Hello,
I try to estimate a growth model with Markov-switching models with AR(1) with both expansion and recession states. That is two states, expansion and recession.
Mainly the impact of a number of indicators(index1 index2 form1 form2) on the growth rate(gdp_g), for expansion and recession.
The theoretical background is a state space model with a latent unit root; (AR(1)).
All variables in the model are endogenous with a single lag. I saw the help file, but I didn't understand how it is done in state space form, when other endogenous parameters affect it...
One step ahead, prediction should be added in addition to address expectations.
Data sample is below.
Thanks for any help!
Mario
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I try to estimate a growth model with Markov-switching models with AR(1) with both expansion and recession states. That is two states, expansion and recession.
Mainly the impact of a number of indicators(index1 index2 form1 form2) on the growth rate(gdp_g), for expansion and recession.
The theoretical background is a state space model with a latent unit root; (AR(1)).
All variables in the model are endogenous with a single lag. I saw the help file, but I didn't understand how it is done in state space form, when other endogenous parameters affect it...
One step ahead, prediction should be added in addition to address expectations.
Data sample is below.
Thanks for any help!
Mario
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Code:
* Example generated by -dataex-. For more info, type help dataex clear input float(year gdp_g index1 index2 form1 form2) 1980 1.2640425 -1.8494064 -.4919946 .1752619 .8247381 1981 1.2 -1.4651397 -.5348802 .2135585 .7864415 1982 1.9678724 -1.1484452 -.7147316 .3413058 .6586943 1983 1.5117022 1.6252997 1.8567197 .26389787 .7361021 1984 2.570638 2.4657974 2.1658478 .18436745 .8156326 1985 .9210638 3.1889434 2.9895875 .1516645 .8483355 1986 2.248085 5.396011 4.5956273 .19957983 .8004202 1987 1.0723404 3.104351 3.289934 .23287007 .76713 1988 2.2489362 1.2033308 .53224826 .22385173 .7761483 1989 2.1693618 .6751824 -.16877118 .23149644 .7685035 1990 .25382978 .05228972 -.3444368 .27984852 .7201515 1991 -.2125532 .935702 .3012117 .3284199 .6465201 1992 .317234 4.1132245 3.1708634 .3083437 .691594 1993 1.3534043 5.108476 2.5406225 .24354993 .7332776 1994 2.612128 3.425388 1.768389 .23602992 .7400631 1995 3.358936 3.972118 2.3609061 .27747253 .7225274 1996 2.3597872 6.033151 4.661421 .325723 .674277 1997 3.0414894 3.793532 3.347975 .30558395 .6757603 1998 1.793617 3.7508705 3.940954 .29313186 .6818681 1999 1.8717022 2.980336 3.343685 .28042582 .7138736 2000 3.385532 2.734389 1.775652 .2688356 .7311644 2001 2.0512767 .7556655 1.75466 .2554258 .7445742 2002 2.913404 1.386648 2.0334747 .2105769 .789423 2003 3.017021 1.5340753 1.0089021 .1955357 .8044643 2004 3.321915 1.81795 .5531137 .2408904 .7591096 2005 2.6668086 -.29726213 -.7515011 .2480769 .7519231 2006 2.732766 -2.838431 -2.9404194 .2673077 .7326923 2007 2.8740425 -1.5961655 -1.8205668 .26824456 .7317554 2008 .04914893 -1.881141 -3.931049 .2303533 .7696467 2009 -.9074468 -.8101007 -1.919522 .23055235 .7694477 2010 2.887234 -2.2245317 -1.8109186 .29588655 .7041135 2011 1.4131914 -1.431529 -1.4509507 .3009355 .6958717 2012 .9474468 -2.6042926 -3.2060516 .25005552 .7229174 2013 1.3274468 -.8640846 -2.6833265 .20782596 .7834868 2014 .8512766 -.6784925 -2.421195 .22527473 .7747253 2015 2.0059574 . . . . 2016 .59042555 . . . . 2017 1.825532 . . . . 2018 1.508298 . . . . 2019 .9170213 . . . . 2020 1.8051064 . . . . end