Results 1  10
of
46
Do Macro variables, asset markets, or surveys forecast ination better?Journal of Monetary
 Economics
, 2007
"... NOTE: Staff working papers in the Finance and Economics Discussion Series (FEDS) are preliminary materials circulated to stimulate discussion and critical comment. The analysis and conclusions set forth are those of the authors and do not indicate concurrence by other members of the research staff ..."
Abstract

Cited by 159 (8 self)
 Add to MetaCart
(Show Context)
NOTE: Staff working papers in the Finance and Economics Discussion Series (FEDS) are preliminary materials circulated to stimulate discussion and critical comment. The analysis and conclusions set forth are those of the authors and do not indicate concurrence by other members of the research staff or the Board of Governors. References in publications to the Finance and Economics Discussion Series (other than acknowledgement) should be cleared with the author(s) to protect the tentative character of these papers.
Forecasting economic time series using targeted predictors
 Journal of Econometrics
, 2008
"... This paper studies two refinements to the method of factor forecasting. First, we consider the method of quadratic principal components that allows the link function between the predictors and the factors to be nonlinear. Second, the factors used in the forecasting equation are estimated in a way t ..."
Abstract

Cited by 58 (1 self)
 Add to MetaCart
This paper studies two refinements to the method of factor forecasting. First, we consider the method of quadratic principal components that allows the link function between the predictors and the factors to be nonlinear. Second, the factors used in the forecasting equation are estimated in a way to take into account that the goal is to forecast a specific series. This is accomplished by applying the method of principal components to ‘targeted predictors ’ selected using hard and soft thresholding rules. Our three main findings can be summarized as follows. First, we find improvements at all forecast horizons over the current diffusion index forecasts by estimating the factors using fewer but informative predictors. Allowing for nonlinearity often leads to additional gains. Second, forecasting the volatile one month ahead inflation warrants a high degree of targeting to screen out the noisy predictors. A handful of variables, notably relating to housing starts and interest rates, are found to have systematic predictive power for inflation at all horizons. Third, the targeted predictors selected by both soft and hard thresholding changes with the forecast horizon and the sample period. Holding the set of predictors fixed as is the current practice of factor forecasting is unnecessarily restrictive.
To combine forecasts or to combine information
 Econometric Reviews
"... When the objective is to forecast a variable of interest but with many explanatory variables available, one could possibly improve the forecast by carefully integrating them. There are generally two directions one could proceed: combination of forecasts (CF) or combination of information (CI). CF co ..."
Abstract

Cited by 12 (2 self)
 Add to MetaCart
When the objective is to forecast a variable of interest but with many explanatory variables available, one could possibly improve the forecast by carefully integrating them. There are generally two directions one could proceed: combination of forecasts (CF) or combination of information (CI). CF combines forecasts generated from simple models each incorporating a part of the whole information set, while CI brings the entire information set into one super model to generate an ultimate forecast. Through analysis and simulation, we show the relative merits of each, particularly the circumstances where forecast by CF can be superior to forecast by CI, when CI model is correctly specified and when it is misspecified, and shed some light on the success of equally weighted CF. In our empirical application on prediction of monthly, quarterly, and annual equity premium, we compare the CF forecasts (with various weighting schemes) to CI forecasts (with methodology mitigating the problem of parameter proliferation such as principal component approach). We find that CF with (close to) equal weights is generally the best and dominates all CI schemes, while also performing substantially better than the historical mean.
Diverse Beliefs and Time Variability of Risk Premia by
, 2007
"... Abstract: Why do risk premia vary over time? We examine this problem theoretically and empirically by studying the effect of market belief on risk premia. Individual belief is taken as a fundamental, primitive, state variable. Market belief is observable, it is central to the empirical evaluation an ..."
Abstract

Cited by 9 (2 self)
 Add to MetaCart
Abstract: Why do risk premia vary over time? We examine this problem theoretically and empirically by studying the effect of market belief on risk premia. Individual belief is taken as a fundamental, primitive, state variable. Market belief is observable, it is central to the empirical evaluation and we show how to measure it. The asset pricing model we use is familiar from the noisy REE literature but we adapt it to an economy with diverse beliefs. We derive the equilibrium asset pricing and the implied risk premium. Our approach permits a closed form solution of prices hence we trace the exact effect of market belief on the time variability of asset prices and risk premia. We test empirically the theoretical conclusions. Our main result is that, above the effect of business cycles on risk premia, fluctuations in market belief have significant independent effect on the time variability of risk premia. We study the premia on long positions in Federal Funds Futures, 3month and 6month Treasury Bills. The annual mean risk premium on holding such assets for 112 months is about 4060 basis points and we find that, on average, the component of market belief in the risk premium exceeds 50 % of the mean. Since time variability of market belief is large, this component frequently exceeds 50 % of the mean premium. This component is larger the shorter is the holding period of an asset and it dominates the premium for very short holding returns of less than 2 months. As to the structure of the premium we show that when the market holds abnormally favorable belief about the future payoff of an asset the market views the long position as less risky
MODELLING HIGHDIMENSIONAL TIME SERIES BY GENERALIZED LINEAR DYNAMIC FACTOR MODELS: AN INTRODUCTORY SURVEY
"... Abstract. Factor models are used to condense high dimensional data consisting of many variables into a much smaller number of factors. Here we present an introductory survey to factor models for time series, where the factors represent the comovement between the single time series. Principal compon ..."
Abstract

Cited by 9 (1 self)
 Add to MetaCart
(Show Context)
Abstract. Factor models are used to condense high dimensional data consisting of many variables into a much smaller number of factors. Here we present an introductory survey to factor models for time series, where the factors represent the comovement between the single time series. Principal component analysis, linear dynamic factor models with idiosyncratic noise and generalized linear dynamic factor models are introduced and structural properties, such as identifiability, as well as estimation are discussed. 1. Introduction. Factor
Rational diverse beliefs and economic volatility’. Prepared for the Handbook of Finance Series Volume Entitled “Handbook of Financial Markets: Dynamics and Evolution
, 2008
"... This work is distributed as a Discussion Paper by the ..."
Abstract

Cited by 6 (3 self)
 Add to MetaCart
This work is distributed as a Discussion Paper by the
Seeing Inside the Black Box: Using Diffusion Index Methodology to Construct Factor Proxies in Large Scale Macroeconomic Time Series Environments
, 2007
"... In economics, common factors are often assumed to underlie the comovements of a set of macroeconomic variables. For this reason, many authors have used estimated factors in the construction of prediction models. In this paper, we begin by surveying the extant literature on diffusion indexes. We the ..."
Abstract

Cited by 5 (5 self)
 Add to MetaCart
In economics, common factors are often assumed to underlie the comovements of a set of macroeconomic variables. For this reason, many authors have used estimated factors in the construction of prediction models. In this paper, we begin by surveying the extant literature on diffusion indexes. We then outline a number of approaches to the selection of factor proxies (observed variables that proxy unobserved estimated factors) using the statistics developed in Bai and Ng (2006a,b). Our approach to factor proxy selection is examined via a small Monte Carlo experiment, where evidence supporting our proposed methodology is presented, and via a large set of prediction experiments using the panel dataset of Stock and Watson (2005). One of our main empirical findings is that our “smoothed ” approaches to factor proxy selection appear to yield predictions that are often superior not only to a benchmark factor model, but also to simple linear time series models which are generally difficult to beat in forecasting competitions. In some sense, by using our approach to predictive factor proxy selection, one is able to open up the “black box ” often associated with factor analysis, and to identify actual variables that can serve as primitive building blocks for (prediction) models of a host of macroeconomic variables, and that can also serve as policy instruments, for example. Our findings suggest that important observable variables include various S&P500 variables, including stock price indices and dividend series; a 1year Treasury bond rate; various housing activity variables; industrial
Some Variables are More Worthy than Others: New Diffusion Index Evidence on the Monitoring of Key Economic
 Indicators, Applied Financial Economics
, 2011
"... Central banks regularly monitor select financial and macroeconomic variables in order to obtain early indication of the impact of monetary policies. This practice is discussed on the Federal Reserve Bank of New York website, for example, where one particular set of macroeconomic “indicators ” is giv ..."
Abstract

Cited by 3 (3 self)
 Add to MetaCart
Central banks regularly monitor select financial and macroeconomic variables in order to obtain early indication of the impact of monetary policies. This practice is discussed on the Federal Reserve Bank of New York website, for example, where one particular set of macroeconomic “indicators ” is given. In this paper, we define a particular set of “indicators ” that is chosen to be representative of the typical sort of variable used in practice by both policysetters and economic forecasters. As a measure of the “adequacy ” of the “indicators”, we compare their predictive content with that of a group of observable factor proxies selected from amongst 132 macroeconomic and financial time series, using the diffusion index methodology of Stock and Watson (2002a,b) and the factor proxy methodology of Bai and Ng (2006a,b) and Armah and Swanson (2010). The variables that we predict are output growth and inflation, two representative variables from our set of indicators that are often discussed when assessing the impact of monetary policy. Interestingly, we find that thc indicators are all contained within the set the observable variables that proxy our factors. Our findings, thus, support the notion that a judiciously chosen set of macroeconomic indicators can effectively provide the same macroeconomic policyrelevant information as that contained in a largescale time series dataset. Of course, the largescale datasets are still required in order to select the key indicator variables or confirm one’s prior choice of key variables. Our findings also suggest that certain yield “spreads ” are also useful indicators.