Markov-Switching MIDAS Models

This paper introduces a new regression model - Markov-switching mixed data sampling (MS-MIDAS) - that incorporates regime changes in the parameters of the mixed data sampling (MIDAS) models and allows for the use of mixed-frequency data in Markov-switching models. After a discussion of estimation and inference for MS-MIDAS, and a small sample simulation based evaluation, the MS-MIDAS model is applied to the prediction of the US and UK economic activity, in terms both of quantitative forecasts of the aggregate economic activity and of the prediction of the business cycle regimes. Both simulation and empirical results indicate that MSMIDAS is a very useful specification.Business cycle, Mixed-frequency data, Non-linear models, Forecasting, Nowcasting

MONOTONICITY IMPLIES STRATEGY-PROOFNESS FOR CORRESPONDENCES

We show that Maskin monotone social choice correspondences on sufficiently rich domains satisfy a generalized strategy-proofness property, thus generalizing Muller and Satterthwaite''s (1977) theorem to correspondences. From the point of view of Nash implementation theory, the result yields a partial characterization of the restrictions entailed by Nash implementability. Alternatively, the result can be viewed as a possibility theorem on the dominant-strategy-implementability of monotone SCCs via set-valued mechanisms for agents who are completely ignorant about the finally selected outcome. It is shown by examples that stronger strategy-proofness properties fail easily.

A THEORY OF RATIONAL CHOICE UNDER COMPLETE IGNORANCE

This paper contributes to a theory of rational choice under uncertainty for decision-makers whose preferences are exhaustively described by partial orders representing ""limited information."" Specifically, we consider the limiting case of ""Complete Ignorance"" decision problems characterized by maximally incomplete preferences and important primarily as reduced forms of general decision problems under uncertainty. ""Rationality"" is conceptualized in terms of a ""Principle of Preference-Basedness,"" according to which rational choice should be isomorphic to asserted preference. The main result characterizes axiomatically a new choice-rule called ""Simultaneous Expected Utility Maximization"" which in particular satisfies a choice-functional independence and a context-dependent choice-consistency condition; it can be interpreted as the fair agreement in a bargaining game (Kalai-Smorodinsky solution) whose players correspond to the different possible states (respectively extermal priors in the general case).

Regional Inflation Dynamics within and across Euro Area and a Comparison with the US

We investigate co-movements in inflation dynamics within and across euro area regions and find it important for monetary policy to monitor regional inflation dynamics to enhance the understanding of aggregate inflation. We employ a model where regional inflation dynamics are explained by common euro area and country specific factors and an idiosyncratic regional component. We find substantial common area wide component, that can be related to common euro area monetary policy, to exchange rate and oil prices changes. The effects of the area wide factors differ across regions, however. We also find a substantial national component. Our findings do not differ substantially before and after EMU. Analysing US regional inflation developments yields similar results regarding the relevance of common US factors.regional inflation dynamics, euro area and US, common factor models

The Forecasting Performance of Real Time Estimates of the EURO Area Output Gap

This paper provides real time evidence on the usefulness of the euro area output gap as a leading indicator for inflation and growth. A genuine real-time data set for the euro area is used, including vintages of several alternative gap estimates. It turns out that, despite some difference across output gap estimates and forecast horizons, the results point clearly to a lack of any usefulness of real-time output gap estimates for inflation forecasting both in the short term (one-quarter and one-year ahead) and the medium term (two-year and three-year ahead). By contrast, we find some evidence that several output gap estimates are useful to forecast real GDP growth, particularly in the short term, and some appear also useful in the medium run. A comparison with the US yields similar conclusions.Output gap, real-time data, euro area, inflation forecasts, real GDP forecasts, data revisions.

Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP

This paper compares different ways to estimate the current state of the economy using factor models that can handle unbalanced datasets. Due to the different release lags of business cycle indicators, data unbalancedness often emerges at the end of multivariate samples, which is sometimes referred to as the "ragged edge" of the data. Using a large monthly dataset of the German economy, we compare the performance of different factor models in the presence of the ragged edge: static and dynamic principal components based on realigned data, the Expectation-Maximisation (EM) algorithm and the Kalman smoother in a state-space model context. The monthly factors are used to estimate current quarter GDP, called the "nowcast", using different versions of what we call factor-based mixed-data sampling (Factor-MIDAS) approaches. We compare all possible combinations of factor estimation methods and Factor-MIDAS projections with respect to now-cast performance. Additionally, we compare the performance of the nowcast factor models with the performance of quarterly factor models based on time-aggregated and thus balanced data, which neglect the most timely observations of business cycle indicators at the end of the sample. Our empirical findings show that the factor estimation methods don't differ much with respect to nowcasting accuracy. Concerning the projections, the most parsimonious MIDAS projection performs best overall. Finally, quarterly models are in general outperformed by the nowcast factor models that can exploit ragged-edge data.nowcasting, business cycle, large factor models, mixed-frequency data, missing values, MIDAS

A Comparison of Methods for the Construction of Composite Coincident and Leading Indexes for the UK

In this paper we provide an overview of recent developments in the methodology for the construction of composite coincident and leading indexes, and apply them to the UK. In particular, we evaluate the relative merits of factor based models and Markov switching specifications for the construction of coincident and leading indexes. For the leading indexes we also evaluate the performance of probit models and pooling. The results indicate that alternative methods produce similar coincident indexes, while there are more marked di.erences in the leading indexes.Forecasting, Business cycles, Leading indicators, Coincident indicators, Turning points

Factor-GMM Estimation with Large Sets of Possibly Weak Instruments

This paper analyses the use of factor analysis for instrumental variable estimation when the number of instruments tends to infinity. We consider cases where the unobserved factors are the optimal instruments but also cases where the factors are not necessarily the optimal instruments but can provide a summary of a large set of instruments. Further, the situation where many weak instruments exist is also considered in the context of factor models. Theoretical results, simulation experiments and empirical applications highlight the relevance and simplicity of Factor-GMM estimation.Factor models, Principal components, Instrumental variables, GMM, Weak instruments, DSGE models

Cross-sectional Averaging and Instrumental Variable Estimation with Many Weak Instruments

Instrumental variable estimation is central to econometric analysis and has justifiably been receiving considerable and consistent attention in the literature in the past. Recent developments have focused on cases where instruments are either weak, in terms of correlations with the endogenous variables, or many or both. The present paper suggests a new way to deal with many, possibly weak, instruments. Our suggestion is to cross-sectionally average the instruments and use these averages as instruments. Intuition and interesting recent work by Hahn (2002) suggest that parsimonious devices used in the construction of the final instruments, may provide effective estimation strategies. Our use of cross-sectional averaging promotes parsimony and therefore falls within the context of such arguments. We provide a theoretical analysis of this approach in terms of its consistency properties and also show, via a Monte Carlo study, that the approach can provide improved estimation compared to standard instrumental variables estimation.Instrumental variable estimation, 2SLS, Cross-sectional average

Factor-MIDAS for now- and forecasting with ragged-edge data: a model comparison for German GDP

This paper compares different ways to estimate the current state of the economy using factor models that can handle unbalanced datasets. Due to the different release lags of business cycle indicators, data unbalancedness often emerges at the end of multivariate samples, which is sometimes referred to as the 'ragged edge' of the data. Using a large monthly dataset of the German economy, we compare the performance of different factor models in the presence of the ragged edge: static and dynamic principal components based on realigned data, the Expectation-Maximisation (EM) algorithm and the Kalman smoother in a state-space model context. The monthly factors are used to estimate current quarter GDP, called the 'nowcast', using different versions of what we call factor-based mixed-data sampling (Factor-MIDAS) approaches. We compare all possible combinations of factor estimation methods and Factor-MIDAS projections with respect to nowcast performance. Additionally, we compare the performance of the nowcast factor models with the performance of quarterly factor models based on time-aggregated and thus balanced data, which neglect the most timely observations of business cycle indicators at the end of the sample. Our empirical findings show that the factor estimation methods don't differ much with respect to nowcasting accuracy. Concerning the projections, the most parsimonious MIDAS projection performs best overall. Finally, quarterly models are in general outperformed by the nowcast factor models that can exploit ragged-edge data. --MIDAS,large factor models,nowcasting,mixed-frequency data,missing values