|
Inflation, R&D and Growth in an Open Economy
|
■Author:
Angus C. Chu
Guido Cozzi
Ching-Chong Lai
Chih-Hsing Liao
|
■Published In:
Journal of International Economics
Volume 96, Issue 2, July 2015, Pages 360–374
|
|
■Keywords:
Inflation; Economic growth; R&D; Trade in intermediate goods
|
本院經濟系賴景昌老師於Journal of International Economics期刊發表論文
■Abstract:
This study explores the long-run effects of inflation in a two-country Schumpeterian growth model with cash-in-advance constraints on consumption and R&D investment. We find that increasing domestic inflation reduces domestic R&D investment and the growth rate of domestic technology. Given that economic growth in a country depends on both domestic and foreign technologies, increasing foreign inflation also affects the domestic economy. When each government conducts its monetary policy unilaterally to maximize the welfare of domestic households, the Nash-equilibrium inflation rates are generally higher than the optimal inflation rates chosen by cooperative governments who maximize the welfare of both domestic and foreign households. Under the CIA constraint on R&D (consumption), a larger market power of firms amplifies (mitigates) this inflationary bias. We use cross-country panel data to estimate the effects of inflation on R&D and also calibrate the two-country model to data in the Euro Area and the US to quantify the welfare effects of decreasing the inflation rates from the Nash equilibrium to the optimal level.
Toward optimal model averaging in regression models with time series errors
|
■Author:
Tzu-Chang F. Cheng
Ching-Kang Ing
Shu-Hui Yu
|
■Published In:
Journal of Econometrics
Volume 189, Issue 2, December 2015, Pages 321–334
|
|
■Keywords:
Asymptotic efficiency; Autocovariance-corrected Mallows model averaging; Banded Cholesky factorization; Feasible generalized least squares estimator; High-dimensional covariance matrix; Time series errors
|
本院經濟系銀慶剛老師於Journal of Econometrics期刊發表論文
■Abstract:
Consider a regression model with infinitely many parameters and time series errors. We are interested in choosing weights for averaging across generalized least squares (GLS) estimators obtained from a set of approximating models. However, GLS estimators, depending on the unknown inverse covariance matrix of the errors, are usually infeasible. We therefore construct feasible generalized least squares (FGLS) estimators using a consistent estimator of the unknown inverse matrix. Based on this inverse covariance matrix estimator and FGLS estimators, we develop a feasible autocovariance-corrected Mallows model averaging criterion to select weights, thereby providing an FGLS model averaging estimator of the true regression function. We show that the generalized squared error loss of our averaging estimator is asymptotically equivalent to the minimum one among those of GLS model averaging estimators with the weight vectors belonging to a continuous set, which includes the discrete weight set used in Hansen (2007) as its proper subset.
▲TOP
|
.png)
本學程所指之東亞，係指亞洲東部各國，包括東北亞的日本、韓國、中國大陸、臺灣、香港、澳門等，以及東南亞的中南半島諸國、菲律賓、汶萊、馬來西亞、印尼和新加坡等國。
.jpg "黃旻華")
