Short-term inflation forecasting models for Turkey and a forecast combination analysis

In this paper, we produce short term forecasts for the inflation in Turkey, using a large number of econometric models. In particular, we employ univariate models, decomposition based approaches (both in frequency and time domain), a Phillips curve motivated time varying parameter model, a suite of VAR and Bayesian VAR models and dynamic factor models. Our findings suggest that the models which incorporate more economic information outperform the benchmark random walk, and the relative performance of forecasts are on average 30% better for the first two quarters ahead. We further combine our forecasts by means of several weighting schemes. Results reveal that, the forecast combination leads to a reduction in forecast error compared to most of the models, although some of the individual models perform alike in certain horizons.     

Monopolists and viscous demand

We characterize the optimal dynamic price policy of a monopolist who faces “viscous” demand for its services. Demand is viscous if it adjusts relatively slowly to price changes. We show that with the optimal policy the monopolist stops short of achieving 100% market penetration, even when all of the consumers have the same long-run willingness to pay for the service. Furthermore, for certain parameter values in the model, the price policy requires rapid oscillations of the price path.     

Time-varying U.S. inflation dynamics and the New Keynesian Phillips curve

This paper introduces a form of boundedly-rational inflation expectations in the New Keynesian Phillips curve. The representative agent is assumed to behave as an econometrician, employing a time series model for inflation that allows for both permanent and temporary shocks. The near-unity coefficient on expected inflation in the Phillips curve causes the agent's perception of a unit root in inflation to become close to self-fulfilling. In a “consistent expectations equilibrium,” the value of the Kalman gain parameter in the agent's forecast rule is pinned down using the observed autocorrelation of inflation changes. The forecast errors observed by the agent are close to white noise, making it difficult for the agent to detect a misspecification of the forecast rule. I show that this simple model of inflation expectations can generate time-varying persistence and volatility that is broadly similar to that observed in long-run U.S. data. Model-based values for expected inflation track well with movements in survey-based measures of U.S. expected inflation. In numerical simulations, the model can generate pronounced low-frequency swings in the level of inflation that are driven solely by expectational feedback, not by changes in monetary policy.     

Interest differentials and extreme support for uncovered interest rate parity

This paper addresses two findings from the empirical literature testing uncovered interest parity (UIP): (i) more favorable results when interest differentials (IDs) are large and (ii) instability across samples. Simulations demonstrate that explanations of the results using large IDs based on the hypothesis of a “zone of speculative inactivity” are inconsistent with empirical evidence. Furthermore, it is shown that, if agents forecast IDs based on long-run values, coefficient estimates will be unstable if rates of decay in IDs vary significantly and, for ex post UIP to hold, IDs must decay in absolute value. This is consistent with OECD country data.     

A complete characterization of optimal growth paths in an aggregated model with a non-concave production function

We use the Principle of Optimality in addition to the Euler equation in order to provide a characterization of optimal one-sector growth for all ranges of interest rates when the technology is not convex. Our key result is that the sequence of capital stocks is necessarily monotonic. For certain interest rates we show that the optimal path converges to a steady state only if the initial capital stock is above a critical level, otherwise it converges to zero. Finally, we demonstrate that the set of points for which the value function is differentiable is precisely the set of initial capital stocks from which there is a unique optimal path.     

Cycles and steps in British commercial property values

This paper examines cyclical behaviour in commercial property values over the period 1956 to 1996, using a structural times series (unobserved components) approach. The influence of the transition to short rent reviews during the late 1960s and the short and long-term impacts of the 1974 and 1990 property crashes are also incorporated into the analysis, via dummy variables. It is found that once these variables are taken into account a fairly regular cyclical pattern can be discerned, with a period of about 7.8 years. Furthermore, the 1974 and 1990 property crashes are shown to have had a major long-term impact on property value growth (presumably via their influence on investors' expectations).     

The measurement and decomposition of nonuse values: A critical review

We critically review the literature that claims that existence values, or nonuse values in general, are a large and measurable component of total value for certain environmental resources. Our concern is not with the question “do nonuse values exist?” For some individuals they surely do. Rather, our concern is with two interrelated questions: are there operationally meaningful theorems which might lead to the specific measurement of nonuse values, and do we in fact have a body of credible evidence which shows that nonuse values, particularly components of any nonuse value, are “large”? We find nothing in the way of operationally meaningful hypotheses which would permit the estimation of values attributable to specific motives of individuals. We find no credible basis for claims related to either the measurement of existence and other motive-related values or claims for the “large” relative size of such values. In short, we question the conventional wisdom that such values are measurable and that they are significant as a component of total value.     

Predicting foreign exchange movements using historic deviations from PPP

The ability to forecast FX rates from historical exchange rate movements is examined. An eight nation study shows a currency's deviation from the rate predicted by PPP over a four year period can predict the direction of its movement in the subsequent one to four years. We show short term exchange rate movements of freely floating currencies are large in comparison with changes in economic fundamentals and these movements accumulate to create pressure which results in a predictable pattern of reversal. The results are robust across currencies and relatively insensitive to the time parameters used in the estimation.     

US bilateral trade equations: forecasts and structural stability

In this paper actual recent US trade values are compared with out-of-sample forecasts using a standard specification for real bilateral trade value equations. Though fairly accurate on average, there are large bilateral errors. A battery of coefficient stability tests indicates significant instability more often for imports over the forecast period and more often for exports over the latter part of the estimation period. Thus, standard bilateral trade equations appear to violate the basic regression assumption of stability and may be of little use for forecasting.     

Forecasting GDP at the Regional Level with Many Predictors

In this study, we assess the accuracy of macroeconomic forecasts at the regional level using a large data set at quarterly frequency. We forecast gross domestic product (GDP) for two German states (Free State of Saxony and Baden‐Württemberg) and Eastern Germany. We overcome the problem of a ‘data‐poor environment’ at the sub‐national level by complementing various regional indicators with more than 200 national and international indicators. We calculate single‐indicator, multi‐indicator, pooled and factor forecasts in a ‘pseudo‐real‐time’ setting. Our results show that we can significantly increase forecast accuracy compared with an autoregressive benchmark model, both for short‐ and long‐term predictions. Furthermore, regional indicators play a crucial role for forecasting regional GDP.     

An Estimated DSGE Model for Business Cycle Analysis in China

A small-scale, but highly-stylized dynamic stochastic general equilibrium model is estimated by the maximum likelihood method using Chinese quarterly data. Model specifications and parameter equalities between various competing model variants are addressed by formal statistical hypothesis tests, while implications for business cycle fluctuations are evaluated via a variance decomposition experiment, second-moments matching, and some out-of-sample forecast exercises. It is highlighted that the monetary authority takes an aggressive stance to the current inflation pressure (there is a significant lagged response), while leaving less attention to changes in aggregate output. Variance decomposition reveals that large percentages of variations in real and nominal variables are explained by the highly volatile preference and potential output shock, respectively. When nominal and real frictions as well as additional shocks are included, overall our estimated model can successfully reproduce the stylized facts from actual data of Chinese business cycles and frequently can even outperform those forecasts from an unconstrained VAR.     

Forecasting UK commercial real estate cycle phases with leading indicators: a probit approach

This paper examines the significance of widely used leading indicators of the UK economy for predicting the cyclical pattern of commercial real estate performance. The analysis uses monthly capital value data for UK industrials, offices and retail from the Investment Property Databank (IPD). Prospective economic indicators are drawn from three sources namely, the series used by the US Conference Board to construct their UK leading indicator and the series deployed by two private organisations, Lombard Street Research and NTC Research, to predict UK economic activity. We first identify turning points in the capital value series adopting techniques employed in the classical business cycle literature. Probit models are then estimated using the leading economic indicators as independent variables and forecast the probability of different phases of capital values, that is, periods of declining and rising capital values. The forecast performance of the models is tested and found to be satisfactory. The predictability of lasting directional changes in property performance represents a useful tool for real estate investment decision-making.     

Not by growth alone: The role of the distribution of income in regional diversity in poverty reduction

This study examines the role of the distribution of income in determining the responsiveness of poverty to income growth and changes in income inequality using panel data of 58 developing countries for the period 1980-1998. We show that the large cross-regional variation in the capacity of income growth to reduce poverty, i.e. the income elasticity, is largely explained by differences in the initial distribution of income and present region and time specific estimates of the income and Gini elasticities of poverty. We find that the income elasticity of poverty in the mid-1990s equals −1.31 on average and ranges from −0.71 for Sub-Saharan Africa to −2.27 for the Middle East and North Africa, and that the Gini elasticity of poverty equals 0.80 on average and ranges from 0.01 in South Asia to 1.73 in Latin America. Furthermore we show that while differing income growth rates account for most of the regional diversity in poverty trends, the additional impact of differences across regions in rates of inequality change and income and inequality elasticities of poverty is almost always significant and far too large to be ignored, most notably so in Eastern Europe and Central Asia.     

Forecasting nonlinear economic time series: A simple test to accompany the nearest neighbor approach

This paper is based on a recent nonparametric forecasting approach by Sugihara, Grenfell and May (1990) to improve the short term prediction of nonlinear chaotic processes. The idea underlying their forecasting algorithm is as follows: For a nonlinear low-dimensional process, a state space reconstruction of the observed time series exhibits spatial correlation, which can be exploited to improveshort term forecasts by means of locally linear approximations. Still, the important question of evaluating the forecast perfomance is very much an open one, if the researcher is confronted with data that are additionally disturbed by stochastic noise. To account for this problem, a simple nonparametric test to accompany the algorithm is suggested here. To demonstrate its practical use, the methodology is applied to observed price series from commodity markets. It can be shown that the short term predictability of the best fitting linear model can be improved upon significantly by this method.     

Changes in health-related quality of life: a compensating income variation approach

This paper investigates the relationship between negative changes in health and life satisfaction, using a sample from the Household, Income and Labour Dynamics of Australia Survey. We use panel data models and estimate the life satisfaction impact of several different changes in health status to calculate the Compensating Income Variation (CIV) of them. Our work innovates with respect to the existing literature by using a more robust CIV method that takes account of the potential measurement error in income. Further, we produce the first set of monetary values for health losses using SF-6D utility values, one of the main measures used to estimate and value health change for economic evaluation. We show that negative changes in SF-6D are significantly associated with a reduction in life satisfaction, and the starting point matters: a drop of 0.1 in SF-6D score is associated with a decrease of 0.12 points in life satisfaction if the starting utility value is 0.8, but the effect is 100% higher if the SF-6D starting point is 0.7. More generally, we find that a 0.1 deterioration in SF-6D has a strong association with life satisfaction and that the CIV value is substantial (over US$ 120,000).     

The new MIBA model: Real-time nowcasting of French GDP using the Banque de France's monthly business survey

This paper introduces a new nowcasting model of the French quarterly real GDP growth rate (MIBA), developed at the Banque de France and based on monthly business surveys. The model is designed to target initial announcements of GDP in a mixed-frequency framework. The selected equations for each forecast horizon are consistent with the time frame of real-time nowcasting exercises: the first one includes mainly information on the expected evolution of economic activity, while the second and third equations rely more on information on observed business outcomes. The predictive accuracy of the model increases over the forecast horizon, consistent with the gradual increase in available information. Furthermore, the model outperforms a wide set of alternatives, such as its previous version and MIDAS regressions, although not a specification including also hard data. Further research should evaluate the performance of the MIBA model with respect to promising alternative approaches for nowcasting GDP (e.g. mixed-frequency factor models with targeted predictors), and consider forecast combinations and density forecasts.     

Understanding and forecasting aggregate and disaggregate price dynamics

There are doubts regarding the empirical benefits of forecast aggregation. Theoretical research clearly supports forecast aggregation but conflicting results exist in the empirical literature. We search the literature for empirical regularities. One important issue often cited is estimation error and papers which are unsupportive of forecast aggregation often have short spans of data. A second empirical regularity is that researchers frequently use a relatively small number of disaggregates. Our work finds that the greatest benefits to aggregation are realised when a large number of disaggregates are used. This is a natural consequence of the theoretical results. A second critical issue in forecast aggregation is model selection. We suggest a simple guide to model choice based on the empirical properties of the data. In this regard, the extent of comovements between the constituent series determines model choice.     

Business Cycles in an Estimated DSGE Model of China

A small-scale New-Keynesian dynamic stochastic general equilibrium model is estimated by maximum likelihood method using quarterly data of China. Model specifications and parameter equalities between various competing model variants are addressed by formal statistical hypothesis tests, while implications for business cycle fluctuations are evaluated via a variance decomposition experiment, second-moments matching, and some out-of-sample forecast exercises. It is highlighted that both forward and backward components are important for the dynamics of output, inflation and real balances. The monetary authority will take a sufficient aggressive stance, with a significant lagged response, to the current inflation pressure, while leaving less attention to changes in aggregate output. Variance decomposition reveals that large percentages of variations in real and nominal variables are explained by the highly volatile preference shock and potential output shock, respectively. When nominal and real frictions as well as additional shocks are included, our estimated model overall can successfully reproduce the stylized facts of business cycles in the actual data of China and even frequently outperform those forecasts from an unconstrained VAR.     

The LeChatelier principle: the long and the short of it

Summary. Using ordinary calculus techniques, we investigate the conditions under which LeChatelier effects are signable for finite changes in parameter values. We show, for example, that the short run demand for a factor is always less responsive to price changes than the long run demand, provided that the factor of production and the fixed factor do not switch from being substitutes to being complements (or vice versa) over the relevant range of the price change. The absence of a sign change in the complementarity/substitutability relation holds under conditions that are considerably more general than supermodularity of the production function.     

Using Meta-Analysis and GIS for Value Transfer and Scaling Up: Valuing Climate Change Induced Losses of European Wetlands

There is growing policy and academic interest in transferring ecosystem service values from existing valuation studies to other ecosystem sites at a large geographic scale. Despite the evident policy demand for this combined transfer and “scaling up” of values, an approach to value transfer that addresses the challenges inherent in assessing ecosystem changes at a national or regional level is not available. This paper proposes a methodology for scaling up ecosystem service values to estimate the welfare effects of ecosystem change at this larger geographical scale. The methodology is illustrated by applying it to value the impact of climate change on European wetlands for the period 2000–2050. The proposed methodology makes use of meta-analysis to produce a value function. The parameters of the value function include spatial variables on wetland size and abundance, GDP per capita, and population. A geographic information system is used to construct a database of wetland sites in the case study region with information on these spatial variables. Site-specific ecosystem service values are subsequently estimated using the meta-analytic value function. The proposed method is shown to enable the adjustment of transferred values to reflect variation in important spatial variables and to account for changes in the stock of ecosystems.