r - Prediction of value with Bayesian Linear Regression using rstanarm -

i have excel file shown below , converted r data frame shown in r data frame: wanted predict value year-2001 , lag-168 (coloured in red) using values in row of year 2001. know value of year-2001 , lag -168 in there research wanted check accuracy of model. need use bayesian glm "rstanarm" package. can please me solve issue. great how predict if there no predictors in solving bayesian glm's.

r dataframe:

year    lag amount  yearno  2001    12  187572  1 2001    24  331364  1 2001    36  354886  1 2001    48  361970  1 2001    60  365251  1 2001    72  366729  1 2001    84  368047  1 2001    96  368775  1 2001    108 369457  1 2001    120 369709  1 2001    132 370048  1 2001    144 370109  1 2001    156 370152  1 2001    168 370262  1 2002    12  193797  2 2002    24  340855  2 2002    36  363227  2 2002    48  369038  2 2002    60  373025  2 2002    72  379143  2 2002    84  381168  2 2002    96  382779  2 2002    108 383456  2 2002    120 383633  2 2002    132 383782  2 2002    144 383858  2 2002    156 383946  2 2003    12  199427  3 2003    24  341038  3 2003    36  361413  3 2003    48  369054  3 2003    60  377653  3 2003    72  381431  3 2003    84  383027  3 2003    96  383479  3 2003    108 383947  3 2003    120 384156  3 2003    132 384286  3 2003    144 384596  3 2004    12  203652  4 2004    24  352605  4 2004    36  379737  4 2004    48  392936  4 2004    60  398892  4 2004    72  404386  4 2004    84  406845  4 2004    96  407229  4 2004    108 407574  4 2004    120 407831  4 2004    132 408103  4 2005    12  232439  5 2005    24  411692  5 2005    36  446767  5 2005    48  462847  5 2005    60  468766  5 2005    72  477511  5 2005    84  478681  5 2005    96  479705  5 2005    108 479862  5 2005    120 479978  5 2006    12  269034  6 2006    24  476014  6 2006    36  514783  6 2006    48  526703  6 2006    60  531778  6 2006    72  534083  6 2006    84  535445  6 2006    96  536868  6 2006    108 537362  6 2007    12  276283  7 2007    24  486645  7 2007    36  531464  7 2007    48  544502  7 2007    60  550863  7 2007    72  554442  7 2007    84  555901  7 2007    96  556313  7 2008    12  359850  8 2008    24  639936  8 2008    36  692785  8 2008    48  707921  8 2008    60  713411  8 2008    72  716725  8 2008    84  718021  8 2009    12  353329  9 2009    24  633537  9 2009    36  676511  9 2009    48  688039  9 2009    60  693073  9 2009    72  696304  9 2010    12  375282  10 2010    24  650360  10 2010    36  689786  10 2010    48  700194  10 2010    60  705370  10 2011    12  398196  11 2011    24  659351  11 2011    36  696741  11 2011    48  707723  11 2012    12  388397  12 2012    24  643874  12 2012    36  683007  12 2013    12  396966  13 2013    24  651386  13 2014    12  386238  14