action decision problem as well as in a more general setting. Relationships between statistical and economic methods of forecast evaluation are discussed and useful links between Kuipers score, used as a measure of forecast accuracy in the meteorology literature, and the market timing tests used in finance, are established. An empirical application to the problem of stock market predictability is also provided, and the conditions under which such predictability could be exploited in the presence of transaction costs are discussed.Decision theory, Forecast evaluation, Probabilistsic forecasts, Economic and statistical measures of forecast accuracy, Stock market predictability