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Template-Type: ReDIF-Paper 1.0
Title: Exchange-Rate Dynamics and Optimal Asset Accumulation Revisited
Author-Name: Maurice Obstfeld
Author-Person: pob13
Note: ITI IFM
Number: 0064
Creation-Date: 1988-02
Order-URL: http://www.nber.org/papers/t0064
File-URL: http://www.nber.org/papers/t0064.pdf
File-Format: application/pdf
Abstract: It has recently been observed that when equations of motion for state variables are nonautonomous, optimal control problems involving Uzawa's endogenous rate of time preference cannot be solved using the change-of-variables method common in the literature. Instead, the problem must be solved by explicitly adding an additional state variable that measures the motion of time preference over time. This note reassesses earlier work of my own on exchange rate dynamics, which was based on a change-of- variables solution procedure. When the correct two-state-variable solution procedure is used, the model's qualitative predictions are unchanged. In addition, the analysis yields an intuitive interpretation of the extra co-state variable that arises in solving the individual's maximization problem.
Handle: RePEc:nbr:nberte:0064
Template-Type: ReDIF-Paper 1.0
Title: Asset Pricing with a Factor Arch Covariance Structure: Empirical Estimates for Treasury Bills
Author-Name: Robert F. Engle
Author-Name: Victor Ng
Author-Name: Michael Rothschild
Author-Person: pro48
Note: ME
Number: 0065
Creation-Date: 1988-11
Order-URL: http://www.nber.org/papers/t0065
File-URL: http://www.nber.org/papers/t0065.pdf
File-Format: application/pdf
Publication-Status: published as Journal of Econometrics, Vol. 45, No. 1/2, pp. 213-237, (July/August 1990).
Abstract: Asset pricing relations are developed for a vector of assets with a time varying covariance structure. Assuming that the eigenvectors are constant but the eigenvalues changing, both the Capital Asset Pricing Model and the Arbitrage Pricing Theory suggest the same testable implication: the time varying part of risk premia are proportional to the time varying eigenvalues. Specifying the eigenvalues as general ARCH processes. the model is a multivariate Factor ARCH model. Univariate portfolios corresponding to the eigenvectors will have (time varying) risk premia proportional to their own (time varying) variance and can be estimated using the GARCH-M model. This structure is applied to monthly treasury bills from two to twelve months maturity and the value weighted NYSE returns index. The bills appear to have a single factor in the variance process and this factor is influenced or "caused in variance" by the stock returns.
Handle: RePEc:nbr:nberte:0065
Template-Type: ReDIF-Paper 1.0
Title: The Size and Power of the Variance Ratio Test in Finite Samples: A Monte Carlo Investigation
Author-Name: Andrew W. Lo
Author-Person: plo171
Author-Name: A. Craig MacKinlay
Note: ME
Number: 0066
Creation-Date: 1988-06
Order-URL: http://www.nber.org/papers/t0066
File-URL: http://www.nber.org/papers/t0066.pdf
File-Format: application/pdf
Publication-Status: published as Journal of Econometrics, vol. 40, 1989, pp. 203-238
Abstract: We examine the finite sample properties of the variance ratio test of the random walk hypothesis via Monte Carlo simulations under two null and three alternative hypotheses. These results are compared to the performance of the Dickey-Fuller t and the Box-Pierce Q statistics. Under the null hypothesis of a random walk with independent and identically distributed Gaussian increments, the empirical size of all three tests are comparable. Under a heteroscedastic random walk null, the variance ratio test is more reliable than either the Dickey-Fuller or Box-Pierce tests. We compute the power of these three tests against three alternatives of recent empirical interest: a stationary AR(1), the sum of this AR(1) and a random walk, and an integrated AR( 1). By choosing the sampling frequency appropriately, the variance ratio test is shown to be as powerful as the Dickey-Fuller and Box-Pierce tests against the stationary alternative, and is more powerful than either of the two tests against the two unit-root alternatives.
Handle: RePEc:nbr:nberte:0066
Template-Type: ReDIF-Paper 1.0
Title: The Dividend Ratio Model and Small Sample Bias: A Monte Carlo Study
Author-Name: John Y. Campbell
Author-Person: pca54
Author-Name: Robert J. Shiller
Author-Person: psh69
Note: ME EFG
Number: 0067
Creation-Date: 1988-07
Order-URL: http://www.nber.org/papers/t0067
File-URL: http://www.nber.org/papers/t0067.pdf
File-Format: application/pdf
Publication-Status: published as Economics Letters, vol.29, no.4, pp.325-331, 1989
Abstract: Small sample properties of parameter estimates and test statistics in the vector autoregressive dividend ratio model (Campbell and Shiller [1988 a,b]) are derived by stochastic simulation. The data generating processes are co integrated vector autoregressive models, estimated subject to restrictions implied by the dividend ratio model, or altered to show a unit root.
Handle: RePEc:nbr:nberte:0067
Template-Type: ReDIF-Paper 1.0
Title: Some Further Results on the Exact Small Sample Properties of the Instrumental Variable Estimator
Author-Name: Charles R. Nelson
Author-Person: pne247
Author-Name: Richard Startz
Note: ME
Number: 0068
Creation-Date: 1988-09
Order-URL: http://www.nber.org/papers/t0068
File-URL: http://www.nber.org/papers/t0068.pdf
File-Format: application/pdf
Publication-Status: published as Journal of Business, January 1990.
Abstract: New results on the exact small sample distribution of the instrumental variable estimator are presented by studying an important special case. The exact closed forms for the probability density and cumulative distribution functions are given. There are a number of surprising findings. The small sample distribution is bimodal. with a point of zero probability mass. As the asymptotic variance grows large, the true distribution becomes concentrated around this point of zero mass. The central tendency of the estimator may be closer to the biased least squares estimator than it is to the true parameter value. The first and second moments of the IV estimator are both infinite. In the case in which least squares is biased upwards, and most of the mass of the IV estimator lies to the right of the true parameter, the mean of the IV estimator is infinitely negative. The difference between the true distribution and the normal asymptotic approximation depends on the ratio of the asymptotic variance to a parameter related to the correlation between the regressor and the regression, error. In particular, when the instrument is poorly correlated with the regressor, the asymptotic approximation to the distribution of the instrumental variable estimator will not be very accurate.
Handle: RePEc:nbr:nberte:0068
Template-Type: ReDIF-Paper 1.0
Title: The Distribution of the Instrumental Variables Estimator and Its t-RatioWhen the Instrument is a Poor One
Author-Name: Charles R. Nelson
Author-Person: pne247
Author-Name: Richard Startz
Note: ME
Number: 0069
Creation-Date: 1988-09
Order-URL: http://www.nber.org/papers/t0069
File-URL: http://www.nber.org/papers/t0069.pdf
File-Format: application/pdf
Publication-Status: published as Econometrica, April 1990.
Abstract: When the instrumental variable is a poor one, in the sense of being weakly correlated with the variable it proxies, the small sample distribution of the IV estimator is concentrated around a value that is inversely related to the feedback in the system and which is often further from the true value than is the plim of OLS. The sample variance of residuals similarly becomes concentrated around a value which reflects feedback and not the variance of the disturbance. The distribution of the t-ratio reflects both of these effects, stronger feedback producing larger t-ratios. Thus, in situations where OLS is badly biased, a poor instrument will lead to spurious inferences under IV estimation with high probability, and generally perform worse than OLS.
Handle: RePEc:nbr:nberte:0069
Template-Type: ReDIF-Paper 1.0
Title: The Time-Varying-Parameter Model as an Alternative to ARCH for Modeling Changing Conditional Variance: The Case of Lucas Hypothesis
Author-Name: Charles R. Nelson
Author-Person: pne247
Author-Name: Chang-Jin Kim
Note: ME
Number: 0070
Creation-Date: 1988-09
Order-URL: http://www.nber.org/papers/t0070
File-URL: http://www.nber.org/papers/t0070.pdf
File-Format: application/pdf
Publication-Status: published as Journal of Business and Economic Statistics, vol. 7, no. 4, October 1989, pp. 433-440.
Abstract: The main econometric issue in testing the Lucas hypothesis (1973) in a times series context is the estimation of the variance conditional on past information. The ARCH model, proposed by Engle (1982), is one way of specifying the conditional variance. But the assumption underlying the ARCH specification is ad-hoc. The existence of ARCH can sometimes be interpreted as evidence of misspecification. Under the assumption that a monetary policy regime is continuously changing, a time-varying-parameter (TVP) model is proposed for the monetary growth function. Based on Kalman filtering estimation of recursive forcast errors and their conditional variances, the Lucas hypothesis is tested for the U.S. economy (1964.1 - 1985.4) using monetary growth as an aggregate demand variable. The Lucas hypothesis is rejected in favor of Friedman's (1977) hypothesis: the conditional variance of monetary growth affects real output directly, not through the coefficients on the forcast error term in the Lucas-type output equation.
Handle: RePEc:nbr:nberte:0070
Template-Type: ReDIF-Paper 1.0
Title: Smart Money, Noise Trading and Stock Price Behavior
Author-Name: John Y. Campbell
Author-Person: pca54
Author-Name: Albert S. Kyle
Author-Person: pky6
Note: ME
Number: 0071
Creation-Date: 1988-10
Order-URL: http://www.nber.org/papers/t0071
File-URL: http://www.nber.org/papers/t0071.pdf
File-Format: application/pdf
Publication-Status: published as Review of Economic Studies, Vol 60, issue 202, January 1993, p. 1-34
Abstract: This paper derives and estimates an equilibrium model of stock price behavior in which exogenous "noise traders" interact with risk-averse "smart money" investors. The model assumes that changes in exponentially detrended dividends and prices are normally distributed, and that smart money investors have constant absolute risk aversion. In equilibrium, the stock price is the present value of expected dividends, discounted at the riskless interest rate, less a constant risk premium, plus a term which is due to noise trading. The model expresses both stock prices and dividends as sums of unobserved components in continuous time. The model is able to explain the volatility and predictability of U.S. stock returns in the period 1871-1986 in either of two ways. Either the discount rate is 4% or below, and the constant risk premium is large; or the discount rate is 5% or above, and noise trading, correlated with fundamentals, increases the volatility of stock prices. The data are not well able to distinguish between these explanations.
Handle: RePEc:nbr:nberte:0071
Template-Type: ReDIF-Paper 1.0
Title: The R&D Master File Documentation
Author-Name: Bronwyn H. Hall
Author-Name: Clint Cumminq
Author-Name: Elizabeth S. Laderman
Author-Name: Joy Mundy
Note: PR
Number: 0072
Creation-Date: 1988-12
Order-URL: http://www.nber.org/papers/t0072
File-URL: http://www.nber.org/papers/t0072.pdf
File-Format: application/pdf
Abstract: This document describes the panel of publicly traded United States manufacturing firms which was created and updated at the National Bureau of Economic Research from 1978 through 1988 within the Productivity Program. The panel consists of about 2600 large manufacturing firms with three to twenty-seven years of data each; the period covered by the sampling frame was 1976 through 1985, with data back to 1959 where possible. There are approximately 70 variables for each firm-year of data, consisting of income statement and balance sheet variables and the corresponding common stock data. The technological data available for these firms consist of R&D expenditures and patents granted, both by date of application and by granting date. The patents data are available only through about 1981, due to the limitations of our sources and budget. The firms on the file are identified both by their CUSIP number and by name, making it feasible to match this data to other sources.
Handle: RePEc:nbr:nberte:0072
Template-Type: ReDIF-Paper 1.0
Title: Tests For Unit Roots: A Monte Carlo Investigation
Author-Name: G. William Schwert
Author-Person: psc116
Note: ME
Number: 0073
Creation-Date: 1988-12
Order-URL: http://www.nber.org/papers/t0073
File-URL: http://www.nber.org/papers/t0073.pdf
File-Format: application/pdf
Publication-Status: published as Journal of Business and Economic Statisticsvo. 7, no.2 pp147-159. April 1989.
Abstract: Recent work by Said and Dickey (1984 ,1985) , Phillips (1987), and Phillips and Perron(1988) examines tests for unit roots in the autoregressive part of mixed autoregressive-integrated-moving average (ARIHA) models (tests for stationarity). Monte Carlo experiments show that these unit root tests have different finite sample distributions than the unit root tests developed by Fuller(1976) and Dickey and Fuller (1979, l981) for autoregressive processes. In particular, the tests developed by Philllps (1987) and Phillips and Perron (1988) seem more sensitive to model misspeciflcation than the high order autoregressive approximation suggested by Said and Diekey(1984).
Handle: RePEc:nbr:nberte:0073